Development of novel microsatellites for population genetic analysis of Phenacoccus solenopsis Tinsley (Hemipeta: Pseudoccoccidae) based on genomic analysis

International Journal of Biological Macromolecules 121 (2019) 1135–1144

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International Journal of Biological Macromolecules journal homepage: http://www.elsevier.com/locate/ijbiomac

Development of novel microsatellites for population genetic analysis of Phenacoccus solenopsis Tinsley (Hemipeta: Pseudoccoccidae) based on genomic analysis Ling Ma a,b,c, Li-Jun Cao c, Ya-Jun Gong c, Ary A. Hoffmann d, Ai-Ping Zeng a,⁎, Shu-Jun Wei c,⁎, Zhong-Shi Zhou b,⁎ a

Institute of Insect Science, Hunan Agriculture University, Changsha 410128, China Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, China c Institute of Plant and Environmental Protection, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China d School of BioSciences, Bio21 Institute, The University of Melbourne, Victoria 3010, Australia b

a r t i c l e

i n f o

Article history: Received 27 August 2018 Received in revised form 13 September 2018 Accepted 18 October 2018 Available online 21 October 2018 Keywords: Phenacoccus solenopsis Genome Microsatellites

a b s t r a c t The cotton mealybug, Phenacoccus solenopsis Tinsley (Hemipeta: Pseudoccoccidae), is an aggressively invasive pest causing huge economic losses of crops around the world. In this study, we developed genome-wide microsatellites for population genetic analysis of P. solenopsis. We obtained a random genome of P. solenopsis with a size of 267.07 Mb and scaffold N50 of 14.12 Kb. In total 115,639 microsatellites were isolated from the genome, of which those with trinucleotide motifs were the most abundant. Forty-two polymorphic loci were selected for primer validation based on three populations. Allele numbers varied from 2 to 5 with an average value of 2.5 per locus, and allelic richness ranged from 1.00 to 4.48. The observed heterozygosity (H0) and expected heterozygosity (HE) ranged from 0.00 to 0.92 and 0.00 to 0.73, respectively. Population genetic structure analysis based on the developed markers revealed strong differentiation between three populations of P. solenopsis collected from its invasive range in China. The microsatellites developed in our study should provide efficient genetic markers for population level studies of P. solenopsis to reveal invasion history and patterns of dispersal. © 2018 Published by Elsevier B.V.

1. Introduction The cotton mealybug, Phenacoccus solenopsis Tinsley (Hemipeta: Pseudococcidae), is a serious emerging invasive pest due to its harmful effects on agricultural and ornamental plants, attacking approximately 60 families in N24 countries and especially cotton [1–5]. Originating from North America, this species is now widely distributed around the world [6]. In China, it was first reported in Guangdong province in 2008 [7,8], and now has rapidly spread to several provinces in southern China, causing substantial economic losses [5,9–11]. Population genetic analyses can help to identify the capacity of pests to expand their range and track the history of invasion [12,13] to help manage invasive species. An informative set of genetic markers is essential for such studies. In P. solenopsis, genetic markers involving mitochondrial DNA and 28S rDNA have been used in population-level studies to suggest that P. solenopsis might persist as two genetic lineages [14–17], however, any hybridization between such lineages and their patterns of invasion around the world remain unknown. ⁎ Corresponding authors. E-mail addresses: [email protected] (A.-P. Zeng), [email protected] (S.-J. Wei) , [email protected] (Z.-S. Zhou).

https://doi.org/10.1016/j.ijbiomac.2018.10.143 0141-8130/© 2018 Published by Elsevier B.V.

A set of neutral markers, such as microsatellites or simple sequence repeats (SSRs), can provide additional insights into the population history of P. solenopsis [12,18,19]. However, the development of microsatellite markers with traditional approaches can be expensive and timeconsuming [20]. Instead, high-throughput next-generation sequencing systems and bioinformatics tools can now provide more cost-effective approaches to develop polymorphic microsatellite markers [12,21,22]. Based on expressed sequence tags (ESTs), seven ESTs microsatellites have previously been developed for P. solenopsis, but six of these deviated from Hardy-Weinberg equilibrium [23]. In an analysis based on transcriptome sequences, trinucleotide motifs (ATT/AAT) were the most abundant repeats in P. solenopsis, but no microsatellite markers have yet been developed and validated based on these repeats [24]. While both these analyses of P. solenopsis have been based on coding regions, microsatellites are often located on non-coding regions [25], and these could provide better genetic markers that are more likely to be neutral for population genetic analyses. In this study, we developed a set of 42 microsatellites for P. solenopsis by obtaining a random genome and screening it for polymorphic SSRs. Microsatellites were validated for neutrality and lack of departure from Hardy-Weinberg equilibrium in three populations. The microsatellites proved suitable for distinguishing the three populations

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and should provide insights into genetic diversity, genetic structure and invasion history of this pest. 2. Materials and methods 2.1. Insect collection A female adult P. solenopsis from the Hubei Insect Resources Utilization Pest Management, Key laboratory of Huazhong Agricultural University was used for genome sequencing. Other samples used in the study were collected from invaded areas in China between August and September in 2017. Eight individuals were used for initial testing of the primer pairs. Then, 24 female adults from each of three geographical populations were used for a population-level study. One population was from Guangzhou in Guangdong province (GDGZ, 113.159°E, 23.157°N), one was from Ganzhou in Jiangxi province (JXGZ, 114.970°E, 25.825°N) and the third was from Wuxi in Jiangsu province (JSWX, 120.282°E, 31.487°N). All specimens were stored in absolute ethanol and frozen at 4 °C until DNA extraction. Morphological identification of the mealybugs were based on the presence of paired dark spots and stripes on the dorsal region [26] and confirmed by molecular identification using the mitochondrial cox1 gene as described below. Voucher DNA of the mealybugs was stored in the Integrated Pest Management Laboratory of the Beijing Academy of Agriculture and Forestry Sciences at −80 °C (Voucher DNA numbers: D1PS2017HBWH001; D1PS2017GDGZ001 - D1PS2017GDGZ024; D1PS2017JXGZ001 D1PS2017JXGZ024; D1PS2017JSWX001 - D1PS2017JSWX024). 2.2. DNA extraction and assembly of the P. solenopsis random genome Genomic DNA was extracted from a female adult of P. solenopsis using a DNeasy Blood & Tissue Kit (Qiagen, Hilden, Germany). A library with a 500-bp insert size was constructed using the Illumina TruSeq DNA PCR-Free HT Library Prep Kit (Illumina, San Diego, CA, USA). The quality of sequences was evaluated by FastQC v 0.11.5 prior to assembly [27]. The low-quality reads were removed by Trimmomatic 0.36 [28]. We assembled the genome sequences using IDBA_UD using kmers from 20 to 140 [29]. The genome size of P. solenopsis was estimated by JELLYFISH v 2.2.6 software with a K-mer method [30] and the completeness of the assembled genome was estimated by using BUSCO v3 [31]. Molecular identification, microsatellites analysis and primer design. A fragment of the mitochondrial cox1 gene was amplified to confirm the morphological identification of the specimens. Primer pairs of C1-J2183 and CI-N-2568 were used for amplification and sequencing [32]. Polymerase chain reaction (PCR) components were 1.0 μL of template DNA (10–20 ng/μL), 7.5 μL of Master Mix (400 μM dNTP and 3 mM MgCl2) (Promega, Madison, WI, USA), 1.0 μL (10 μM) forward primer, 1.0 μL (10 μM) reserve primer and 4.5 μL of ddH2O. The cox1 gene was amplified under the following conditions: 3 min at 94 °C, followed by 35 cycles at 94 °C for 30 s, 52 °C for 30 s, 60 °C for 1 min, and a postcycle incubation at 60 °C for 10 min. All microsatellites were investigated from the assembled genome by MSDB software (http://msdb.biosv.com/) with a minimum of 12, 7, 5, 5, 5 and 5 repeats to identify the mononucleotide, dinucleotide, trinucleotide, tetranucleotide, pentanucleotide and hexanucletide motifs, respectively, according to Wang [21]. Considering the start position of repeat motif and their reverse complement, we simplified classifying repeats into classes, such as (ATC)n, (TCA)n, (CAT)n, (GAT)n, (TGA)n and (ATG)n, the trinucleotide, belonged to the same class [33]. The average length of microsatellites, as well as the length, total counts, frequency (loci/Mb) and density (loci/bp) of motif were analyzed [34]. QDD program was used to design primers for the loci [35]. The criteria for searching for primers were as follows. (i) Tri-, tetra-, penta- and hexa-nucleotide motifs were considered with at least N6, 6, 5, 5 repeats respectively. (ii) The size of PCR product ranged from 100 to 350 bp, and 20 bp was seen as an optimal primer length. (iii) The

annealing temperature (Tm) needed to be between 58.0 °C and 63.0 °C, and the difference in Tm between pairwise primers had to be lower than 5 °C. Other parameters were set at default. Primers were strictly filtered; only the best locus for each sequence and design strategy of ‘A' was retained (see QDD manual). The minimum distance between the 3′ end of two primer pairs and their target region had to be N10 bp. 2.3. Primer screening and polymorphic detection With cost factors taken into consideration, we added a universal primer tail C (PC tail) (5′CAGGACCAGGCTACCGTG3′) to the 5′ end of the candidate forward primer [36,37]. Eight female adults of the mealybug were tested for initial primer validation. PCR amplification was performed in 10 μL volume reactions using final concentrations of the reagents: 0.5 μL of template DNA (10–20 ng/uL), 5 μL of Master Mix (400 uM dNTP and 3 mM MgCl2) (Promega, Madison, WI, USA), 0.32 μL of universal primer with PC tail, 0.08 μL (10 μM) forward primer, 0.16 μL (10 μM) reserve primer and 3.94 μL of ddH2O. The microsatellites were amplified under the following conditions: 2 min at 94 °C, followed by 35 cycles at 94 °C for 30 s, 56 °C for 45 s, 72 °C for 45 s, a post-cycle incubation at 72 °C for 7 min. Randomly selected PCR products were visualized on agarose gel (1.0%) electrophoresis. All PCR products were analyzed using ABI 3730xl DNA Analyzer (Applied Biosystems, Foster, CA, USA) with the GeneScan 500 LIZ size standard (Applied Biosystems). Primers selected for initial testing followed three criteria. (i) Primers with PCR amplification rate higher than 75% were maintained for genotyping. (ii) Primers with low polymorphism in tested individuals were excluded. (iii) Loci showing more than two peaks in one individual were removed. The remaining primers were validated in 72 individuals from three populations. The amplification mixture, amplification program, and analysis of PCR fragments followed the above steps. 2.4. Genetic diversity analysis The genotyped data of three region were corrected by MICROCHECKER [38] before they were analyzed by GENEMAPPER 4.0 (Applied Biosystems, USA). The number of alleles, observed heterozygosity (HO), expected heterozygosity (HE) and polymorphism information content (PIC) were calculated by the macros Microsatellite Tools [39]. The null allele frequencies were estimated using the software FreeNA [40]. Deviation from Hardy-Weinberg equilibrium (HWE) for each locus/population combination, linkage disequilibrium (LD) among loci within each population, pairwise mean population differentiation (FST) and inbreeding coefficients (FIS) were estimated in GENEPOP v 4.0.11 [41]. We used the software FSTAT v2.9.3 to test allelic richness (AR) of each locus [42] and putative loci under selection were detected with two options: “neutral mean FST” and “force mean FST” by LOSITAN [43]. 2.5. Population genetic structure analysis Population genetic structure was investigated using STRUCTURE v 2.3.4 program [44]. In order to identify a cluster, we used replicates with K from 1 to 3, repeats with 30 times for each, with 200,000 Markov chain Monte Carlo iterations and a burnin of 100,000 iterations. After submitting the results to Structure Harvester Web 0.6.94 to determine the optimal K value by the Deita method (http://taylor0.biology.ucla. edu/structureHarvester/), membership coefficient matrices (Q-matrices) associated with the optimal k were processed using CLUMPP v 1.12 [45], and then visualized with DISTRUCT v 1.1 [46]. We also used a discriminant analysis of principal component (DAPC) to analyze population genetic structure under default settings [47], as a complement to the STRUCTURE analysis.

L. Ma et al. / International Journal of Biological Macromolecules 121 (2019) 1135–1144 Table 1 Frequency distribution of different microsatellites in Phenacoccus solenopsis as identified through MSDB software. Motif

DNRa TNRb TTNRc PNRd HNRe

Repeats (rn) 5 b rn b 10

11 b rn b 15

16 b rn b 20

21 b rn b 25

26 b rn b 30

rn N 30

5077 8529 4705 672 44

680 427 148 34 5

83 43 34 11 5

22 10 11 26 7

16 2 4 21

13 1 2

Frequencies (%)

28.55 43.68 23.77 3.70 0.30

a Dinucleotide repeats; btrinucleotide repeats; ctetranucleotide repeats; dpentanucleotide repeats; and ehexanucleotide repeats.

3. Results 3.1. Genome sequencing and assembly We generated 25.69 Gb paired-end (PE) sequences (171,296,024 reads) with read length of 150 bp from a DNA library by sequencing on an Illumina Hiseq 4000 system. The genome size was estimated to be 261.61 Mb using a K-mer method. After removing low quality reads, the remaining high-quality reads were assembled into 283,033 scaffolds by IDBA_UD [29]. The length of total scaffolds was 267.07 Mb, with longest scaffold of 317.8 Kb, scaffold N50 of 14.12 Kb. By using BUSCO, the estimated completeness of the genome was 89.6% (single-copy and duplicated sequences were 88.0% and 1.6%, respectively), while the missing and fragmented genes were 4.8% and 5.6%, respectively.

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3.3. Development and validation of microsatellite markers In total, 28,469 of the loci discovered were suitable for primer design by QDD program. The number of primers obtained in our study were similar to those obtained in other studies of insects [21,22,48]. Seventy-six pairs of primers were selected from different scaffolds for Polymerase Chain Reaction (PCR) amplification. In our initial test on eight individuals, 42 primer pairs generated polymorphic genotypes, while 27 loci were monomorphic and nine loci failed to amplify in N75% individuals (two loci overlapped). The 42 loci were characterized for three populations with 24 individuals per population. Null allele frequency was low for all populations and loci except in two cases (Table 2). Observed heterozygosity (H0) and expected heterozygosity (HE) ranged from 0.00 to 0.92 and 0.00 to 0.73, respectively (Table 2). Allele numbers varied from 2 to 5 with an average value of 2.5 per locus and allelic richness ranged from 1.00 to 4.48 (Table 2). The polymorphism information content of the loci (PIC) ranged from 0.03 to 0.53 with an average of 0.28 (Table 2). The inbreeding coefficient (FIS) ranged from −0.67 to 0.78, with the PS3-S01 and PS5-S34 loci in some populations having a high FIS, perhaps reflecting a Wahlund effect [49,50] or biased sampling in the Jiangsu population. In the Jiangxi population, three loci showed deviations from Hardy-Weinberg equilibrium (HWE) (P b 0.05). Seven loci showed deviation from HWE in the Guangdong and Jiangsu populations, and two loci (PS4-S27 and PS5-S34) deviated significantly in both populations (Table 2). A higher frequency of null alleles was also found for locus PS5-S34. In addition, 13 pairs of loci showed significant linkage disequilibrium (LD) (P b 0.01) in all populations. However, no pairs of loci were in LD in all populations. In the LOSITAN test, two loci (PS3S23 and PS4-S16) fell into the candidate space for balancing selection, while others fell into space consistent with neutral expectations (Fig. 2).

3.2. Genome-wide characterization of microsatellite A total of 115,639 candidate microsatellites were isolated from the assembly scaffolds by MSDB software (http://msdb.biosv.com/). Ignoring mononucleotide repeats, we found 5891 (28.55%) dinucleotide, 9012 (43.68%) trinucleotide, 4904 (23.77%) tetranucleotide, 764 (3.70%) pentanucleotide and 61 (0.30%) hexanucleotide repeats (Table 1). In decreasing order, the five most frequently repeats were AAT, AT, AAAT, AAC and AG (Fig. 1). The top five motifs represent 70.44% while the most abundant repeat AAT represent 23.37% of all microsatellites (Fig. 1). The average length of the different nucleotide repeats was 17 to 54 bp while the frequencies and densities ranged from 0.18 to 26.7 loci Mb−1 and 9.9 to 522.91 bp Mb−1, respectively.

3.4. Population genetic structure of P. solenopsis Molecular identification using the cox1 gene identified all specimens as P. solenopsis. A single haplotype of mitochondrial cox1 gene was found with 100% similarity to the GenBank record KJ187569. The population structure of P. solenopsis was analyzed based on all 42 microsatellites. STRUCTURE analysis [44] showed the 72 individuals from three populations divided into two clusters (Fig. 3). The GDGZ and JSWX populations together formed one cluster, while the JXGZ population formed the other group. DAPC analysis showed clear separation of the three populations (Fig. 4).

Fig. 1. Frequency distribution of microsatellites among different motif types in Phenacoccus solenopsis. The “others” category represents summed motifs with counts below 200.

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Table 2 Characteristics of 42 microsatellite loci validated in 72 individuals of Phenacoccus solenopsis. Locus

Primer (5′-3′)

PS3-S01 F a: CGAGTT CGACTCCG TGCATT R b: CAGC TGTCTAT CCGTAATT CCG PS4-S02 F: ATGTTC GTGGATTG CTTGCG R: GCTTTC TAAGCGCA TTTGGAG PS3-S11 F: TTTAGA AGGACGGG TCGGG R: GCATCT CCACCGCA CCATAG PS3-S12 F: CGGAAA GTCATACG ATACCGAA R: TCTACA CATAACTC TGCAGGCA PS5-S13 F: CAACAC CTCCCTAC CGAACC R: CGAACT AGAATGTG GATGTGCG PS3-S15 F: GCTACC TATTTCCT GGCCGC R: AGAACT GTCGTCAA GAGGTGA PS4-S16 F: ACATTT GGGTCCTT TCAGCGA R: ACTCGA AGGGATGG TTTGGC PS4-S17 F: CGTGCA ACGAACTC CTACGT R: ACACCT TCACCCGA ACAAAG PS5-S18 F: AATTCG CAGCGCCT AGTCAT R: GTTGCA GGTTCGTT ACAGGC PS5-S20 F: CGCTCC TTTGGAAA CGACTG R: TTCACC GAAGAATG CGCAAG PS3-S21 F: CCTATG CTAGTCGC GTATCCA R: TGCTGG TATGCACA AATACGTG PS5-S22 F: AATTGC AGCACACG CACAAT R: TGCCTC GCATCTCT TAGTGC

Allele ARc

HOd

HWEf

HeE

FISh

Null Allele Frequencies g

PICi

GDGZ JSWX JXGZ GDGZ JSWX JXGZ GDGZ JSWX JXGZ GDGZ JSWX JXGZ GDGZ JSWX JXGZ GDGZ

JSWX

JXGZ

2.00

2.00

2.00

1.85

0.43

0.11

0.13

0.47

0.48

0.12

1.00

0.00

1.00

0.02

0.25

0.00

0.09

0.78

−0.05 0.27

2.00

1.85

1.98

2.00

0.13

0.25

0.71

0.12

0.22

0.51

1.00

1.00

0.10

0.00

0.00

0.00

−0.05 −0.12 −0.40 0.23

5.00

1.46

3.42

3.73

0.04

0.92

0.78

0.04

0.59

0.58

–

0.00

0.16

0.00

0.00

0.00

0.00

3.00

2.78

2.76

3.00

0.43

0.68

0.74

0.39

0.54

0.60

0.47

0.32

0.32

0.00

0.00

0.00

−0.10 −0.28 −0.24 0.43

2.00

2.00

2.00

1.98

0.50

0.46

0.25

0.48

0.36

0.22

1.00

0.29

1.00

0.00

0.00

0.00

−0.05 −0.28 −0.12 0.28

2.00

2.00

2.00

1.46

0.46

0.75

0.04

0.36

0.50

0.04

0.29

0.01

–

0.00

0.00

0.00

−0.28 −0.53 0.00

2.00

2.00

2.00

1.99

0.54

0.33

0.29

0.40

0.42

0.25

0.14

0.34

1.00

0.00

0.00

0.00

−0.35 0.21

−0.15 0.29

5.00

3.80

4.48

2.46

0.61

0.71

0.50

0.54

0.73

0.39

0.36

0.89

0.44

0.00

0.00

0.00

−0.14 0.02

−0.28 0.49

4.00

3.00

3.56

3.63

0.71

0.54

0.63

0.61

0.60

0.49

0.45

0.42

0.63

0.00

0.00

0.00

−0.16 0.09

−0.27 0.49

2.00

1.92

1.46

2.00

0.17

0.04

0.79

0.16

0.04

0.50

1.00

–

0.01

0.00

0.00

0.00

−0.07 0.00

−0.60 0.18

3.00

2.85

1.92

2.00

0.29

0.17

0.75

0.38

0.16

0.51

0.07

1.00

0.04

0.04

0.00

0.00

0.24

−0.07 −0.50 0.28

2.00

1.00

1.00

1.71

0.00

0.00

0.08

0.00

0.00

0.08

–

–

1.00

0.00

0.00

0.00

–

–

−0.58 −0.36 0.35

0.23

−0.02 0.03

L. Ma et al. / International Journal of Biological Macromolecules 121 (2019) 1135–1144

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Table 2 (continued) Locus

Primer (5′-3′)

PS3-S23 F: TCTGGG CTAGAGGC GAATTT R: GGCTTT GTATGGGA GCTGGG PS4-S24 F: TCTCCC GTCGTTGA ACATGG R: CGGAAA CTGCGCGT AATAAGG PS5-S26 F: CCTCTG CCTGCTCA GTTCAT R: CGGTTT CAGTTTAG GAGCGG PS4-S27 F: TCTCAC GCTTATGA CGTACTCG R: GCGCCA GACTACAC CTAACC PS5-S28 F: AGCACA ATCATCTT GACAGCTG R: CGACGT GTACGCAA AGTTGA PS3-S32 F: TGACGA GAACTGAA CGAATGGT R: GGTAAC GGCAGCCA ACAATT PS3-S33 F: AGAGGA TAGCCGAA TACGCG R: GCCAAA GTTCGACT CGTATACA PS5-S34 F: GAAGTG CCAGAGGA GCCATT R: ATCATA TCATCGCC GCCGTG PS3-S35 F: TATCGC TTAACGGG TCCGC R: AGTGCC TGCATGTC TAGTGT PS3-S43 F: ATGCAT TTAGTGGT AACTGGCA R: TATCTT GAGTGAGC CACGCC PS6-S44 F: CGGCAA AGGCAGAA CAACTC R: GCGCCG GATCATTC AGGTAA PS4-S45 F: TGCGGC TGAATAAA CTGACAAC R: GGGCAT TCGCGTTA TACTCG

Allele ARc

HOd

HeE

HWEf

FISh

Null Allele Frequencies g

GDGZ JSWX JXGZ GDGZ JSWX JXGZ GDGZ JSWX JXGZ GDGZ JSWX JXGZ GDGZ JSWX JXGZ GDGZ

JSWX

JXGZ

PICi

3.00

2.00

2.94

2.00

0.29

0.38

0.33

0.31

0.39

0.34

1.00

0.53

1.00

0.01

0.02

0.00

0.06

0.03

0.01

0.29

2.00

2.00

1.85

2.00

0.58

0.13

0.38

0.42

0.12

0.36

0.13

1.00

1.00

0.00

0.00

0.00

−0.39 −0.05 −0.04 0.24

2.00

2.00

1.85

2.00

0.46

0.13

0.38

0.40

0.12

0.31

0.63

1.00

0.55

0.00

0.00

0.00

−0.14 −0.05 −0.21 0.23

4.00

3.00

2.00

2.71

0.75

0.71

0.54

0.61

0.49

0.46

0.00

0.04

0.81

0.00

0.00

0.00

−0.24 −0.46 −0.18 0.42

4.00

3.50

2.87

3.58

0.86

0.65

0.74

0.69

0.52

0.62

0.00

0.13

0.72

0.00

0.00

0.00

−0.26 −0.27 −0.19 0.53

2.00

2.00

2.00

1.00

0.42

0.75

0.00

0.34

0.50

0.00

0.54

0.01

–

0.00

0.00

0.01

−0.24 −0.53 –

2.00

1.71

1.92

1.99

0.08

0.17

0.29

0.08

0.16

0.25

1.00

1.00

1.00

0.00

0.00

0.00

−0.02 −0.07 −0.15 0.15

2.00

2.00

2.00

1.96

0.17

0.17

0.13

0.38

0.51

0.19

0.01

0.00

0.21

0.17

0.22

0.08

0.57

0.35

0.28

2.00

2.00

2.00

1.46

0.42

0.79

0.04

0.34

0.50

0.04

0.54

0.01

–

0.00

0.00

0.00

−0.24 −0.60 0.00

0.23

2.00

2.00

2.00

2.00

0.83

0.63

0.71

0.51

0.47

0.50

0.00

0.17

0.09

0.00

0.00

0.00

−0.67 −0.35 −0.42 0.37

2.00

1.96

1.46

2.00

0.21

0.04

0.83

0.19

0.04

0.51

1.00

–

0.00

0.00

0.00

0.00

−0.10 0.00

2.00

2.85

2.77

2.99

0.29

0.29

0.67

0.38

0.27

0.59

0.07

1.00

0.85

0.04

0.00

0.00

0.24

0.68

0.21

−0.67 0.19

−0.10 −0.13 0.36

(continued on next page)

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Table 2 (continued) Locus

Primer (5′-3′)

Allele ARc

HOd

HeE

HWEf

FISh

Null Allele Frequencies g

GDGZ JSWX JXGZ GDGZ JSWX JXGZ GDGZ JSWX JXGZ GDGZ JSWX JXGZ GDGZ JSWX JXGZ GDGZ PS5-S46 F: GGGCAC GATCTAAA GAGTCGA R: CGCACA CGTTACTA GGCAGA PS4-S47 F: GGTACC TAGTCGTT GCGCTA R: TGTACT TTAAGGTG CACTTGCG PS5-S53 F: AGTTTA AAGCGCAC GAGTGAC R: GGGAAA TCGACAGC CGCT PS5-S56 F: GCGCGT GTAATTCT AGCGAT R: ACCTTC AGCAGTTG ACAATCTT PS3-S60 F: TCGCGT GTTATTAT GCCGGA R: TCCAGC TTTACGTA TCGCCT PS5-S61 F: TGCGCT GCTACTTA TGCAGA R: TCGTCG TGGTCGTG TCTTAC PS3-S64 F: AAAGTA GTCCGCCC GTCAAG R: CGCAGT CCGAACAC AATTGC PS4-S65 F: CGGTAA CCTGTGAT CTTGTGG R: CCATCG ATGCGGAG ACCAAT PS3-S66 F: CGTAAG CAGTGATA GCGCAC R: TGCCAT CCCAAGCA GAAGTT PS5-S68 F: CCAACA TCTATACC CGCGTATT R: ACAGCC ATACTTGC GGTATACA PS3-S69 F: GGATAA GGCGACGT GCTCTT R: ATCGCG TATTCGGA AAGCGA PS3-S70 F: ATGACT ACTGGCCA CTGTGC R: CTCTGG TAGGGACT TACTACGT

PICi

JSWX

JXGZ

–

−0.02 0.03

2.00

1.00

1.00

1.71

0.00

0.00

0.08

0.00

0.00

0.08

–

–

1.00

0.00

0.00

0.00

–

2.00

1.91

1.61

2.00

0.13

0.06

0.58

0.12

0.06

0.42

1.00

–

0.13

0.00

0.00

0.00

−0.04 0.00

2.00

2.00

1.71

1.96

0.58

0.08

0.21

0.42

0.08

0.19

0.13

1.00

1.00

0.00

0.00

0.00

−0.39 −0.02 −0.10 0.19

2.00

2.00

1.85

2.00

0.58

0.13

0.42

0.42

0.12

0.34

0.13

1.00

0.54

0.00

0.00

0.00

−0.39 −0.05 −0.24 0.24

2.00

2.00

2.00

1.46

0.79

0.54

0.04

0.49

0.44

0.04

0.00

0.36

–

0.00

0.00

0.00

−0.64 −0.24 0.00

0.25

3.00

2.98

1.46

2.00

0.75

0.04

0.25

0.61

0.04

0.45

0.01

–

0.06

0.00

0.00

0.14

−0.23 0.00

0.46

0.30

2.00

2.00

2.00

1.96

0.54

0.33

0.13

0.50

0.34

0.19

1.00

1.00

0.20

0.00

0.00

0.08

−0.08 0.01

0.35

0.27

2.00

2.00

2.00

2.00

0.50

0.46

0.42

0.42

0.40

0.38

0.62

0.64

1.00

0.00

0.00

0.00

−0.19 −0.14 −0.09 0.32

2.00

2.00

1.46

1.85

0.58

0.04

0.13

0.42

0.04

0.12

0.13

–

1.00

0.00

0.00

0.00

−0.39 0.00

3.00

2.00

2.83

2.00

0.36

0.68

0.32

0.31

0.56

0.27

1.00

0.57

1.00

0.00

0.00

0.00

−0.18 −0.24 −0.16 0.31

2.00

1.00

2.00

2.00

0.00

0.46

0.42

0.00

0.36

0.34

–

0.29

0.54

0.00

0.00

0.00

–

3.00

2.46

2.45

1.96

0.63

0.38

0.13

0.53

0.32

0.19

0.31

1.00

0.20

0.00

0.00

0.08

−0.19 −0.18 0.35

−0.39 0.16

−0.05 0.16

−0.28 −0.24 0.19

0.28

L. Ma et al. / International Journal of Biological Macromolecules 121 (2019) 1135–1144

1141

Table 2 (continued) Locus

Primer (5′-3′)

Allele ARc

HOd

HeE

HWEf

FISh

Null Allele Frequencies g

JXGZ

PICi

2.00

2.00

2.00

2.00

0.54

0.38

0.29

0.49

0.44

0.31

0.68

0.64

1.00

0.00

0.04

0.01

−0.11 0.15

0.06

0.32

3.00

2.92

2.00

2.96

0.67

0.29

0.50

0.56

0.36

0.60

0.24

0.56

0.08

0.00

0.06

0.07

−0.19 0.20

0.17

0.42

3.00

3.00

2.46

3.00

0.54

0.42

0.63

0.65

0.35

0.65

0.01

0.63

0.30

0.05

0.00

0.02

0.17

−0.21 0.04

0.47

3.00

2.98

2.71

3.00

0.71

0.42

0.63

0.60

0.35

0.64

0.16

1.00

0.32

0.00

0.00

0.01

−0.19 −0.19 0.02

0.45

2.00

2.00

1.98

2.00

0.21

0.25

0.25

0.36

0.22

0.28

0.07

1.00

0.51

0.12

0.00

0.03

0.43

−0.12 0.12

0.24

2.00

2.00

2.00

2.00

0.33

0.71

0.38

0.28

0.51

0.36

1.00

0.10

1.00

0.00

0.00

0.00

−0.18 −0.40 −0.04 0.30

GDGZ JSWX JXGZ GDGZ JSWX JXGZ GDGZ JSWX JXGZ GDGZ JSWX JXGZ GDGZ JSWX JXGZ GDGZ PS3-S71 F: TGAGGT GATAAGAA GCTCTTGC R: AAGGGT CGGGATAG TTCGTT PS3-S72 F: CCGCTG GTAGAGGT TGTGTT R: GAGCAA AGTTACGC GCACTG PS3-S73 F: GTCGAT CGAGGATG ACAGGC R: GTTGCG AGCAGTAC AAGCAC PS3-S74 F: GGCGTG TAAACCGT GTAACG R: GGTAGT ACTAGCGC TGCCAG PS5-S75 F: CCGTAA GCATAGCA CTTGGC R: ACGATG GTTACCGT TTACCGA PS4-S76 F: GCGCTA GCCTTCTC AACGTA R: GCTTCC ATTGCCCT TCCAAC

JSWX

a Forward primer; b reverse primer; c Allele richness; d observed heterozygosity; e expected heterozygosity; f exact p-value of Hardy-Weinberg equilibrium; g Null allele frequencies; h inbreeding coefficient and ipolymorphism information content.

Fig. 2. Neutrality tests of the 42 microsatellite loci developed in this study. Loci located in the red region are candidates for positive selection, and those in the yellow region are candidates for balancing selection, while the grey region represents neutral candidates. S23 and S16 showed balancing selection while the other loci fell into the neutral space.

JS WX

JX GZ

L. Ma et al. / International Journal of Biological Macromolecules 121 (2019) 1135–1144

GD GZ

1142

Fig. 3. Genetic structure of three Phenacoccus solenopsis populations based on 42 markers using STRUCTURE when k = 2. GDGZ, population from Guangzhou, Guangdong Province; JXNC, population from Nanchang, Jiangxi Province; JSWX, population from Wuxi, Jiangsu Province.

4. Discussion For the first time, we sequenced random genomic sequences of P. solenopsis with size of 267.07 Mb. This genome size is comparable to that of other Hemiptera species which is usually estimated to be several hundred megabases, such as Myzus persicae (Hemiptera: Aphididae) (470 Mb) (https://doi.org/10.1101/063610.), Diuraphis noxia Kurdjumov (Hemiptera: Aphididae) (397 Mb) [51] and Bemisia tabaci (Hemiptera: Aleyrodidae) (615 Mb) [52]. With the development of next-generation sequencing, sequencing a DNA library using Illumina platform becomes high in efficiency and low in cost. The assembled sequences from single DNA library in our study covered 89.6% of the estimated genome size, which could be used as references for genome-wide survey of microsatellites. Compared with traditional isolation approaches of microsatellite development, such as PCR-based [53], enriched library [54–56], this method is not only high in efficiency, but provide approach to isolate abundant microsatellite

GDGZ JSWX JXGZ

Fig. 4. Genetic structure of three populations in Phenacoccus solenopsis based on 42 markers by DAPC. GDGZ, population from Guangzhou of Guangdong Province; JXGZ, population from Nanchang of Jiangxi Province; JSWX, population from Wuxi of Jiangsu Province.

marker at genomic level. Microsatellite markers have been isolated from transcriptome sequences in some species [57,58]). The developed markers from coding regions may be prone to selection [19], while microsatellites isolated in our study contain abundant neutral markers from non-coding regions. The richness of microsatellites in P. solenopsis estimated here was higher than in previous reports based on ESTs [23] and transcriptome [24]. Dinucleotide repeats are usually more abundant than tri-, tetr-, penta and hexa-nucleotide motifs in insects [21,22,59,60]. However, we found more trinucleotide motifs than the other types of repeats in P. solenopsis, which was also noted in the previous study on this species based on transcriptome sequences [24]. A predominance of trinucleotide repeats has been noted in other insects, such as Tenebrio molitor (Coleoptera: Tenebrionidae) [57]. The proportion of microsatellites with most frequent motif is 23.38% (AAT) in P. solenopsis, while in other insect species, this value was usually high, such as in Venturia canescens (Hymenoptera: Ichneumonidae) (62%) and Diabrotica virgifera (Coleoptera: Chrysomelidae) (60%) [61]. This indicate a relatively low bias of microsatellite with different repeat motifs in P. solenopsis. In our study, the genetic diversity of H0 and HE ranged from 0.00 to 0.92 and 0.00 to 0.73, respectively, which were similar to the result inferred from EST-SSR [23]. Some loci showed deviation from HWE in P. solenopsis (such as PS4-S27, PS5-S28, PS3-S32, PS5-S34 and PS3-S43 in Table 2), which might be caused by an excess of homozygotes or heterozygotes. Although the cotton mealybug was introduced into China only around ten years ago, our results suggest that genetic structure among the three tested populations is already present in this species. Population genetic differentiation in invasive species in introduced areas might be caused by multiple introductions [19] or rapid evolution [18] although the latter would not be expected to cause differentiation in a set of selectively neutral microsatellites. Further sampling from its native and introduced range will help to explain the genetic differentiation of P. solenopsis in China. The markers we have developed here should be useful in a detailed population genetic analysis of P. solenopsis to investigate these hypotheses further.

5. Conclusions Base on the de novo assembled genome, forty-two polymorphic microsatellite markers were developed and validated for the P. solenopsis. Population level study revealed genetic structure across three population of P. solenopsis. The specific markers deveopled will provide efficient molecular tools for studies on invasion history, pattern of dispersal and management of P. solenopsis.

L. Ma et al. / International Journal of Biological Macromolecules 121 (2019) 1135–1144

Acknowledgement We thank Xu-Bo Wang and Hua-Yan Chen for their helps on the collection of specimens. This research was supported by the National Key R & D Program of China (2016YFC1202100), the National Natural Science Foundation (31472025), the Natural Science Foundation of Beijing Municipality (6162010), The Beijing Key Laboratory of Environmentally Friendly Pest Management on Northern Fruits (BZ0432), all of China. Author contributions Conception and design, Shu-Jun Wei and Zhong-Shi Zhou; Data curation, Ling Ma; Formal analysis, Ling Ma and Li-Jun Cao; Methodology, Li-Jun Cao and Shu-Jun Wei; Resources, Zhong-shi Zhou, Ling-Ma, Ya-Jun Gong; Supervision, Ai-Ping Zeng, Shu-Jun Wei and Zhong-Shi Zhou; Validation, Ya-Jun Gong and Ai-Ping Zeng; Writing – original random, Ling Ma; Writing – review & editing, Ary A. Hoffmann, Shu-Jun Wei and Zhong-Shi Zhou. Conflicts of interest The authors declare no conflict of interest. References [1] J.D. Tinsley, Notes on Coccidae, with descriptions of new species, Can. Entomol. 30 (12) (1898) 317–320. [2] M.I. Arif, M. Rafiq, A. Ghaffar, Host plants of cotton mealybug (Phenacoccus solenopsis): a new menace to cotton agroecosystem of Punjab, Pakistan, Int. J. Agric. Biol. 11 (2) (2009) 163–167. [3] S.S. Ibrahim, F.A. Moharum, N.M.A. Elghany, The cotton mealybug Phenacoccus solenopsis Tinsley (Hemiptera: Pseudococcidae) as a new insect pest on tomato plants in Egypt, J. Plant Res. 55 (1) (2015) 48–51. [4] G. Abbas, M.J. Arif, M. Ashfaq, M. Aslam, S. Saeed, Host plants distribution and overwintering of cotton mealybug (Phenacoccus solenopsis; Hemiptera: Pseudococcidae), Int. J. Agric. Biol. 12 (3) (2010) 421–425. [5] Y.P. Wang, W. Gillianw, R.Z. Zhang, The potential distribution of an invasive mealybug Phenacoccus solenopsis and its threat to cotton in Asia, Agric. For. Entomol. 12 (4) (2010) 403–416. [6] T.W. Fuchs, J.W. Stewart, R. Minzenmayer, M. Rose, First record of Phenacoccus solenopsis Tinsley in cultivated cotton in the United States, Southwest. Entomol 16 (3) (1991) 215–221. [7] S.A. Wu, R.Z. Zhang, A new invasive pest, Phenacoccus solenopsis, threatening seriously to cotton production, Chin. Bull. Entomol. 1 (2009) 159–162. [8] Y.Y. Lu, L. Zeng, L. Wang, Y.J. Xu, K.W. Chen, Precaution of solenopsis mealybug Phenacoccus solenopsis Tinsley, J. Environ. Entomol. 30 (4) (2008) 386–387. [9] H. Fang, J.M. Zhang, P.J. Zhang, Y.B. Lu, Reproduction of the solenopsis mealybug, Phenacoccus solenopsis: males play an important role, J. Insect Sci. 13 (137) (2013) 1–12. [10] L.D. Zhang, D. Ling, D.Z. Wang, J.Q. Wei, Z.L. Qiu, H.M. Wu, Q. Rao, Infestation and phylogenetic study of an invasive mealybug phenacoccus solenopsis in Hangzhou, J. Biosaf. 25 (2) (2016) 127–132. [11] J.F. Wei, H.F. Zhang, W.Q. Zhao, Q. Zhao, Niche shifts and the potential distribution of Phenacoccus solenopsis (Hemiptera: Pseudococcidae) under climate change, PLoS One 12 (7) (2017), e0180913. [12] M.X. Liu, Y. Xu, J.H. He, S. Zhang, Y.Y. Wang, P. Lu, Genetic diversity and population structure of broomcorn millet (Panicum miliaceum L.) cultivars and landraces in China based on microsatellite markers, Int. J. Mol. Sci. 17 (3) (2016) 370–387. [13] C.E. Lee, Evolutionary genetics of invasive species, Trends Ecol. Evol. 17 (8) (2002) 386–391. [14] D. Chu, G.X. Liu, H.B. Fu, W. Xu, Phylogenetic analysis of mt COI reveals the cryptic lineages in Phenacoccus solenopsis complex (Hemiptera: Pseudococcidae), Acta Entomol. Sin. 52 (11) (2009) 1261–1265. [15] Z. Chen, J.A. Zhang, H.F. Fu, Z.Z. Xu, K.Z. Deng, J.Y. Zhang, On the validity of the species Phenacoccus solenopsis based on morphological and mitochondrial COI data, with the description of a new body color variety, Biodivers. Sci. 20 (4) (2012) 443–450. [16] J. Zhao, Y. Sun, Y.A. Tan, L.B. Xiao, L.X. Bai, X.J. Lu, S.F. Zheng, Genetic differentiation among different geographic populations of Phenacoccus solenopsis based on sequences of COI and 28S rDNA, J. Cotton Sci. 26 (2) (2014) 130–137. [17] M.Z. Ahmed, J. Ma, B.L. Qiu, R.R. He, M.T. Wu, F. Liang, J. Zhao, L. Lin, X.N. Hu, L.H. Lv, Genetic record for a recent invasion of Phenacoccus solenopsis (Hemiptera: Pseudococcidae) in Asia, Environ. Entomol. 44 (3) (2015) 907–918. [18] L.J. Cao, S.J. Wei, A.A. Hoffmann, J.B. Wen, M. Chen, Rapid genetic structuring of populations of the invasive fall webworm in relation to spatial expansion and control campaigns, Divers. Distrib. 22 (12) (2016) 1276–1287. [19] L.J. Cao, Z.M. Li, Z.H. Wang, L. Zhu, Y.J. Gong, M. Chen, S.J. Wei, Bulk development and stringent selection of microsatellite markers in the western flower thrips Frankliniella occidentalis, Sci. Rep. 6 (2016) 26512–26519.

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