Population variation and polymorphism of the Cotton Pink Bollworm Pectinophora gossypiella (Saunders) (Lepidoptera: Gelechiidae) using Inter Simple Sequence Repeats (ISSRs) as molecular markers

doi:10.21203/rs.3.rs-3551467/v1

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Population variation and polymorphism of the Cotton Pink Bollworm Pectinophora gossypiella (Saunders) (Lepidoptera: Gelechiidae) using Inter Simple Sequence Repeats (ISSRs) as molecular markers

https://doi.org/10.21203/rs.3.rs-3551467/v1

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The most common pest infesting Egyptian cotton plants is the pink bollworm Pectinophora gossypiella (Saunders) (Lepidoptera: Gelechiidae). Intensive insecticide applications required for effective control in production season, resulting insecticide resistance problems. Some investigation ideas were floating for sponsored management. Thus, PCR based examination of pest population DNA tissue differentiation using eight Inter-Simple Sequence Repeats (ISSRs) primers screening for genetic diversity and population differentiation between some Egyptian field populations, based on similarity in the pattern of SDS-page bands. We detected 117 loci with an average population polymorphism of 97.64%. Genetic distance and similarity between groups were calculated and UPMGA phylogenetic tree were constructed. Based on Nei’s distance the specimens statistically grouped into three major clusters and six sub clusters. The average primer efficiency calculated value of PIC = 0.0027, 0.0029 and 0.22, H = 0.326, 0.346 and 0.266, and MI = 0.0669, 0.0768 and 0.04 for Sharkia, Benisuef and Fayoum respectively. Number of identified alleles Na = 1.92, 2.1 and 2.03 and the effective number of alleles Ne = 1.6, 1.93 and 1.78 respectively. Principal component, correspondence analysis and AMOVA results revealed great variance between populations. Moderate level of gene flow was 0.472 to 0.474 and 0.462. Infinite diversity distinguished by Fixation index Fst was ranged from 0.674 to 0.677 across the genotypes. Estimated Shannon’s index (I) procedures of calculations completed as inter-population differences was 6.62 and Nei’s genetic diversity h = 4.7, 4.656 and 4.60. Eveness was 1.09, 1.089 and 1.079 and estimated diversity was 1.24, 1.23 and 1.298, and species richness was 115, 117 and 117 for the same arrangement respectively. All results indicates a high level of genetic variation exists among the three geographical sample populations of the same pest.

ISSR-PCR Primers

polymorphism

population differentiation

genetic diversity

similarity

distance

Egyptian Governorates

Pectinophora gossypiella

Cotton is the most significant fibre crop in Egypt and at most world countries. It grows in both tropical and sub-tropical regions throughout the world as well as the cultivated area of African regions, (El-Aswad and Aly 2007 and El-barkey et al. 2009). The production of Egyptian cotton are facing a much lepidopteran pest infestation, which cause more than 80% of damage. The pink bollworm Pectinophora gossypiella (Saunders) (Lepidoptera: Gelechiidae), is known to be one of the major insect pests that causes severe damage and loss to Egyptian cotton production, (Hassan et al. 2017). The larvae were spend much time in diapause stage inside the cottonseed, after gather and before industries, and become well distributed in all cotton-growing season zones of Egypt. The failures to control this pest with insecticides is due to its larval stage hiding inside cotton bolls and insecticide application exposure cannot reach except the eggs and first instar. In addition, repeated applications induce the development of insecticide resistance (Derbalah et al. 2014 and Darwish et al. 2017). To limit resistance to insecticides some actions were bending depending on genetic variation differentiations such as monitoring the genetic diversity, conservation priority that enhance ecosystem stability of the pest and the genetic structure of its populations (Abd-Elhady and Abdel-Aal 2011 and Khidr et al. 2006). A variety of molecular markers developed for a variety of purposes based on PCR, including Simple Sequence Repeat (SSR) (Luqu et al. 2002), Inter Simple Sequence Repeat (ISSR), Random Fragment Length Polymorphism (RFLP), Random Amplified Polymorphic DNA (RAPD) (Singh et al. 2017). Phylogenetic manipulation to recognize the evolutionary relationships among organisms require some array data types, such as morphological traits and molecular sequence data that become more common, Minoarivelo et al., 2015. Moreover, exploitation and utilization of ISSR-PCR as a DNA marker technique for high polymorphism and high efficiency of the proper amplification due to the number of fragments and bands produces when using dissimilar templates and primer of random nucleotide sequence to attain results of genetic differentiation as a phenotypic measurement tools considered helpful in some argument including genetic diversity assessment, insect genome fingerprint in diploid or haploid genotypes, genetic map construction (Liu et al., 2015). Furthermore higher annealing temperature processes of PCR thermal phases can differentiate between closely related individuals and the production of nucleotides sequence to identify orthologues of other species or homologs of the same species at the same geographical locations related to the proper traits (Tonk, et al. 2014 and Deng etal., 2019). The investigation of the genetic differentiation using ISSR technique were done in numerous agriculture pests genomes information such as blackflies (Diptera: Simuliidae) (Liu 2010, Dušinsky et al. 2006), the planthopper Sogatella furcifera (Barbosa et al. 2014), the boll weevil (Dos Santos et al. 2021), Bactrocera zonata Al-Senosy and Badr 2018). In addition to, bollworm species collection genetic diversities as (Moustafa et al. 2021). Insect habitually occupying a relatively small geographic area and construction of populations occurs in very heterogeneous environments due to differences in average temperature and precipitation and some topography features (Salmela et al., 2016). Investigating genetic diversity within and among populations of a species, can lead to increase knowledge of the evolutionary processes underlying genetic variation, provide important basic information for insect climate conditions and suitable habitats to conserve the genetic resources (Wang et al. 2010). Thus regional and local scales in the high environmental heterogeneity and a high potential for divergent selection can affect patterns of nucleotide variation at some loci. But also from demographic processes related to population size changes, genetic drift and the development of local adaptation will realize the spatial heterogeneity in the environment. Phylogenetic is used by evolutionary ecologists to explain the emergence and maintenance of diversity estimating the rate and time of phenotypic diversification or the rate of speciation or extinction within a set of species Minoarivelo et al., 2015. Then the magnitude of genetic differentiation between populations depends on several factors including demographic history of the pattern of natural selection that can lead to local adaptation, level of gene flow and heterogeneity (Poyraz 2016). Generally populations with low average densities may be particularly affected by such spatial variability compared to higher-density populations. Therefore monitoring the effects of these processes on population variation at the molecular scale are urgently needed (Lega etal., 2002). Study of nucleotide diversity is known to assess genetic variation within and among insect populations from different environments across cultivated areas (Vijayan et al., 2006) and build brief relationship through pairwise genetic distance and matrix to show dissimilarity between samples and to draw a phylogeny tree that can created from gel page of electrophoresis bands and lane of different samples or directly from the alignment of multiple sequences (Guz and Dağeri 2017). In this study within population sampling exploited to focus on genetic differentiation among P. gossypiella few populations at a definite scale, and to investigate the nature genetic variations in the three-governorate population of P. gossypiella that attack cotton crops in closely related geographical areas of Egyptian governorates without insecticide exposure through PCR-ISSR which considered the molecular procedure of ecological genetic diversity profile produces acquired in the same species and population level to show the environmental differences between sites.

Insect sources and sampling

A large quantity of cotton plant bolls heavily infested with Pectinophora gossybiella larvae were collected from three localities of cotton cultivated lands in Egypt (Sharkia, BeniSuef and Fayoum) of Nile Delta governorates (Fig. 1) and transferred to the laboratory directly used without any insecticide bioassay operations. The larvae extracted from bolls into petri dishes with a thin layer of pure cotton inside Petri dishes and transferred to a molecular biology lab for DNA extraction.

Molecular biology techniques:

1-Genomic DNA extraction, purification and quantification

DNA extracted using modified CTAB method (Doyle and Doyle, 1990), and purified by phenol/chloroform extraction method (Moreau, 2014). For total DNA isolation, larvae weighing about 50 to 100 mg macerated with micro pestle in a 1.5 mL Eppendorf tube containing 500 µL of grinding homogenizing buffer, followed by 25µL of 20% SDS,20µL of 20 mg /mL proteinase K, 100µL of 0.5M EDTA and 50µL of 0.1M Tris (pH8.0). Then incubated at 55°C for 3 hours or overnight. Then 550µL of phenol added and shacked as vortex for 1 minute and centrifuged for 5–10 minutes. The supernatant placed in new tubes. The previous steps repeated 2–3 times, until the cloudy color of supernatant disappeared. Next, 500µL of chloroform added to samples, vortex for 1 minute and centrifuged for 5–10 minutes. Then, the supernatant placed in new-labeled tubes and repeated these steps 5–7 times. Hence, 750µL of cold 100% EtOH added to the supernatant kept for 2 hours at 20°C overnight. Then, samples centrifuged for 10 minutes to pellet DNA and gently discarded the supernatant by slowly pouring it off, leaving only the pellet. The pellet washed by 70% ethanol dried and finally suspended in 300–500µL sterile distilled water. Quality of genomic DNA assessed by agarose gel (0.7% prepared in TAE buffer) that high molecular weight and DNA band near wells and no streaking or RNA band.

2-Amplification of DNA using ISSR:

The PCR reactions were performed in a final volume of 25 µl containing 50 ng total genomic DNA, 12.5µl 2× Deam Taq green master Mix kit (2X) and 50-pmole primer found in Table (1). Gently vortexed and centrifuged. Then placed a thin-walled PCR tube on ice. Some components added for each 25 µl volume of reaction: Dream taq green M.M (2X) equal 12.5µl, primer 2µl, template DNA50 ng, water, nuclease-free to 25 µl and total volume 25 µl, gently vortex and spin down. The PCR cycling conditions consisted of an initial denaturation at 95°C for 5 min followed by 40 cycles of denaturation at 95^oC for 30 seconds, annealing at 54^oC for 1 min, extension at 72°C for 2 min and a final extension at 72°C for 10 min. The amplified PCR fragments separated on 1.5% agarose gel in Tris-acetic acid/EDTA buffer (1×TAE) and electrophoresis carried out with a constant voltage.

Table 1

List of ISSR primes used and their information.
No	Name	Sequence	Frequency/loci	NP	Fixed band	F/I	Tm	Length	MW bp	GC %
1	UBC818	(CA)8G	0.25	13	2	5.5	50.2	17	5148	52.9%
2	UBC834	GAGAGAGAGAGAGAGAGAGAT	0.86	16	0	4.6	50.5	21	6728	47.6%
3	TA-1	(AG)10C	1	11	4	7.7	51.9	12	3534	91.7%
4	UBC-823	TCTCTCTCTCTCTCTCC	0.26	13	0	5.1	46.3	17	5036	52.9%
5	UBC-835	AGAGAGAGAGAGAGAGYC	0.75	12	2	6.25	47.1	18	5725	52.8%
6	17898A	(CA)6AC	0.5	13	2	5.75	47.5	14	4217	50%
7	17898B	(CA)6GT	0.25	14	0	4.25	43.2	14	4403	50%
8	HB 11	(GT)6CC	0.25	14	1	5.0	56.8	14	4104	92.9%
		total		106	11			127
NP = number of polymorphic bands, F/I = frequency /individuals, Tm = melting temperature, MW = Molecular weight, and GC = guanine cytosine ratio

Data Analysis of the gel results:

Fragment analysis preparations

Gel picture documents for each primer screened by gel analyzer software and get the results of gel Bands and Lanes. Only the distinct amplified DNA fragments were scoring by (1) as presence and (0) as absence constructing matrix of bands as binary data of distance and similarity matrixes, and ignoring the weak bands.

1- Performance of each primers: measured by the online program of marker efficiency calculations using absence and presence of gel band data and this iMEC site was (https://irscope.shinyapps.io/iMEC/). Results attained by calculating different parameters was the content of polymorphism information where pi is the allele frequency and expected heterozygosity (H), by serial formula of calculations began from H = 1-∑pi and continued to the final of the online test according to (Amiryousefi, et al. 2018, Chesnokov, and Artemyeva 2015). Generally, all calculation of all analysis in this study was by using such recommended software according to Labate (2000).

2-Genetic differentiation

completed by similarity indices calculations to compare patterns between populations according to (Hancock 2004). Dendrogram constructed by Past-software (Hammer et al., 2001), using average linkage UPGMA tree (Unweighted Pair Group Arithmetic Mean) under different Bootstrap 1,000 replicates of analysis (Botstein et al., 1980 and Lin et al., 2015). Certainly Genetic dissimilarity coefficients among the P.gossypiella population individuals were estimated using Dice method from FAMDv.1.3 software (Schlüter and Harris (2006). and the phylogenetic tree was obtained from unweighted pair group matrices by method with arithmetical averages of linkage (UPGMA) (Sneath and Sokal 1973).

3-Genetic relationship achieved by four statistical analyses

firstly, cluster analysis performed to determine the relative genetic distance and similarity between pairwise populations and to check the consistency of the genetic differentiation. Secondly, Principle Component Analysis (PCA) based on Euclidian measures. In addition to corresponding analysis and AMOVA (Analysis of Molecular Variance and diversity) calculated using the FAMD software, (Schlüter and Harris (2006) and Past v.3 software (Hammer 2001) and for comparing results of data analysis and visualization.

4-Genetic diversity analysis:

A-Quantifying genetic diversity in relation to population structure completed according to Hedrick (2005) and Chesnokov, and Artemyeva (2015), from estimated (Gst) of Nei’s genetic differentiation index among populations. Where genetic divergence attained using, FST is the coefficient of gene differentiation (Nei 1973). And GST = DST/Ht= (Ht-Hs) /Ht’ where DST is the average gene diversity between subpopulations, DST= (Ht-Hs) and Ht = (1–Σpi 2), where pi is the frequency of allele at a locus in a population and Σ is the summation of all alleles representing gene diversities can be defined as expected heterozygosity under Hardy-Weinberg equilibrium and averaged among sub populations (Hs) and of total population (Ht). In addition, gene flow (Nm) = 0.5 (1-GST)/GST was calculated using Popgene version 1.32 (Raymond and Rousset 1995 and Evanno, et al. 2005).

B-Quantifying genetic diversity using Shannon (1948) index, formula is: H′= -Σ [ni/N] ln [ni/N]. Where: ni is the number of individuals in each species, N is the total number of individuals in the sample, and Σ is the total number of species in the sample. Evenness index (Pielou 1966), e = H′/log S Where: H is diversity index and S is number of species and index of similarity (Sorensen 1984 and Roswell et al. 2021) S % = (2C / A + B) X 100. Where, A is number of species in one study site, B is number of species in another study site and C number of species common to both sites. S = species richness H = Shannon's index of diversity, E = Shannon's evenness index. The power of the base e (e = 2.718281828) must be raised to obtain the natural logarithm (ln) number.

1-Polymorphism quantification and fingerprinting by ISSR primers:

PCR amplification of P.gossypiella DNA from three distinct geographical area of Egyptian governorate samples detected by eight ISSR primers and analytically performed in Cairo University molecular biology laboratories considered as genotype were showed the percentage of polymorphism data found in (Table 1). Formerly, banding pattern of specific gel images detected for each primer was very distinct and repeatable, images found in (Fig. 2). Results of all primer together showed total number of bands were 468 in average 117 per all sample (geographical area was Sharkia, Benisuef and Fayoum) as sample and total 106 polymorphic band representing averaged 90.6% polymorphism. ISSR profiles with some amplified DNA fragments ranged from a total 30, 36 and 37 polymorphic band for all primer used and mean about 3.7 to 4.6 polymorphic band for each primer (standard deviation calculated was 1874.9 and standard error = 43.3 of data in Table 2). The results concluded that detecting of high levels of polymorphism is one of the most important features of the ISSR technique, and it useful for this study (Tables 1 and 2). The range of the amplified band noted from 100 to 2000 bp (Fig. 2). However, all primers generated multiple band patterns spanned from 16 to 17 band with a mean of 15.93 alleles per loci. However, when speak about individual places (Samples); the total number of bands was 134,136 and 128 for Sharkia, Benisuef and Fayoum, respectively. Number of polymorphic bands of all primer was 24, 26 and 18 in the same arrangement, respectively, and representing percentage of polymorphism was 17.4, 16.25 and 13.89% for the same arrangement respectively. Similar results were attained by Liu et al. (2009), found highly polymorphic 13 microsatellite loci from 527 sample of P. gossypiella from 14 sites of three countries, have allele frequency distributions proved that Chinese P. gossypiella derived from the Pakistani and American populations. And (Velu et al. 2008), PCR-ISSR markers among mutant silkworm strains of Bombyx mori were 73.45% found to be polymorphic. Al-Senosy et al. 2018, determine the level of malatox resistance in peach fruit fly (Bactrocera zonata) by (ISSR) to make fingerprinting by five primers, detected a total of 176 bands, and 35.2 bands per primer and percentage of polymorphism was 66% (P13) to 100% (C16). Moustafa et al. 2021, Genetic profile of Earias insulana (Boisd.) (Lepidoptera: Noctuidae) larvae was identified using five ISSR primers resulted in 15 bands with molecular weight between 1630 and 175 bp.

Table (2) Information of polymorphism of each primer used in ISSR procedure represented by number of polymorphic band and percentage of polymorphism produced by gel scanning.

	Sharkia				Benisuef				Fayoum
Primer	a	b	c	Freq mono	a	b	c	Freq	a	b	c	Freq
UBC818	17	3	17.6	0.20	17	3	17.6	0.20	16	7	12.5	0.13
UBC834	16	4	40	0.0625	17	4	23.5	0.125	16	5	31.25	0.0625
TA-1	20	5	25	0.4	20	5	25	0.4	18	3	16.6	0.26
UBC-823	15	5	33	0.076	19	6	13.5	0.38	16	3	18.7	0.15
UBC-835	17	5	29.4	0.35	16	4	25	0.28	16	4	25	0.28
17898A	15	2	13.3	0.26	14	5	7.1	0.13	14	5	35.7	0.13
17898B	18	4	22	0.14	16	2	12.5	0.071	16	2	12.5	0.071
HB 11	16	2	12.5	0.13	17	6	17.6	0.20	16	7	12.5	0.13
Total	134	30	139.3	1.618 0	136	36	130	1.786 0	128	37	111.15	1.213
Mean	16.75	3.75	17.4	0.20	17	4.12	16.25	0.223 0	16	4.6	13.89	0.151
a = Total No, of band b = No. of polymorphic bands c=% polymorphism

2-Marker efficiency analysis (MEA):

The polymorphic efficiency of each ISSR primers iMEC details found in table (3). In addition, details about Shannon index found in Table (3). PIC is the polymorphic information content, its values is an important likelihood index of variations, diversity and number of allele frequency distribution, where the higher PIC was 0.379 recorded for Sharkia followed by 0.286 Benisuef larvae and the lowest was 0.226 for Fayoum larvae. The estimated heterozygosity (H) is the second informative genomic marker indices of polymorphism its values varied from 0.346 for Benisuef and 0.326 for Sharkia and 0.260 for Fayoum. The arithmetic means of heterozygosity (Havp) ranged between 0.00296 for Benisuef and 0.0278 for Sharkia. The effective multiplex ratio (EMR) is the publicity of primer polymorphism was 26, 24 and 18. Also marker index (MI) values was 40.768, 0.669 and 0.04 for Benisuef, Sharkia, and Fayoum respectively. Discriminative power (D) gain similar values for the three insect collections, values ranged between about 0.977 to 0.952. Moreover, note the Rp is 0 for the governorate analysis but in the primer analysis RP ranged from 90 to 97. Generally, all primers used in this study gained appropriate results of polymorphism. Similar results were detected by some scientist of plant and animals as (Khan et al. 2021), who set of 32 ISSRs were screened 96 primers which detected a total of 510 loci of 44 Bambara groundnut (Vigna subterranea L. verdc.) genotypes with an average of 97.64% polymorphism using marker efficiency analysis of the same site. And Liu etal., 2020 diversity and similarities between different Lycium varieties based on ISSR analysis

Table 3

Marker efficiency analysis (MEA).
	H	EMR	H.avp	PIC	D	Mi	Rp
Calibrator	1.27	1.09	1.088	1.92	1.32	1.86	0.0
Sharkia	0.326	24.0	0.00278	0.379	0.960	0.0669	0.0
Benisuef	0.346	26.0	0.00296	0.286	0.952	0.0768	0.0
Fayoum	0.260	18.0	0.0022	0.226	0.977	0.040	0.0
PIC = Polymorphic Information Content. Rp = Resolving Power. D = Discriminating Power. H = Expected heterozygosity. Hayp = Arithmetic mean heterozygosity. MI = Marker index. EMR: Effective multiplex ratio, TNB = Total number of bands; NPB: Number of polymorphic bands

3-Genetic differentiation (Genetic distance and similarity) among P. gossypiella populations:

The genetic relationships among the three species genotype were determined, the scoring data were completed the calculation of the similarity matrices in the cluster analysis and dendrogram generated using UPGMA method (Fig. 4). Distance and similarity matrices and statistical stability of the clusters was estimated by Bootstrap analysis was about 1000 replication using the Past software package, data in table (4 and 5). this data revealed that distance between the three populations ranged from 9.849 to 4 means that there is 98.4% variability related to the different geographic distance between them (Table 4). In addition, data showed such similarity indices ranged from 0.89 to 0.12 (Table 5). In contrast (Liu et al. 2010), find that the genetic diversity of Chinese populations of P. gossypiella using mitochondrial COII and Nad4, was extremely low of genetic variability between two regions among nine populations examined and populations were not significantly different from each other. Similar results was cited about insect population differentiation using ISSR-PCR exploitation like (Vijayan et al. 2006), was Samia cynthia ricini (Lepidoptera: Saturniidae), the Indian Eri silkworm, using Twenty primers produced 87% of inter population variability among the six populations.

Table (4) Pairwise genetic distance values between P.gossypiella populations quantifying genetic relationships.

	Pairwise Individual Distance of Governorate				Population features
Population	Calibrator	Sharkia	Benisuef	Fayoum	pi	Allele freq freq	N.allele
Calibrator	0	0	0	0	0.809	0.51	110
Sharkia	9.849	0	0	0	0.79	0.502	25
Benisuef	9.849	3.464	0	0	0.72	0.52	27
Fayoum	9.644	4.243	4	0	0.75	0.523	19

Calculation of distance, or dissimilarity, follows one of two possible models: the equilibrium model that mean distance remains constant over time, equilibrium exists between migration and genetic drift and the disequilibrium model that means distance changes with time through species overlap, habitat suitability through climate changes, insect adaptations or environmental acclimations, population composition, population structure, genetic drift, densities, abundances, similarities and heterogeneity, dispersal processes under neutrality (Lynch and Milligan 1994) and (Re´jou-Me´chain and Hardy 2011).

Table 5

Similarity index of three Pink boll worm *P. gossipiella* population each for 8 primer. Individually.
Primer	Calibrator	Sharkia	Beni-suef	Fayoum	Primer	Calibrator calibartor	Sharkia	Beni-suef	Fayoum
UBC818	1	0.24	0.24	0.25	UBC-835	1	0.35	0.38	0.5
	0.24	1	1	0.8		0.35	1	0.89	0.44
	0.24	1	1	0.8		0.38	0.89	1	0.5
	0.25	0.8	0.8	1		0.5	0.44	0.5	1
UBC834	1	0	0.12	0.13	17898A	1	0.27	0.14	0.14
	0	1	0.67	0		0.27	1	0.67	0.67
	0.12	0.67	1	0		0.14	0.67	1	1
	0.13	0	0	1		0.14	0.67	1	1
TA-1	1	0.5	0.5	0.33	17898B	1	0.33	0.13	0.25
	0.5	1	0.8	0.75		0.33	1	0.67	0.67
	0.5	0.8	1	0.75		0.13	0.67	1	0.5
	0.33	0.75	0.75	1		0.25	0.67	0.5	1
UBC-823	1	0.27	0.53	0.38	HB 11	1	0.13	0.12	0.13
	0.27	1	0.5	0.4		0.13	1	0.8	0.5
	0.53	0.5	1	0.67		0.12	0.8	1	0.8
	0.38	0.4	0.67	1		0.13	0.5	0.8	1
Standard genetic distances (DS) =-In [Jxy/JxJy root (Nei and Li, 1972)

4-Inter population genetic diversity

Quantifying genetic diversity concluded dimension of inter and Intra population genetic diversity, using analysis of molecular variance (AMOVA) calculated from FAMD Software, (Schlüter and Harris 2006) performed using absence and presence of bands of ISSR markers, where constructing molecular distance matrix scores to attain the total variance among and within population group component. Degrees of freedom and P-values at different hierarchical levels, between pairs (Euclidean) of multi locus ISSR phenotypes was calculated and results found in table (6). The data showed variance of within population was 0.1298, whereas variance among population was − 0.00126 and percentage of variance was 100.98 of within population and − 0.984 of among populations. The within population variance in facts expressed that the populations are homogenous in nature, whereas the higher genetic variability (100.98%) among the populations indicated to populations different between each. Data showed the total variation in P. gossyoiella three populations was mostly attributable to diversity within populations (141.47%), whereas variation of diversity among populations (77.9%). while the total observed phenotypic diversity among all population was highly significant (P < 0.001). Diversity profile and clusters showing similarity found in Fig. (3, 4), data showed high levels of variation within populations in each taxon and the phenotypic diversity among subpopulations was highly significant (P < 0.001). Figures shows, distance was 24, 36.3 and 71.8 and Similarity was 76, 36.6 and 28.1 for Sharkia, Benisuef and Fayoum. The molecular variance measured using AMOVA sustained F-statistics testing for differentiation among groups of populations and the genetic structure based on the method of a K-means clustering analysis incorporated in calculation of F-statistics using AMOVA were more useful, Meirmans and Liu (2018).

Table 6

Analysis of molecular variance (AMOVA) for insecticide treated populations of *P.gossypiella* using random amplified polymorphic DNA phenotypes.
AMOVA	Sum of squares	Statistics	Variance components	% Variance	df	P	PhiST
Among populations	(SSD)AP	0.779	0.00126	-0.984	7	0.001	-0.00984
Within populations	(SSD)WP	14.147	0.1298	100.98	109	0.001
Total	(SSD)T	14.925	0.1285	100	116	0.001

5-Intra population genetic diversity:

The hierarchal cluster analysis completed based on unweighted pair group method to produce a cluster tree dendogram by ordinal weight kneibuor joining (Fig. 5) and the phenotypic relationship among P. gossypiella populations of Egyptian governorate samples this was using past software were attained. The figure showed that samples distributed in line connected of the constructed nodes attached to each other where the length of any branch refer to the genetic distance. The data scores divided into two main groups according to genetic similarity reached 97%. Then those groups divided into two sub-cluster with genetic similarity reached 45% and 48%, bootstrap values given at each group was 100. The phonetic concept measures in many organisms requires identification of clusters by multivariate statistics, the smallest unit in these clusters has sufficient similarity called a species. Based on specific degree there is a similarity between any two similar species objects in the universe (Phenotype), the members of the same species can be significantly different and individuals of various species may look more related to each other than of members of the same species (Aldhebiani 2018). From literature, the central Indian population samples originating from the various geographical regions of Madhya Pradesh, India. All locus fall under Hardy–Weinberg equilibrium except one, that highly informative and allele frequencies was combined power of discrimination > 0.99999. In addition, clustering pattern and genetic distance showed genetic relatedness with geographically close populations and significant variation with distant populations achieved by clustering pattern of the kniebour joining tree and the Principle component analysis (Shrivastava et al. 2015). Also Wang et al. 2010) identify five haplotypes of P.gossypiella different geographical populations, its flanking sequences revealed significant differences among populations from Australia and China compared to other global populations.

4-Genetic relationship achieved by statistical analyses:

The study data used to calculate principle component analysis (PCA) scores and completed the statistical technique for plot dataset, in few dimensions and identify clusters of closely related data points to identify and describe clusters of genetically related samples linearly uncorrelated. In this study, scores of the nods in addition to bootstrap and eigenvalues of the covariance matrix found in table (6) (Fig. 6), data in table and figures proved that geographical places was different in each other of scores and nods with minimum dissimilarity was 0.2 and maximum dissimilarity was 1. Nucleotide diversity was pi = 0.809, 0.79, 0.72 and 0.75 at number of segregating site average was 14. In addition principle and correspondence analysis creates orthogonal components like PCA 1,2,3 depending on the scores used but it preserves chi-square distance (Figs. 6 and 7). The variance values mention to the distance between samples like 2.79, 3.27, 0.59 for principle component analysis and 18.0, 4.33, 6.5 for correspondence analysis respectively for Sharkia and for Benisuef and Fayoum that refer to the geographical distance detected between each was 24,36.3, 71.8 and similarity was 76.0,63.6,28.1 for each respectively. From literature, (Naik et al. 2020), proved that two populations of pink bollworm, occurring early in the season are genetically close to the late season populations using partial Cytochrome Oxydase1 gene. And Zhang etal., 2021 Study the diversity of 27 pea aphid samples from 13 geographic populations of China and plotted Canonical correspondence analysis (CCA) reflecting the relationship between microflora and environmental factors

Table 6

Principal component and corresponding analysis scores and eigenvalue.
	Principle component analysis					Corresponding analysis
Scores	PC1	PC2	PC3	Eigenvalue	Variance	PC1	PC2	PC3	Eigenvalue	Variance
Sharkia	0.604	-0.515	0.607	34.6	2.79	-1.34	1.37	-1.6	40.1	18.0
Benisuef	0.648	-0.124	-0.75	7.5	3.27	1.34	0.31	1.9	8.7	4.33
Fayoum	0.462	0.847	0.259	4.9	0.595	-1.0	-2.6	-0.79	5.7	6.5

5-Population Polymorphism information content and FST values:

Genetic diversity data analyzed according to some gene structure parameters of within and among population variation as Na, Ne, (Fst values), (Nm), Gst and Dst, average distances He and Ho (Expected and observed heterozygosity) and fixation index F between individuals cluster recorded using structure software program, results found in table (8). Variation within and among populations recorded mean of Fst value was 0.674, 0.673 and 0.676 for Sharkia, Benisuef and Fayoum. When FST is between 0 to 0.05 it small, 0.05 to 0.15 moderate, 0.15 to 0.25 large and > 0.25 very large then large differentiation between the three P.Gossypiella population tested and support the fixation for alternate alleles in different subpopulations. The high amount of gene flow between populations Nm was (-0.474) for Benisuef and not different from other was 0.472, -0.462 for Sharkia and Fayoum and looks as similar in phenotypic traits and genetic similarity also represent near geographically and climate condition is the same. However, the random mating in subpopulations and gene diversities stand on observed and expected heterozygosity Ho and He under Hardy-Weinberg equilibrium averaged among sub populations gene diversity (Hs) and of the total genetic diversity in species population (Ht) attained in (Table 8). Gst is the mean of genetic diversity among population and represent the genetic differentiation = Dst/HT. Where D_st is the total genetic diversity distributed among populations or inter population diversity, D_st=Ht-Hs. Data found in table (8) explain G_st, D_st and where Hs is intra-within population genetic diversity and Ht is the total genetic diversity Ht = Hs + D_st. All items proved that samples near to each other and little differences are closely related. The highest level of genetic diversity was (Hs = 6.4, He = 0.75) for Benisuef. Moreover, the lowest level of genetic diversity was (Hs = 4.4, He = 0.75) was for Fayoum. The genetic parameters including allele’s frequency was 0.5 and showed observed number of alleles Na was 1.92, 2.11, 2.03 and number of effective allele was 1.524, 1.602, 1.929 and 1.778 for Sharkia, Benisuef and Fayoum respectively. Moreover, the genetic diversity Ht was 18.1, 19.6 and 13.6, for the same governorate respectively. Thus, the populations evaluated not differ in their phenotypic traits. The term heterozygosity (He) was represent 75% of the tested populations and the homozygosity was 25%. Data results of Shannon’s index (I) was 6.62, variance 3.37 and standard deviation was 1.84, and unbiased expected heterozygosity (uHe = (2N/(2N − 1)) × He) were estimated for each cluster, where N is group size. This results were similar to (Aguirre-Obando et al., 2015), said that genetic variability in natural populations of Aedes aegypti (Linnaeus) from Colombia indicated about 94% homozygous for the wild allele and 6% were heterozygous. In addition, quantifying genetic diversity must be based on rate of polymorphism (Pj), proportion of polymorphic loci, and allelic richness and average number of alleles per locus, effective number of alleles (Ne), average expected heterozygosity (He) of Nei’s formulas was effective ways (Cavalli–Sforza and Bodmer 1981). Also similar as (Velu et al. 2008), PCR-ISSR markers for genetic relationships among strains of Bombyx mori mutant of genetic stocks, found the average number of observed allele was 1.7080, effective alleles 1.5194 and genetic diversity (Ht) was 0.2901. In fact, geographical isolation, mutation and selection are the most likely forces of population genetic differentiation like that observed in natural populations of Drosophila (Frydenberg et al. 2003). And S. cynthia ricini silk moths discovered are poor flyers with short life span, hadn’t genetic mixing among geographically close populations as gene flow and prevents local adaptation that results from fixation of alleles favored by local climatic conditions (Vijayan, et al. 2006). Generally, gene flow generates new polymorphisms and gene combinations in which selection can act Khan et al., 2021.

Table 8

Polymorphism information content of the three populations of *P.gossypiella*.
Population	Ho	He	Hs	Ht	Na	Ne	DST	GST	F	Fis	Fst	NM
Calibrator	1.0	0.75	27.1	81.9	1.88	1.52	54.8	0.6691	-0.333	0.669	0.669	-0.493
Sharkia	1.0	0.76	5.9	18.1	1.929	1.60	12.2	0.6740	-0.316	0.674	0.674	-0.472
Benisuef	1.0	0.75	6.4	19.6	2.11	1.93	13.2	0.6735	-0.333	0.674	0.674	-0.474
Fayoum	1.0	0.76	4.4	13.6	2.03	1.78	9.2	0.6765	-0.317	0.677	0.677	-0.462
He = 1 − Σpi2, F = Fixation Index= (He − Ho)/He, Fst = (H T – HS)/ H T, Fis= (Mean He − Mean Ho)/Mean He), Nm = 1/Fst/4Fst

Table 9

Shannon diversity index analysis (SHE) results by past software.
Population	N	S	D	RA	ln N	ln S	H	ln E	LnE/Lns	Distance	Diversity	Eveness
Calibrator	109	109	0.009174	109	4.6913	4.6913	4.6913	0	0	0.096	1.105	1.01
Sharkia	133	115	0.04167	24	4.8903	4.7449	4.7027	-0.042	-0.0089	0.225	1.245	1.09
Benisuef	159	117	0.03846	26	5.0689	4.7622	4.6566	-0.105	-0.022	0.229	1.23	1.089
Fayoum	177	117	0.05556	18	5.1761	4.7622	4.6023	-0.159	-0.0336	0.381	1.28	1.079
N = species counts, S = Species richness, RA = Relative abundance, D = Simpson diversity index or dominance, H = Shannon and E = Evenness

6- Diversity monitoring:

In ecosystem ecology explorer, diversity is measured by two main components: species richness is the number of species present in the insect community, species evenness is the relative abundant of the number of the specie presents, and the species composition expressed as which particular species are present, Slade and Ong (2023). Biodiversity index is a quantitative measure that reflects how many different types of insect species there are in a dataset of a habitat populations, represented by (Richness, Evenness, and Dominance) that are useful for comparing data of different regions, and identifying locations with high native species abundant, where the larger the Shannon value, the higher the community diversity (Landmann etal., 2023). The larger the Simpson index value, the lower the population diversity (Simpson 1949). The species dominance (the opposite of diversity) in a community and low evenness values indicates that 1 or few species dominate the community (Leinster et al., 2012). Data of P. gossypiella species diversification were found in table (9). The value of evenness ranges from 0 to 1. Higher values indicate higher levels of evenness. In addition, species richness (S) values were looked similar to each other about 115–117 and 117 for Sharkia, Benisuef and Fayoum respectively. Diversity analysis utilization on any organism communities that being under stress or the unexposed, to identify the quantity of changes on population density or natural habitat effect on population structure, As found by Forister et al., 2019, Focused on establishing the breadth and depth of the phenomenon and on documenting factors causing insect declines.

The genetic differentiation using ISSR primers for amplification of some similar populations of P.gossypiella, appear to be effective molecular marker for this investigations. The variation of genetic relationships exhibited by (UPGMA,PCA), among the evaluated insecticide treatments and divergence between them are observed, where the low diversity refer to low genetic variation due to environmental stresses, and experimentally detected differ from wild genetic resources (control). The analysis of genetic diversity and structure of populations involves the quantification of diversity and the relationships within and between populations are noticeable. In addition to choosing the correct selection of distance or similarity calculation and clustering methods to assess relationships in the sample of interest are clearly suitable tools.

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No competing interests reported.

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Population variation and polymorphism of the Cotton Pink Bollworm Pectinophora gossypiella (Saunders) (Lepidoptera: Gelechiidae) using Inter Simple Sequence Repeats (ISSRs) as molecular markers

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Abstract

Figures

Introduction

Materials and methods

Insect sources and sampling

Molecular biology techniques:

1-Genomic DNA extraction, purification and quantification

2-Amplification of DNA using ISSR:

Data Analysis of the gel results:

Results and Discussions

1-Polymorphism quantification and fingerprinting by ISSR primers:

2-Marker efficiency analysis (MEA):

4-Inter population genetic diversity

5-Intra population genetic diversity:

4-Genetic relationship achieved by statistical analyses:

5-Population Polymorphism information content and FST values:

6- Diversity monitoring:

Conclusion

References

Additional Declarations

Supplementary Files

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