Indian Journal of Entomology, Review Article (2020) DoI No.:

GENETIC VARIABILITY IN PREDATORY STINK BUG ANDRALLUS SPINIDENS (F.) FROM NORTH-WEST HIMALAYA

Bhojendra*, ravi Prakash Maurya, Latika BrijwaL, hiManshu PatwaL and ParuL suyaL

Department of Entomology, College of Agriculture, G.B. Pant University of Agriculture and Technology,

Pantnagar-263145, U.S. Nagar, Uttarakhand *Email: patelbhojendra95@gmail.com (corresponding author)

ABSTRACT

Present study deliberates on the geographical distribution and genetic variability of the predatory stink bug Andrallus spinidens (F.) from different crop ecosystems of north west Himalaya. Andrallus spinidens is an important biological control agent as it predates on lepidopteran larvae. Samples of this bug were collected from five locations of plains and hilly areas of Uttarakhand and subjected to RAPD-PCR analysis. The results revealed that of the 12 RAPD primers evaluated 10 primers generated 117 loci with 0.26 polymorphism information content (PIC), 2.75 marker index (MI), 4.67 resolving power (RP). RAPD primer, EF4 was found to be efficient in deciphering the variability with highest RP values of 7.71 with 4.37 MI and 0.39 PIC values. The samples were clustered locationwise with 0.26-0.95 similarity coefficient during cluster analysis using UPGMA method. The pairwise comparison analysis (PCA) also supported the clustering pattern obtained. The results reveal that genetically distant populations of A. spinidens prevail in all the five locations studied.

Key words: Andrallus spinidens, Himalaya, distribution, genetic variability, polymorphism, RAPD-PCR, primers, resolving power, cluster analysis

E:Review article-- 20024-Bhojendra

The Asopinae bug Andrallus spinidens F. (Hemiptera: Pentatomidae) is a generalist predator with biocontrol potential due to its wide host range and unique mode (extra oral digestion) of predation (Ho et al., 2003; Grazia et al., 2015). It is widely distributed (Distant, 1902; Thomas, 1994) and associated with agricultural environment feeding on major pests viz: Helicoverpa armigera, Chilo suppressalis, Rivulia sp., Naranga aenescens, Spodoptera litura and S.frugiperda (Rajendra and Patel, 1971; Rao and Rao, 1979; Singh and Singh, 1987; Mohaghegh and Massod, 2007; Claver and Jaiswal, 2013, Shylesha and Sravika, 2018). In India A. spinidens had been reported from Assam, Sikkim, Jharkhand, Meghalaya, West Bengal and Karnataka (Distant, 1902; 1908). In Uttarakhand, it is known as a predator of Zygograma bicolorata (Pandey et al., 2002) and S. litura (Maurya and Sharma, 2014).

The north west Himalayan region of Uttarakhand (28º43’-31º27’N, 77º 34’-81º02’E) (Anonymous, 2102-13) is rich in diversity and geographical features viz: high hills, mid hills, foot hills, valleys and Tarai region (Samant et al., 2007). Environmental condition having complex interaction with insect population and their morphology, reproduction, predatory efficiency, mortality, prey selection and longevity will also

affected with reffrence to their distribution or habitat geographical location (Sokolowski, 2001). In addition, A. spinidens can survive at various altitudes (Bhojendra et al., 2019) but the effect of climatic conditions on their genetic variability has not been not explored. The diversity of any population depends on its genotypic and environmental flexibility (Tauber et al., 1986). RAPD (Random amplified polymorphic DNA) marker is an efficient device to differentiate isolated population genetically (Fuchs et al., 1998; Pavlovcic et al., 2008). This study evaluates the effect of geographical locations on genetic variation of A. spinidens, so that a suitable strain for a particular geographical area can be identified for its utilization in biocontrol.

MATERIALS AND METHODS

The samples of A. spinidens were collected from various locations viz., plain/ tarai area (Pantnagar: 235 masl, Tanakpur: 255 masl, Ramnagar: 367 masl) and hilly area (Majhera: 922 masl, Almora: 1212 masl) in Kumaon region of Uttarakhand in northeest Himalaya during 2017-2018. From each location, three crop fields were surveyed and samples collected through hand picking and stored at -200C in 80% ethanol (Ellango et al., 2015). From these thorax muscle was randomly

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2 Indian Journal of Entomology, Review Article 2020

extracted for DNA isolation to avoid cross contamination (n=7) (Gracia et al., 1998). Genomic DNA was extracted with HipurATM Insect DNA Purification Kit (Himedia) from 50 mg samples (Sambrook, et al., 2001).

DNA amplification was performed with sets of 12 arbitrary deca-nucleotide RAPD primers (Kavar et al., 2006) in PCR thermo-cycler (Wee 32 Himedia). The RAPD primers were synthesized by Chromous Biotech Bangalore. For amplification 15 µl reaction mixture prepared by mixing of diluted DNA sample (2µl), 10Mm primer (1.2µl), 10X PCR buffer (1.5µl), 10Mm dNTP mix (0.9 µl), 5U/µl Taq DNA polymerase (0.3µl) and molecular biology grade water (9.1µl) following cycling protocol: Initial denaturation at 94°C for 5 min followed by 40 cycles of denaturation at 94°C for 45 sec, annealing at 30°C for 30 sec and extension at 72°C for 2 min. Final extension was carried out at 72°C for 5 minu (Williams et al., 1990; Tan et al., 2007). Amplified DNA was subjected to electrophoresis on 2.5% agarose gel at constant voltage of 70V with TAE (50X) buffer. DNA bands were visualized on UV gel documentation system and captured (Alpha innotech Alpha imager EC).

The DNA polymorphism was calculated by analysing the polymorphic and monomorphic bands resolved on gel image. Scoring of banding pattern was done by credited in binary medium where ‘1’ for presence and ‘0’ for absence of bands of different molecular weights were scored. Genetic distance based on Jaccard Coefficient (Jaccard, 1908) was calculated using NTSYSpc 2.11a software package (Rohlf, 2002). The distance coefficient obtained were used to construct the dendrogram using UPGMA (The unweighted pair group method with arithmetic average) employing the SHAN clustering algorithm in same software package. Principal component analysis (PCA) was used for clustering the populations in bidimentional scatter plot through R software package (Jombart, 2008; Lopes et al., 2017) and % Polymorphism (no of polymorphic loci/Total number of loci) computed. Polymorphism information content (PIC) was assessed according to Rodan-Ruiz et al. (2000) to detect ability of primer to establish polymorphism in the population depending on the number of alleles detected and on their distribution frequency: ,Where, PIC = Polymorphism information content of primer, PICi = polymorphism content of ith loci and, , where, Pi = Frequency of ith allele. Effective multiplex ratio (EMR) and Marker index (MI) were calculated according to Powell et al. (1996) in order to estimate the overall effectiveness of marker system, EMR= {np(np/n)},where, EMR = Effective multiplex

ratio, np = no. of polymorphic bands, n = no. of bands and MI=PICEMR. Resolution power (Rp) was assessed according to Prevost and Wilkinson (1999) to characterize the ability of primer combination to detect the difference between samples. Where, Bli = Band informativeness of ith loci, and where, Pi = Frequency of the ith allele.

RESULTS AND DISCUSSION

In the present study 35 samples of A. spinidens from five different locations of were analysed. Out of 12 RAPD primers, 10 primers were able to generate scorable banding pattern. These primers amplified numbers of separate loci of varying intensity with varying amplicon patterns, and the amplicons/ primer was found to be 130. The size and number of monomorphic and polymorphic loci varied- of 117 loci, 105 were polymorphic with 74.93% polymorphism, while, 12 loci were monomorphic with 8.4% monomorphism. The maximum 14 and minimum 10 loci amplified by EF10 and EF1, respectively. 100% polymorphism was detected with primer EF2 and EF12, while minimum 78.57% was with EF10. Polymorphism information contain (PIC) value depends on the frequency of loci was found to be 0.26 with maximum 0.39 in EF4 and EF12 and minimum 0.31 in EF2, EF10 and EF11 primers. Effective Multiplex ratio (EMR) of any primer depends upon the number and types of loci that ranged from 6.4 to 11.07 with 7.83 in 12 primers. Marker index depends upon PIC and EMR of particular primer (2.15 to 4.37, mean 2.75); and resolving power (RP) was from 3.77 to 7.71 (mean 4.67). The MI was found to be maximum of 4.37 with EF4 with a maximum RP of 7.71 (Table 1).

Scoring of 130.0 amplicons from 35 individuals generated a unique RAPD profile in the dendrogram (Fig. 1); genetic distance was 0.26, all individuals grouped in Cluster C as a main one subdivided into C1 and C2- C1 with those from the plain (Tarai) region consisting of Pantnagar (P1-7), Tanakpur (T1-7) and Ramnagar (R1-7) with 43% genetic similarity. The subfragments of C1are given in Fig. 1. On the other hand C2 consisting of the ones from the hilly areas revealed 40% similarity within individuals of Majhera (M1-7) and Almora (A1-7), with further subgroupings. Thus distinctive grouping was observed between hills and plain locations. The pair wise comparison among all 35 individuals through R software gave a scatter plot as given in Fig. 2. Like UPGMA, PCA separated Majhera (M1-7) and Almora (A1-7) into 2 individual

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Genetic variability in predatory stink bug Andrallus spinidens (f.) from north-west Himalaya 3 Bhojendra et al.

S.

No.

Prim

erN

o. o

f am

plic

ons

To

ta

l am

plic

onR

ange

of

alle

leN

o. o

f lo

ciN

PLN

ML

% P

% M

PIC

EMR

MI

RP

PR

TM

A1

EF1

GG

AC

CC

AA

CC

3440

3430

2115

910

0-60

010

82

80.0

020

.00

0.34

6.4

2.15

4.7

2EF

2 G

TCG

CC

GTC

A16

1628

310

9130

0-11

0011

110

100.

000

.00

0.31

113.

364.

973

EF3

TTG

GC

AC

GG

G0

00

00

0-

00

00.

00.

00.

00.

00.

00.

04

EF4

CA

CC

GA

CA

AG

3537

4828

4919

710

0-12

5013

121

92.3

007

.70

0.39

11.0

74.

377.

715

EF5

TC

TCC

GC

CC

T27

3338

1930

146

100-

1100

1110

190

.90

09.1

00.

349.

093.

105.

376

EF6

GA

GC

CC

TCC

A30

3229

2534

150

100-

1000

1110

190

.90

09.1

00.

369.

093.

255.

887

EF7

GC

AC

TCTG

CC

00

00

00

-0

00

0.0

0.0

0.0

0.0

0.0

008

EF8

GG

CTA

AC

CG

A36

3547

6872

258

200-

1200

1210

283

.33

16.6

70.

388.

333.

157.

379

EF9

AC

CC

CG

CC

AA

2521

2831

3914

420

0-12

5013

121

92.3

007

.70

0.35

11.0

73.

915.

2510

EF10

AG

GC

CC

GAT

G28

4114

4551

179

150-

1500

1411

378

.57

21.4

30.

318.

642.

683.

7711

EF11

CTG

GG

CA

CG

A8

2642

265

107

250-

1600

1110

190

.90

09.1

00.

319.

092.

814.

5712

EF12

GG

AG

CC

TCA

G14

1914

3349

129

150-

800

1111

010

0.0

00.0

00.

3911

4.26

6.57

Tota

l15

6011

710

512

899.

210

0.8

3.17

94.7

833

.04

56.1

Aver

age

130.

09.

578.

751.

074

.93

8.4

0.26

7.83

2.75

4.67

Tabl

e 1.

Gen

etic

var

iabi

lity

of A

. spi

nide

ns

*NPL

=No.

of p

olym

orph

ic lo

ci, N

ML=

No.

of m

onom

orph

ic lo

ci, %

P= %

pol

ymor

phis

m, %

M=%

mon

omor

phis

m, P

IC=P

olym

orph

ism

info

rmat

ion

cont

ent,

EMR

= Ef

fect

ive

mul

tiple

x ra

tio, M

I=M

arke

r ind

ex, R

p=R

esol

utio

n po

wer

.

plots and rests other individuals from plain areas viz., Pantnagar, Ramnagar, Tanakpur sharing same plot but not overlapping in the form of mixed plot area that represented the changes in their DNA banding pattern according their geographical location. The Eigen value generated by PCA in first dimension was 22.3%, while in second dimension, it was 17.2% and the negative Eigen value represented reversed direction.

Among the 12 primers used for RAPD analysis, 10 primers generated a numbers of discrete bands of different intensity, where primer EF4 and EF12 had same 0.39 PIC. Primer EF12 showed none monomorphic band with 100% polymorphism, while, primer EF4 had one monomorphic band with 92.30% polymorphism. Monomorphic band supports primer EF4 to a potential to serve as genetic marker because if no monomorphic bands were found then the particular population would be considered as distinct species (EI-Bassiony and Abu El-Ghiet, 2014). Therefore, primer EF4 was found to be as efficient marker.

Cluster analysis through dendrogram revealed that the 35 individuals fell in two distinct groups viz., plain area C1 (Pantnagar C1Aa:235mt/771ft; TanakpurC1Ab; Ramnagar C1B:367mt/1204ft) and hilly area C2 (Almora C2A: 1212mt/3976ft; Majhera C2B:922mt/3026ft). Similar type of grouping was found during PCA (Pair wise comparison) analysis used for clustering the population in bidimentional scatter plot (Lopes et al., 2017). Bhatnagar et al. (2019) studied the predatory stink bug, Eocanthacona furcellata and found that population from high hills was distinct at gene level but not in biological characteristics. Similar patteren of differeation were obtained from RAPD anlysis during preliminary studies in case of Cochliomyia hominivorax; Diaprepes abbreviates L; H. armigera Hubner; neotropical brown stink bug, Euschistus heros; Nezara virudula (Infante-Malachias, 1999; BAS et al., 2000; Zhou et al., 2000; Sosa-Gómez et al., 2001; Kavar et al., 2006). Present study also suggests that the RAPD-PCR technique is an efficient tool to understand the genetic variability, and the populations of A. spinidens from locations with altitudenal varitions might affect their DNA banding pattern.

ACKNOWLEDGEMENTS

The authors acknowledge the Science & Engineering Research Board, New Delhi for financial assistance and Department of Entomology and Department of Genetics and Plant Breeding, G.B. Pant University of Agriculture and Technology, Pantnagar for providing facilities.

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Genetic variability in predatory stink bug Andrallus spinidens (f.) from north-west Himalaya 4 Bhojendra et al.

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(Manuscript Received: January, 2020; Revised: April, 2020; Accepted: April, 2020; Online Published: April, 2020)

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