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Molecular Characterization and Identification of Three Stored Grain Pests Based on Mitochondrial Cytochrome C Oxidase Subunit I (coi) Gene Sequences

Abu Faiz Md. Aslam
Department of Zoology, Jahangirnagar University, Savar, Dhaka-1342, Bangladesh

Sharmin Sultana
Department of Zoology, Jahangirnagar University, Savar, Dhaka-1342, Bangladesh

Faria Farhana Rain
Department of Zoology, Jahangirnagar University, Savar, Dhaka-1342, Bangladesh

Susmita Sarker
Department of Zoology, Jahangirnagar University, Savar, Dhaka-1342, Bangladesh

Sumita Rani Das
Department of Zoology, Jahangirnagar University, Savar, Dhaka-1342, Bangladesh

Ayesha Siddika
Department of Zoology, Jahangirnagar University, Savar, Dhaka-1342, Bangladesh

Abdul Jabber Howlader
Department of Zoology, Jahangirnagar University, Savar, Dhaka-1342, Bangladesh

Abstract/ Executive Summary:

Stored grain pests are discovered in food as immature stages, which further complicates the identification process. A DNA barcode dataset of some important pests that can be used for easy and confirm identification in stages of life is constructed. COI genes of three stored grain insect pests i.e,, Sitophilus oryzae, Callosobruchus chinensis and Oryzaephilus surinamensis were sequenced. The sequenced genes were submitted to NCBI GenBank and obtained accession numbers MG967331.1, MG967332.1, MG967333.1 and MK041216.1. BLAST analysis showed 99 to 100% homology with existing GenBank sequences. The nucleotide composition analysis revealed that the value of A+T (64.8%) is greater than G+C (35.2%). Genetic distance among four sequences of three store pests were ranged from 0.00293-0.32807. Phylogenetic analysis showed that these three species are originated from different clades. Haplotype analysis of mitochondrial COI gene of the stored grain insect pests showed high genetic diversity among them. C. chinensis, O. surinamensis and S. oryzae were separated from their common ancestor by 80, 73 and 64 mutational steps. These information may be helpful for attempting any successful control measures against the pest species. In conclusion, present author established the first DNA barcode dataset of three store grain pests and confirmed its efficiency for identifying these pests.

Keywords: Molecular characterization, DNA barcode, Stored grain pest, Cytochrome oxidase, Phylogeny

Location: All over Bangladesh

Start Date:

End Date:

Program Area: Pest Management

Commodity: Insects, Pesticide

Objectives:

The main objectives behind this work are to associate morphological and molecular identification of the stored grain insect pests in order to find more strategies of managing these pests and for further research.

Methodology:

DNA isolation: Three important stored grain pests Sitophilus oryzae, Callosobruchus chinensis and Oryzaephilus surinamensis were collected from different infested store grains. The genomic DNA was extracted from somatic tissue rich in mitochondria (e.g., leg or wing) using Wizard® Genomic DNA Purification Kit, USA, following the manufacturer’s protocol. The remaining parts of insects and respective individuals were kept as voucher specimens. In short, separated tissue was ethanol sterilized and homogenized in 600 µl nuclei lysis solution. After adding 3 µl RNase and incubation in water bath for 15 minutes, 200 µl protein precipitation solution was added. DNA containing supernatant was separated following 1400 rpm centrifugation and added 600 µl isopropanol into it. The solution was centrifuged at high speed to get DNA pellet. DNA was washed by 600 µl of nuclease-free 70% ethanol. The DNA was then air-dried and rehydrated in 40 µl DNA rehydration solution by incubating at 65ºC for 1 hour. Processed DNA was stored at 4ºC or −20ºC. DNA quantification and quality measurement: The quantity and purity of DNA was measured by using Nano drop™ 2000 spectrophotometer (Thermo Fisher Scientific, USA). The ratio of the absorbence A260/A280 indicates the purity of PCR amplified DNA. For pure DNA, A260/A280 value is 1.8. Polymerase chain reaction (PCR): The extracts were subjected to PCR amplification of a 658 bp region near the 5′ terminus of the CO 1 gene following standard protocols. Primers used were forward primer: (LCO 1490 5′- GGTCAACAAATCATAAAGATATTG G-3′) and reverse primer: (HCO 2198 5′- TAAACTTCAGGGTGACCAAAAAATCA-3′). PCR reactions were carried out in 96- well plates with 20 µl reaction volume containing Promega Gotaq® G2 Green Master Mix - 10 μl, forward and reverse primers - 1 μl (10 pmol/μl), template DNA, and nuclease free water (adjustable). Thermocycling consisted of an initial denaturation of 94°C for 3 min, followed by 30 cycles of denaturation at 94°C for 30 sec, annealing at 49°C for 30 sec, extension at 72°C for 1 min, final extension: 72ºC for 10 min and hold: 4ºC. PCR was performed using a Veriti® Thermal Cycler from Thermo Fisher Scientific Thermal Cycler. Gel electrophoresis: The amplified product was analysed on a 1.5% agarose gel electrophoresis. The DNA was then visualized under gel documentation system - BioDoc Analyzer of Biometra. Sequencing: The PCR products were purified using Promega Wizard® SV Gel and PCR clean up system manufactured by Promega Corporation, USA following manufacturer's protocol. The quantity and purity of PCR purified products was checked by spectrophotometer. DNA sequencing was performed to determine the nucleotide sequence in cytochrome oxidase I region. BigDye® Terminator v3.1 cycle sequencing kit was used in this process. Each species was bi-directionally sequenced to get sequence of both (5’ and 3’) the DNA strands. Submission of gene to GenBank: Sequenced data were checked for quality by BioEdit v.7.0.5 software. Homology, insertions - deletions, stop codons, and framshifts was checked using NCBI BLAST. BankIt, a WWW-based submission tool with wizards to guide the submission process was used. The GenBank database was intended for new sequence data that was determined and annotated by the submitter. All sequences were uploaded to GenBank. Data analysis: The chromatograms were converted to FASTA format using FinchTV chromatogram viewer software. The DNA sequences in ABI file were manually edited using BioEdit v.7.0.5. Results of sequence editing were analyzed using BLAST (Basic local alignment search tool) NCBI to indicate the homology from closest species. Phylogenetic tree was constructed using maximum likelihood method, calculation using Bootstrap with 1000 times of repetition in Molecular evolutionary genetic analysis (MEGA) software program v.X (Kumar et al. 2018).

Source of Information: Bangladesh J. Zool. 47(1): 1-11, 2019 ISSN: 0304-9027 (print); 2408-8455 (online)

Article Link (Online Journal): DOI: https://doi.org/10.3329/bjz.v47i1.42016

Funding Source:

Budget:

Total Budget (Tk.) :

Achievement/Findings:

To confirm that the desired portion of COI gene has been amplified, gel electrophoresis was conducted. Thermo Fisher GeneRular 100 bp was used as ladder. The gel documentation image obtained by BioDoc Analyzer shows that all the samples selected for gel electrophoresis gave bands between 600 and 700bp of DNA ladder. It reveals that desired COI gene of mtDNA were properly polymerased. The visualized PCR product contained no double bands on agarose gel, thus indicating that sequences obtained were targeted mitochondrial DNA and not nuclear or mitochondrial mistargets. Over the last decade, the field of DNA barcoding has emerged as a molecular method for species identification. The goal of scientists who perform DNA barcoding is to create a library of every organism on earth (Stoeckle et al. 2004, Kerr et al. 2007). Although the major insect pests in food are widespread worldwide, only a few studies have been conducted on the DNA barcodes for these species (Seo et al. 2013). Therefore, this study is the first to attempt the construction of a DNA reference dataset using the mitochondrial COI gene from store food-associated insect species. This dataset can be effectively used to identify store food-associated insect pests that are currently important in commercial food markets. DNA barcoding can help in identifying pests in any stage of life making easier to control them saving farmers from cost of billion dollars from pest damage (Kaur 2015, Sarvananda 2018).

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