Leguminivora glycinivorella cultivar:soybean pod borer
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA397954 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR5985984_1.fastq.gz | Fastqsanger.gz | |||
| SRR5985984_2.fastq.gz | Fastqsanger.gz | |||
| SRR5985985_1.fastq.gz | Fastqsanger.gz | |||
| SRR5985985_2.fastq.gz | Fastqsanger.gz | |||
| SRR5985986_1.fastq.gz | Fastqsanger.gz |
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Project description:Leguminivora glycinivorella overview
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Project description:Purpose: Soybean aphid (Aphis glycines Matsumura; SBA) is major pest of soybean (Glycine max) in the United States of America. One previous study on soybean, soybean-aphid interactions showed that avirulent (biotype 1) and virulent (biotype 2) biotypes can co-occur and potentially interact on resistant and susceptible soybean resulting induced susceptibility. The main objective of this research was to employ RNA sequencing technique to characterize the induced susceptibility effect in which initial feeding by virulent aphids can increase the suitability of avirulent aphids in both susceptible and resistant cultivars. Methods: The data in this submission come from the green house experiment using two genotypes of soybean: susceptible soybean cultivar was LD12-15838R and the resistant cultivar was LD12-15813Ra (with Rag1 gene) and two aphid populations: biotype 1 (avirulent) and biotype 2 (virulent biotype 2). RNA was extracted from the leave samples from resistant and susceptible cultivars treated with no aphids, biotype 2: biotype1 collected at day 1 and no aphids, biotype 2: biotype1 and no aphids: biotype1 at day 11 using PureLink RNA mini kit (Invitrogen, USA). RNA samples were treated with TURBOTM DNase (Invitrogen, USA) to remove any DNA contamination following the manufacturer’s instructions. Assessment of the isolated RNA integrity was performed by 1% agarose gel electrophoresis, and RNA concentration was measured by Nanodrop 2000 (Thermo Fisher Scientific, USA). Three replicates from these treatments in resistant and susceptible cultivars were pooled in equimolar concentration. RNAseq library construction was prepared using Illumina’s TruSeq Stranded mRNA Kit v1 (San Diego, CA). The libraries were quantified by QuBit dsDNA HS Assay (Life Technologies, Carlsbad, CA) and pooled in equimolar concentrations. The libraries were sequenced on an Illumina NextSeq 500 using a NextSeq 500/550 High Output Reagent Cartridge v2 (San Diego, CA) at 75 cycles. Results: A total of 10 RNA libraries were prepared and sequenced with the sequencing depth ranging from 24,779,816 to 29,72,4913. Total reads of 266,535,654 were subjected to FastQC analysis to determine the data quality using various quality metrics such as mean quality scores, per sequence quality scores, per sequence GC content, and sequence length distribution. The phred quality scores per base for all the samples were higher than 30. The GC content ranged from 45 to 46% and followed the normal distribution. After trimming, more than 99% of the reads were retained as the clean and good quality reads. Upon mapping these reads, we obtained high mapping rate ranging from 90.4% to 92.9%. Among the mapped reads, 85.8% to 91.9% reads were uniquely mapped. Conclusions: The objective of this study is to characterize the mechanism of induced susceptibility in soybean via transcriptional response study of soybean in presence of biotype 1 and biotype 2 soybean aphids using RNA-Seq. The data resulted from this study might provide insights into the interactions between soybean and soybean aphids and identify genes, their regulation and enriched pathways that may be associated with resistance or susceptibility to A. glycines.
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Project description:Purpose: Soybean aphid, Aphis glycines Matsumura (Hemiptera: Aphididae) and soybean cyst nematode, Heterodera glycines Ichinohe, (SCN) are the two most economically important pests of soybean, Glycine max (L.) Merr., in the Midwest. Although the soybean aphid is an aboveground pest and SCN is a belowground pest there is evidence that concomitant infestations result in improved SCN reproduction. This study is aimed to characterize the three-way interactions among soybean, soybean aphid and SCN using demographic and genetic datasets. Results: More than 1.1 billion reads (61.4 GB) of transcriptomic data were yielded from 47 samples derived from the experiment using whole roots of G. max. The phred quality scores per base for all the samples were higher than 30. The GC content ranged from 43 to 45% and followed the normal distribution. After trimming, more than 99% of the reads were retained as the clean and good quality reads. Upon mapping these reads, we obtained high mapping rate ranging from 73.8% to 94.3%. Among the mapped reads, 67.1% to 87.6% reads were uniquely mapped. Conclusions: The comprehensive understanding of these transcriptome data would help in understanding the molecular interactions among soybean, A. glycines, and H. glycines. The use of multifaceted bioinformatics approaches could facilitate finding candidate genes and their function that might play a crucial role in various pathways for host resistance against both soybean aphids and SCN. For differential gene expression analysis, EdgeR, limma, and DEseq2 could be used. Apart from standalone tools like iDEP, Galaxy (https://usegalaxy.org), CyVerse (http://www.cyverse.org), and MeV (http://mev.tm4.org) could also be used for both analysis and visualization of RNA- seq data.
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