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BMC Research Notes

, 12:325 | Cite as

Transcriptome profiling of induced susceptibility effects on soybean–soybean aphid (Hemiptera: Aphididae) interaction

  • Surendra Neupane
  • Adam J. Varenhorst
  • Madhav P. NepalEmail author
Open Access
Data note

Abstract

Objectives

Soybean aphid (Aphis glycines Matsumura; SBA) is the most economically damaging insect of soybean (Glycine max) in the United States. One previous study demonstrated that avirulent (biotype 1) and virulent (biotype 2) biotypes could co-occur and interact on resistant (i.e., Rag1) and susceptible soybean resulting in induced susceptibility after 11 days of feeding. The main objective of this research was to employ RNA sequencing (RNA-seq) technique to compare the induced susceptibility effect of biotype 2 on susceptible and resistant soybean at day 1 and day 11 (i.e., both susceptible and resistant soybean were initially challenged by biotype 2 and the effect was monitored through biotype 1 populations).

Data description

We investigated susceptible and Rag1 transcriptome response to SBA feeding in soybean plants colonized by biotype 1 in the presence or absence of an inducer population (i.e., biotype 2). Ten RNA datasets are reported with 266,535,654 sequence reads (55.2 GB) obtained from pooled samples derived from the leaves collected at day 1 and day 11 post SBA infestation. A comprehensive understanding of these transcriptome data will enhance our understanding of interactions among soybean and two different biotypes of soybean aphids at the molecular level.

Keywords

Aphis glycines Transcriptome RNA-seq Soybean Induced susceptibility 

Abbreviations

SBA

soybean aphids

RNA-seq

RNA sequencing

CPM

counts per million

Rag

resistance to Aphis glycines

Objective

Soybean aphid (Aphis glycines Matsumura; SBA) is the most economically damaging insect pest of soybean (Glycine max) in the United States (US) [1]. In the US, it is estimated that annual economic losses due to the SBA are approximately $4 billion [2]. Although host plant resistance to SBA exists, farmers rely on broad-spectrum foliar insecticide applications to reduce SBA populations [3]. The dependency on the use of chemical management has resulted in pyrethroid resistance in SBA populations in Iowa, Minnesota, North Dakota and South Dakota as well as the effects on non-target beneficial organisms [4, 5]. Host resistance to SBA is not widely adopted, which may partially be due to the presence of four SBA biotypes (i.e., biotype 1: avirulent, biotype 2: virulent to Rag1, biotype 3: virulent to Rag2, biotype 4: virulent to Rag1, Rag2 and Rag1 + Rag2) in the US [6, 7, 8]. Initial observations of SBA on resistant soybean were attributed to the presence of virulent biotypes [6, 7, 8]. However, Varenhorst et al. [6] demonstrated that inducer populations of avirulent (biotype 1) or virulent (biotype 2) biotypes improved conditions for subsequent (i.e., response) populations of biotype 1 or biotype 2 SBA on resistant (i.e., Rag1) and susceptible soybean, which is defined as induced susceptibility [9]. Furthermore, the induced susceptibility effect could be further categorized as feeding facilitation [10] (i.e., conspecific inducer improves host for conspecific response population) and obviation of resistance [11] (i.e., virulent inducer improves host susceptibility for avirulent response population). While induced susceptibility effects indicate that not all SBA observed on the resistant hosts are necessarily virulent [9], the mechanism of the induced susceptibility effects is yet to be characterized. Therefore, the major objective of this study was to use RNA sequencing (RNA-seq) to characterize induced susceptibility in soybean when a biotype 2 inducer is present.

Data description

Plant material and aphid biotypes

The data in this submission came from a greenhouse experiment using two genotypes of soybean (susceptible cultivar LD12-1583R, and resistant cultivar LD12-15813Ra with Rag1 gene), and two SBA populations (biotype 1-avirulent and biotype 2-virulent [6]). A detailed overview of the experiment is provided in Supplementary file 1 and Figure S1 (Table 1).
Table 1

Overview of data files/data sets

Label

Name of data file/data set

File types (file extension)

Data repository and identifier (DOI or accession number)

Supplementary file 1

Methodology description

Word document (.dox)

 https://doi.org/10.6084/m9.figshare.7980176

Figure S1

A flow chart representing experimental methods

Image file (.tiff)

 https://doi.org/10.6084/m9.figshare.7980176

Figure S2

A flow chart showing the RNA-seq data analysis pipeline

Image file (.tiff)

 https://doi.org/10.6084/m9.figshare.7980176

Figure S3

The hierarchical clustering of top 3000 variable genes

Image file (.tiff)

 https://doi.org/10.6084/m9.figshare.7980176

Figure S4

The correlation between the samples using the top 75% genes

Image file (.tiff)

 https://doi.org/10.6084/m9.figshare.7980176

Figure S5

Quality metrics of G. max sequencing data. (a) Mean quality scores per position. (b) Per sequence quality scores. (c) GC content distribution. (d) Read length distribution

Image file (.tiff)

 https://doi.org/10.6084/m9.figshare.7980176

Data file 1

Control: No aphids; Susceptible soybean; Day 1; SRR8848027

fastq file (.fastq)

https://identifiers.org/ncbi/insdc.sra:SRR8848027

Data file 2

Control: No aphids; Susceptible soybean; Day 11; SRR8848028

fastq file (.fastq)

https://identifiers.org/ncbi/insdc.sra:SRR8848028

Data file 3

Control: No aphids; Resistant soybean; Day 1; SRR8848025

fastq file (.fastq)

https://identifiers.org/ncbi/insdc.sra:SRR8848025

Data file 4

Control: No aphids; Resistant soybean; Day 11; SRR8848026

fastq file (.fastq)

https://identifiers.org/ncbi/insdc.sra:SRR8848026

Data file 5

Inducer: None; Response: 15 biotype 1; Susceptible soybean; Day 11; SRR8848031

fastq file (.fastq)

https://identifiers.org/ncbi/insdc.sra:SRR8848031

Data file 6

Inducer: 50 biotype 2; Response:15 biotype 1; Susceptible soybean; Day 1;SRR8848032

fastq file (.fastq)

https://identifiers.org/ncbi/insdc.sra:SRR8848032

Data file 7

Inducer: 50 biotype 2; Response: 15 biotype1; Susceptible soybean; Day 11; SRR8848029

fastq file (.fastq)

https://identifiers.org/ncbi/insdc.sra:SRR8848029

Data file 8

Inducer: None; Response: 15 biotype 1; Resistant soybean; Day 11; SRR8848030

fastq file (.fastq)

https://identifiers.org/ncbi/insdc.sra:SRR8848030

Data file 9

Inducer: 50 biotype 2; Response: 15 biotype 1; Resistant soybean; Day 1; SRR8848023

fastq file (.fastq)

https://identifiers.org/ncbi/insdc.sra:SRR8848023

Data file 10

Inducer: 50 biotype 2; Response: 15 biotype 1; Resistant soybean; Day 11; SRR8848024

fastq file (.fastq)

https://identifiers.org/ncbi/insdc.sra:SRR8848024

Data file 11

Control: No aphids; Susceptible soybean; Day 1; GSM3717543

txt (.txt.gz)

http://identifiers.org/geo:GSM3717543

Data file 12

Control: No aphids; Susceptible soybean; Day 11; GSM3717544

txt (.txt.gz)

http://identifiers.org/geo:GSM3717544

Data file 13

Control: No aphids; Resistant soybean; Day 1; GSM3717545

txt (.txt.gz)

http://identifiers.org/geo:GSM3717545

Data file 14

Control: No aphids; Resistant soybean; Day 11; GSM3717546

txt (.txt.gz)

http://identifiers.org/geo:GSM3717546

Data file 15

Inducer: None; Response: 15 biotype 1; Susceptible soybean; Day 11; GSM3717547

txt (.txt.gz)

http://identifiers.org/geo:GSM3717547

Data file 16

Inducer: 50 biotype 2; Response: 15 biotype 1; Susceptible soybean; Day 1; GSM3717548

txt (.txt.gz)

http://identifiers.org/geo:GSM3717548

Data file 17

Inducer: 50 biotype 2; Response: 15 biotype1; Susceptible soybean; Day 11; GSM3717549

txt (.txt.gz)

http://identifiers.org/geo:GSM3717549

Data file 18

Inducer: None; Response: 15 biotype 1; Resistant soybean; Day 11; GSM3717550

txt (.txt.gz)

http://identifiers.org/geo:GSM3717550

Data file 19

Inducer: 50 biotype 2; Response: 15 biotype 1; Resistant soybean; Day 1; GSM3717551

txt (.txt.gz)

http://identifiers.org/geo:GSM3717551

Data file 20

Inducer: 50 biotype 2; Response: 15 biotype 1; Resistant soybean; Day 11; GSM3717552

txt (.txt.gz)

http://identifiers.org/geo:GSM3717552

Data file 21

The transformed transcript abundance counts for all the samples

Spreadsheet (.xlsx)

 https://doi.org/10.6084/m9.figshare.7980176

Data file 22

The hierarchical clustering of top 3000 variable genes

Spreadsheet (.xlsx)

 https://doi.org/10.6084/m9.figshare.7980176

Table S1

Statistics of the transcriptomic data using RNA-seq pipeline used in this study

Word document (.dox)

 https://doi.org/10.6084/m9.figshare.7980176

The supplementary materials (Supplementary file 1, Figure S1–S5, Data file 21, Data file 22, and Table S1) can be assessed openly on Figshare [19]. The raw RNA-seq data (.fastq files) are available for download on the SRA [20] and the raw transcript abundance counts (.txt.gz) are available on Gene Expression Omnibus (GEO) [21]

RNA extraction, library preparation, and sequencing

Leaf samples collected at day 1 and day 11 from resistant and susceptible cultivars (non-infested, infested with inducer biotype 2: response biotype 1) were used to isolate RNA using PureLink RNA mini kit (Invitrogen, USA). Isolated RNA was treated with TURBO™ DNase (Invitrogen, USA) to remove any DNA contamination, following the manufacturer’s instructions. The RNA samples from three replicates were pooled in equimolar concentration, and RNA-seq libraries were sequenced on an Illumina NextSeq 500 at 75 cycles. Ten RNA libraries were prepared and sequenced with the sequencing depth ranging from 24,779,816 to 29,72,4913 reads (Data files 1–10; Table 1; Table S1).

Quality control assessment

Quality control of reads was assessed using FastQC program (version 0.11.3) [12]. The FastQC results were visualized using MultiQC v1.3 [13]. Low quality bases (QC value < 20) and adapters were removed by trimming using the program Trimmomatic (version 0.36) [14]. The coding sequences (Gmax: Gmax_275_Wm82.a2.v1.transcript_primaryTranscriptOnly.fa.gz) were obtained from the Phytozome database and aligned using Salmon ver.0.9.1 [15] accessed from Bioconda [16] (Data files 11–20). A flow chart showing the RNA-seq data analysis pipeline is shown in Figure S2. The downstream analyses were conducted using iDEP 0.82 [17]. Read quants were filtered with 0.5 counts per million (CPM) in at least one sample. Quantified raw reads were transformed using regularized log (rlog), which is implemented in the DESeq 2 package [18] (Data file 21). The transformed data were subjected to exploratory data analysis such as hierarchical clustering (Figure S3; Data file 22) and the correlation between samples (Figure S4).

Statistics of transcriptome data

The FastQC analysis showed Phred quality scores per base for all samples higher than 30, and GC content ranged from 45 to 46% with a normal distribution (Figure S5, Table S1). After trimming, over 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. After filtering with 0.5 counts per million (CPM) in at least one sample and rlog transformation, a total of 37,468 genes (66.9% of original 55,983) were retained for transformation (Data file 21). The hierarchical clustering based on 3000 most variable genes, sample distances (Figure S3; Data file 22) indicated that sample clustering followed the time points of sample collection (i.e., Day 1 and Day 11). The correlation between the samples using the top 75% of genes showed in a range of 0.96–1 (Figure S4).

Limitations

The quality filtering of downloadable raw fastq files is recommended before use. Kal’s z-test [22] integrated with CLC Genomics Workbench (https://www.qiagenbioinformatics.com/) and analysis guided by the reference genes could be used to study the differential gene expression for pooled samples with no replications.

Notes

Acknowledgements

We thank Philip Rozeboom for his assistance in greenhouse experiments.

Authors’ contributions

SN carried out the experiments. AJV collected the plant and aphid biotypes. AJV and MPN conceived the project and contributed designing the experiments. SN analyzed the data. All authors contributed writing this manuscript. All authors read and approved the final manuscript.

Funding

Funding for the greenhouse experiments and RNA sequencing came partly from South Dakota Soybean Research and Promotion Council (SDSRPC-SA1800238), and partly from the United States Department of Agriculture hatch projects (SD00H469-13 and SD00H659-18) to M.P.N. SN received graduate teaching/research assistantships from the Department of Biology & Microbiology to support his PhD dissertation work (data analysis, interpretation and writing of the manuscript) at South Dakota State University.

Ethics approval and consent to participate

Not applicable.

Consent to publish

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Copyright information

© The Author(s) 2019

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors and Affiliations

  1. 1.Department of Biology and MicrobiologySouth Dakota State UniversityBrookingsUSA
  2. 2.Department of Agronomy, Horticulture and Plant ScienceSouth Dakota State UniversityBrookingsUSA

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