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ABSTRACT:
PROVIDER: PRJNA1172591 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:The English grain aphid, Sitobion avenae, is a major agricultural pest of wheat, barley and oats, and is a major vector of Barley Yellow Dwarf Virus (BYDV) leading to reductions in grain yield. RNA-seq data from a genotype (SA3) was generated from heads and bodies, and from winged and unwinged aphids. The primary goal was to generate evidence for genome annotation, and the secondary goal was to compare expression of genes between head and body, and also between winged and unwinged aphids.
2021-12-01 | E-MTAB-10540 | biostudies-arrayexpress
Project description:The soybean aphid, a plant sap sucking insect, is an important soybean pest in the USA causing significant yield losses. The Rag2 gene of soybean provides resistance to soybean aphid biotypes I and II. Transcriptomic analyses were performed on near isogenic lines (NILs) with the Rag2 allele for aphid resistance or rag2 for susceptibility at the Rag2 locus. Soybeans were infested with soybean aphids and leaves were collected at 0, 4, 8, 24, and 48 hours after infestation. RNA were extracted and a high throughput RNA-seq approach was used to examine mRNA expression in Rag2 and rag2 soybean leaves. The expression of ~43,000 genes was detected in both the Rag2 and rag2 leaves. Statistical analysis identified 2361 genes significantly regulated between the resistant and susceptible lines at different times after aphid infestation. Genes found up-regulated in the Rag2 line were annotated as involved in the cell wall, secondary and hormone metabolism, as well as in stress, signaling and transcriptional responses. Genes found up-regulated in the rag2 line were annotated as involved in photosynthesis and carbon metabolism. Interestingly, mRNAs of 2 genes (unknown and mitochondrial protease) located within the Rag2 locus were expressed significantly higher in the resistant genotype. The expression of the putative NBS-LRR resistant gene present in the Rag2 locus was not different between the two soybean lines. However, another NBL-LRR gene located just at the border of the Rag2 locus was and, therefore, may be involved in the differential resistance to aphid infestation exhibited by the two NIL genotypes analyzed.
2016-04-08 | GSE67779 | GEO
Project description:Soybean aphids are phloem-feeding pests that can cause significant yield losses in soybean plants. Soybean aphids thrive on susceptible soybean lines but not on resistant lines. Aphids do not normally kill their host and colonize plants for long periods of time, up to several months in soybean. However, our knowledge of plant responses to long-term aphid colonization is very limited. We used microarrays to characterize the soybean plant's transcriptional response against aphids in two related cultivars, a susceptible line and a resistant line with the Rag1 aphid-resistance gene. We measured transcript levels in leaves after 21 days of aphid infestation.
2018-06-15 | GSE115790 | GEO
Project description:Soybean aphids are phloem-feeding pests that can cause significant yield losses in soybean plants. Soybean aphids thrive on susceptible soybean lines but not on resistant lines. We used microarrays to characterize the soybean plant's transcriptional defense against aphids in two related cultivars, a susceptible line and a resistant line with the Rag1 aphid-resistance gene. We measured trancript levels in leaves after one and seven days of aphid infestation.
2012-01-31 | GSE35427 | GEO
Project description:Soybean aphid is one of the major limiting factors for soybean production. However, the mechanism for aphid resistance in soybean is remain enigmatic, very little information is available about the different mechanisms between antibiosis and antixenosis genotypes. Here we dissected aphid infestation into three stages and used genome-wide gene expression profiling to investigate the underlying aphid-plant interaction mechanisms. Approximately 990 million raw reads in total were obtained, the high expression correlation in each genotype between infestation and non-infestation indicated that the response to aphid was controlled by a small subset of important genes. Moreover, plant response to aphid infestation was more rapid in resistant genotypes. Among the differentially expressed genes (DEGs), a total of 901 transcription factor (TF) genes categorized to 40 families were identified with distinct expression patterns, of which AP2/ERF, MYB and WRKY families were proposed to playing dominated roles. Gene expression profiling demonstrated that these genes had either similar or distinct expression patterns in genotypes. Besides, JA-responsive pathway was domination in aphid-soybean interaction compared to SA pathway, which was not involved plant response to aphid in susceptible and antixenotic genotypes but played an important role in antibiosis one. Throughout, callose were deposited in all genotypes but it was more rapidly and efficiently in antibiotic one. However, reactive oxygen species were not involved in response to aphid attack in resistant genotypes during aphid infestation. Our study helps uncover important genes associated with aphid-attack response in antibiosis and antixenotic genotypes of soybean.
2020-08-26 | GSE141720 | GEO
Project description:Whole genome re-sequencing of winged and wingless males of pea aphid clone P123
| PRJNA385573 | ENA
Project description:Soybean aphids are phloem-feeding pests that can cause significant yield losses in soybean plants. Soybean aphids thrive on susceptible soybean lines but not on resistant lines. We used microarrays to characterize the soybean plant's transcriptional defense against aphids in two related cultivars, a susceptible line and a resistant line with the Rag1 aphid-resistance gene. We measured trancript levels in leaves after one and seven days of aphid infestation. This was a full-factorial experiment with three factors: soybean variety (susceptible SD01-76R,resistant LD05-16060), aphid treatment (control, aphids), and infestation duration (1 day, 7 days). There were three replicates per treatment, for a total of 24 samples. The experiment was carried out in a growth chamber. At the V3 growth stage, thirty aphids were added to the third trifoliate leaves of the aphid-treated plants. Each plant had a net to prevent aphid movement among different plants. The aphids were removed prior to sampling.
2012-01-31 | E-GEOD-35427 | biostudies-arrayexpress
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.
2019-05-18 | GSE125103 | GEO