Aphis glycines isolate:Biotype 1
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA336353 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Gene expression profiles in resistant (cv. Dowling) and susceptible (Williams 82) soybean genotypes [Glycine max (L.) Merrill] were compared at 6 and 12 h with and without aphid (Aphis glycines Matsumura) infestation using cDNA microarrays consisting of approximately 18,000 soybean-expressed sequence tags. More genes were induced in Dowling than Williams 82 at 6 h after infestation. Genes that were differentially expressed between aphid and control treatments were selected as aphid-response genes. Eighty-four genes showed specific responses in Dowling and included genes related to defense and other processes. Expression of three defense-related genes was examined at 6, 12, 24, 48, and 72 h after infestation in both genotypes by quantitative real-time PCR. The increases in the transcripts of three defense-related genes were earlier and stronger at 6, 12 and 24 h after infestation in Dowling compared to Williams 82. The differential gene expression between the two genotypes without aphids was determined, and five genes with constitutively higher expression levels were found in Dowling. Keywords = genomic Keywords = Defense Responses Keywords = plant Keywords = DNA-binding protein Keywords = PR proteins Keywords = plant resistance Keywords = signal transduction keywords = insect Keywords: susceptible vs resistant
2008-10-13 | GSE3070 | GEO
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
Project description:Aphis glycines Gene expression
| PRJNA231526 | ENA
Project description:Aphis glycines head whole genome bisulfite sequencing
| PRJNA509918 | ENA
Project description:Aphis glycines strain:Biotype 1 Raw sequence reads
| PRJNA729496 | ENA
Project description:Aphis glycines strain:Biotype 3 Transcriptome or Gene expression
| PRJNA222584 | ENA
Project description:Aphis glycines Transcriptome or Gene expression
| PRJNA269762 | ENA
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