The e-ROSA project seeks to build a shared vision of a future sustainable e-infrastructure for research and education in agriculture in order to promote Open Science in this field and as such contribute to addressing related societal challenges. In order to achieve this goal, e-ROSA’s first objective is to bring together the relevant scientific communities and stakeholders and engage them in the process of coelaboration of an ambitious, practical roadmap that provides the basis for the design and implementation of such an e-infrastructure in the years to come.
This website highlights the results of a bibliometric analysis conducted at a global scale in order to identify key scientists and associated research performing organisations (e.g. public research institutes, universities, Research & Development departments of private companies) that work in the field of agricultural data sources and services. If you have any comment or feedback on the bibliometric study, please use the online form.
You can access and play with the graphs:
- Evolution of the number of publications between 2005 and 2015
- Map of most publishing countries between 2005 and 2015
- Network of country collaborations
- Network of institutional collaborations (+10 publications)
- Network of keywords relating to data - Link
Transcriptomic and Proteomic Responses of Sweetpotato Whitefly, Bemisia tabaci, to Thiamethoxam
Background: The sweetpotato whitefly, Bemisia tabaci (Hemiptera: Aleyrodidae), is one of the most widely distributed agricultural pests. Although it has developed resistance to many registered insecticides including the neonicotinoid insecticide thiamethoxam, the mechanisms that regulate the resistance are poorly understood. To understand the molecular basis of thiamethoxam resistance, "omics" analyses were carried out to examine differences between resistant and susceptible B. tabaci at both transcriptional and translational levels. Results: A total of 1,338 mRNAs and 52 proteins were differentially expressed between resistant and susceptible B. tabaci. Among them, 11 transcripts had concurrent transcription and translation profiles. KEGG analysis mapped 318 and 35 differentially expressed genes and proteins, respectively, to 160 and 59 pathways (p<0.05). Thiamethoxam treatment activated metabolic pathways (e.g., drug metabolism), in which 118 transcripts were putatively linked to insecticide resistance, including up-regulated glutathione-S-transferase, UDP glucuronosyltransferase, glucosyl/glucuronosyl transferase, and cytochrome P450. Gene Ontology analysis placed these genes and proteins into protein complex, metabolic process, cellular process, signaling, and response to stimulus categories. Quantitative real-time PCR analysis validated "omics" response, and suggested a highly overexpressed P450, CYP6CX1, as a candidate molecular basis for the mechanistic study of thiamethoxam resistance in whiteflies. Finally, enzymatic activity assays showed elevated detoxification activities in the resistant B. tabaci. Conclusions: This study demonstrates the applicability of high-throughput omics tools for identifying molecular candidates related to thiamethoxam resistance in an agricultural important insect pest. In addition, transcriptomic and proteomic analyses provide a solid foundation for future functional investigations into the complex molecular mechanisms governing the neonicotinoid resistance in whiteflies.
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