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. 2022 Jul 7;23:493. doi: 10.1186/s12864-022-08712-4

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© The Author(s) 2022

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.

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Fig. 1 — Diagrammatic representation of the methodology used to predict putative effectors from Diuraphis. noxia whole-body transcriptome data. RNA-seq data was mined for protein sequences with characteristics of extracellularly secreted proteins. This data was also analysed with EffectorP and combined into a single list, constituting the secretome. Selected sequences were analysed for salivary gland specific expression through RT-PCR. Orthologs of the putatively secreted D. noxia sequences were identified in three aphid species. The differential expression of the orthologs between the head versus body transcriptome data of three aphid species was determined to identify transcripts with a Log2-FC>2. This predicted differential expression was confirmed in D. noxia head, body and salivary gland tissue through RT-qPCR analysis