Genome annotations for: Multi-omics approaches define novel aphid effector candidates associated with virulence and avirulence phenotypes

Peter Thorpe1, Simone Altmann1, Rosa Lopez-Cobollo2, Nadine Douglas3, Javaid Iqbal2, Sadia Kanvil2, Jean-Christophe Simon4, James C. Carolan3, Jorunn Bos1*, Colin Turnbull2* 

1School of Life Sciences, University of Dundee, UK; 2Department of Life Sciences, Imperial College London, UK; 3Department of Biology, Maynooth University, Republic of Ireland; 4 INRAE , France. *Authors for correspondence: [e-mail], [e-mail].  

This is a repository for the version3 gene predictions and annotation for the pea aphid used for the publication:

Multi-omics approaches define novel aphid effector candidates associated with virulence and avirulence phenotypes 

ABSTRACT

Background. Compatibility between aphids and plant hosts is genetically determined by both interacting organisms. For example, plants may carry resistance (R) genes or deploy chemical defences. Aphid saliva contains many proteins that are secreted into host tissues. Subsets of these proteins are predicted to act as effectors, either subverting or triggering host immunity. However, associating particular effectors with virulence or avirulence outcomes presents challenges due to the combinatorial complexity. Here we use defined aphid and host genetics to test for co-segregation of expressed aphid transcripts and proteins with virulent or avirulent phenotypes. 

Results. We compared virulent and avirulent pea aphid parental genotypes, and their bulk segregant F1 progeny on Medicago truncatula genotypes carrying or lacking the RAP1 resistance quantitative trait locus. Differential expression analysis based on RNA sequencing of whole body and head samples, in combination with proteomics of saliva and salivary glands, enabled us to pinpoint proteins associated with virulence/avirulence phenotypes. There was relatively little impact of host genotype, whereas large numbers of transcripts and proteins were differentially expressed between parental aphids, likely a reflection of their classification as divergent biotypes within the pea aphid species complex. Many fewer transcripts intersected with the equivalent differential expression patterns in the bulked F1 progeny, providing an effective filter for removing genomic background effects. Overall, there were more upregulated genes detected in the F1 avirulent dataset compared with the virulent one. Some of the differentially expressed transcripts were also found in the differentially expressed proteomes, with aminopeptidase N proteins being the most frequent differentially expressed family. In addition, a substantial proportion (26%) of salivary proteins lack annotations, suggesting that many novel functions remain to be discovered.  

Conclusions. Especially when combined with tightly controlled genetics of both insect and host, multi-omic approaches are powerful tools for revealing and filtering candidate lists down to plausible genes for further functional analysis as putative aphid effectors.