The first transcriptomes from field-collected individual whiteflies (Bemisia tabaci, hemiptera: Aleyrodidae): A case study of the endosymbiont composition

Abstract

Background: Bemisia tabaci species (B. tabaci), or whiteflies, are the world’s most devastating insect pests. They cause billions of dollars (US) of damage each year, and are leaving farmers in the developing world food insecure. Currently, all publically available transcriptome data for B. tabaci are generated from pooled samples, which can lead to high heterozygosity and skewed representation of the genetic diversity. The ability to extract enough RNA from a single whitefly has remained elusive due to their small size and technological limitations. Methods: In this study, we optimised a single whitefly RNA extraction procedure, and sequenced the transcriptome of four individual adult Sub-Saharan Africa 1 (SSA1) B. tabaci. Transcriptome sequencing resulted in 39-42 million raw reads. De novo assembly of trimmed reads yielded between 65,000-162,000 Contigs across B. tabaci transcriptomes. Results: Bayesian phylogenetic analysis of mitochondrion cytochrome I oxidase (mtCOI) grouped the four whiteflies within the SSA1 clade. BLASTn searches on the four transcriptomes identified five endosymbionts; the primary endosymbiont Portiera aleyrodidarum and four secondary endosymbionts: Arsenophonus, Wolbachia, Rickettsia, and Cardinium spp. that were predominant across all four SSA1 B. tabaci samples with prevalence levels of between 54.1 to 75%. Amino acid alignments of the NusG gene of P. aleyrodidarum for the SSA1 B. tabaci transcriptomes of samples WF2 and WF2b revealed an eleven amino acid residue deletion that was absent in samples WF1 and WF2a. Comparison of the protein structure of the NusG protein from P. aleyrodidarum in SSA1 with known NusG structures showed the deletion resulted in a shorter D loop. Conclusions: The use of field-collected specimens means time and money will be saved in future studies using single whitefly transcriptomes in monitoring vector and viral interactions. Our method is applicable to any small organism where RNA quantity has limited transcriptome studies.