De novo transcriptome assembly analysis of weed Apera spica-venti from seven tissues and growth stages
Marielle Babineau1, Khalid Mahmood1, Solvejg K Mathiassen1
1Department of Agroecology, Aarhus University, Forsøgsvej 1, Slagelse, 4200, Denmark.
Background:
Loose silky bentgrass (Apera spica-venti) is an important weed in Europe with a recent increase in herbicide resistance cases. The lack of genetic information about this noxious weed limits its biological understanding such as growth, reproduction, genetic variation, molecular ecology and metabolic herbicide resistance. This study produced a reference transcriptome for A. spica-venti from different tissues (leaf, root, stem) and various growth stages (seed at phenological stages 05, 07, 08, 09). The de novo assembly was performed on individual and combined dataset followed by functional annotations. Individual transcripts and gene families involved in metabolic based herbicide resistance were identified.
Results:
Eight separate transcriptome assemblies were performed and compared. The combined transcriptome assembly consists of 83,349 contigs with an N50 and average contig length of 762 and 658 bp, respectively. This dataset contains 74,724 transcripts consisting of total 54,846,111 bp. Among them 94% had a homologue to UniProtKB, 73% retrieved a GO mapping, and 50% were functionally annotated. Compared with other grass species, A. spica-venti has 26% proteins in common to Brachypodium distachyon, and 41% to Lolium spp. Glycosyltransferases had the highest number of transcripts in each tissue followed by the cytochrome P450s. The GSTF1 and CYP89A2 transcripts were recovered from the majority of tissues and aligned at a maximum of 66 and 30% to proven herbicide resistant allele from Alopecurus myosuroides and Lolium rigidum, respectively.
Conclusions:
De novo transcriptome assembly enabled the generation of the first reference transcriptome of A. spica-venti. This can serve as stepping stone for understanding the metabolic herbicide resistance as well as the general biology of this problematic weed. Furthermore, this large-scale sequence data is a valuable scientific resource for comparative transcriptome analysis for Poaceae grasses.
