Back

A novel and efficient Apple Latent Spherical Virus-based gene silencing method for functional genomic studies in Chenopodium quinoa

Melgar, A. E.; Palacios, M. B.; Martinez-Tosar, L. J.; Zelada, A. M.

2024-01-23 plant biology
10.1101/2024.01.20.576358 bioRxiv
Show abstract

Quinoa (Chenopodium quinoa Willd.), with its resilience in harsh environments and excellent nutritional value, has become crucial for global food security. Despite recent progress in genomic research, the inability to perform functional studies in quinoa due to the absence of transformation techniques remains a significant obstacle. In this work, we present the development of a novel Apple Latent Spherical Virus (ALSV)-mediated virus-induced gene silencing (VIGS) protocol that will allow to perform functional genomics studies in quinoa in a fast and simple way. The method was fine-tuned using ALSV plasmids that carry partial gene sequences of phytoene desaturase (PDS) from Nicotiana benthamiana and quinoa. The developed technique involves an initial inoculation in Nicotiana plants through agroinfiltration with Agrobacterium tumefaciens cultures carrying the different viral constructs. Viral extracts were prepared using local or systemic leaves, which were then used as inoculum to infect quinoa leaves through mechanical damage. The method was successfully tested in two contrasting quinoa varieties, although some differences were observed in infection phenotype and viral susceptibility. The effect of insertion sequence size in the viral vectors was also analyzed, resulting in differences in bleaching or chlorosis phenotype and impact on plant growth. The presence of the virus in infected plants was confirmed, and the reduction in PDS gene expression in silenced plants was verified. Because quinoa lacks stable transformation protocols, limiting heterologous expression assays, our ALSV-based VIGS protocol is very attractive for loss-of-function gene studies.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.