Back

Soybean-SCN duel: Novel insight into Soybean's Resistant Responses to Heterodera glycines

Torabi, S.; Seifi, S.; Geddes-McAlister, J.; Tenuta, A.; Wally, O.; Torkamaneh, D.; Eskandari, M.

2023-05-23 plant biology
10.1101/2023.05.22.541756 bioRxiv
Show abstract

Soybean cyst nematodes (SCN, Heterodera glycines Ichinohe) are a significant threat to soybean production globally, causing severe yield losses and necessitating the development of effective strategies to combat this devastating nematode disease. This study presents dual RNA-seq analyses of the three most common SCN-resistant lines (Plant Introduction (PI) 437654, 548402, and 88788) and the susceptible line Lee74 against the SCN HG type 1.2.5.7 to identify the mechanisms of resistance and virulence genes involved in resistance breakdown. Transcriptomic and pathway analyses reveal the activation of the phenylpropanoid pathway, MAPK signaling pathway, plant hormone signal transduction, and secondary metabolite pathways in the resistance mechanisms. PI 437654, which exhibited robust resistance (female index, FI=0%), demonstrated unique gene expression associated with cell wall reinforcement, oxidative enzymes, ROS scavengers, and Ca+2 sensors governing the salicylic acid (SA) biosynthesis process, indicating its key defense mechanism. Moreover, using different hosts with varying levels of immunity and a susceptible line provided insights into SCN pathogenesis and how H. glycine overcomes different layers of host immunity by modulating its virulence genes. This research provides novel insights into the molecular mechanisms underlying soybean-SCN interactions and identifies potential targets for developing strategies to manage this devastating nematode disease.

Matching journals

The top 8 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.