Back

Conserved transcriptional reprogramming in nematode infected root cells

Saura-Sanchez, M.; Gomez Rojas, A.; Deveux, M.; Minne, M.; Grones, C.; Eekhout, T.; Abril-Urias, P.; Van Bel, M.; Tenorio Berrio, R.; Vandepoele, K.; Escobar, C.; Beeckman, T.; De Rybel, B.; Kyndt, T.

2025-09-05 plant biology
10.1101/2025.09.05.674408 bioRxiv
Show abstract

Plant-parasitic nematodes are responsible for important annual losses in crop productivity worldwide1,2. Although the formation of feeding organs within the roots is essential for successful sedentary parasitism3, the molecular mechanisms underlying their development are poorly understood. This is partly because these organs originate from a limited number of root cells4-7, making difficult to capture the transcriptional reprogramming that occurs during the early stages of the infection. Here, we first developed a comparative host-pathogen framework to study the nematode infection process in Arabidopsis and rice. Using a cross-species single-cell transcriptomics approach, we identified a unique molecular signature in infected root cells and show that the cellular reprogramming during these early stages is highly conserved across both host-pathogen interactions. This transcriptional cell reprogramming is associated with stemness acquisition related to de novo organogenesis process. By cell-type specific gene regulatory network analysis, we identified AtATHB2/OsHOX28 as an evolutionary conserved and key regulator of the nematode infection process. Loss-of-function of this regulator in both species results in nematode resistance without affecting root growth. This discovery opens up new avenues for the development of sustainable nematode control strategies that could be translated across crop species.

Matching journals

The top 3 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.