Back

Identification of Lactuca sativa transcription factors impacting resistance to Botrytis cinerea through predictive network inference

Pink, H.; Talbot, A.; Carter, R.; Hickman, R.; Cooper, O.; Law, R.; Higgins, G.; Yao, C.; Gawthrop, F.; Hand, P.; Pink, D.; Clarkson, J.; Denby, K.

2023-07-19 plant biology
10.1101/2023.07.19.549542 bioRxiv
Show abstract

Lettuce is susceptible to a wide range of plant pathogens including the fungal pathogens Botrytis cinerea and Sclerotinia sclerotiorum, causal agents of grey mould and lettuce drop, respectively. Chemical control is routinely used but there is an urgent need to develop varieties with enhanced resistance given the economic and environmental costs of preventative pesticide sprays, the prevalence of fungicide-resistant isolates of both pathogens in the field, and the increasing withdrawal of approved fungicides through legislation. Resistance against Botrytis cinerea and Sclerotinia sclerotiorum is quantitative, governed by multiple small-medium impact loci, with plant responses involving large-scale transcriptional reprogramming. The elucidation of the gene regulatory networks (GRNs) mediating these responses will not only identify key transcriptional regulators but also interactions between regulators and show how the defence response is fine-tuned to a particular pathogen. We generated high-resolution (14 time points) time series expression data from lettuce leaves following mock-inoculation or inoculation with B. cinerea, capturing the dynamics of the transcriptional response to infection. Integrating this data with a time series dataset from S. sclerotiorum infection of lettuce identified a core set of 4362 genes similarly differentially expressed in response to both pathogens. Using the expression data for these core genes (with additional single time point data from 21 different lettuce accessions) we inferred a GRN underlying the lettuce defence response to these pathogens. Using the GRN, we have predicted and validated key regulators of lettuce immunity, identifying both positive (LsBOS1) and negative (LsNAC53) regulators of defence against B. cinerea, as well as downstream target genes. These data provide a high level of detail on defence-induced transcriptional change in a crop species and a GRN with the ability to predict transcription factors mediating disease resistance both in lettuce and other species.

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.