Capturing the transcription factor interactome in response to sub-lethal insecticide exposure

Area of Science:

• Entomology
• Genomics
• Toxicology

Background:

• Pesticide resistance in agricultural pests and disease vectors poses significant risks to food security and public health.
• Increasing insecticide resistance necessitates understanding the molecular mechanisms underlying pest responses to sub-lethal doses.

Purpose of the Study:

• To identify key transcription factors involved in the response to sub-lethal pyrethroid exposure in a resistant Anopheles coluzzii population.
• To model the complex regulatory networks governing insecticide resistance using dynamic Bayesian networks.

Main Methods:

• Application of dynamic Bayesian networks to a transcriptome time-course dataset.
• Analysis of gene expression data from Anopheles coluzzii exposed to sub-lethal pyrethroid doses.
• Inclusion of circadian rhythm and ageing effects in the network model.

Main Results:

• Identification of 44 transcription factors putatively regulating insecticide-responsive transcripts.
• High concordance between model-generated associations and laboratory validation.
• Discovery of six key regulators with distinct enrichment terms, highlighting the complexity of pesticide response.

Conclusions:

• The identified transcription factors provide crucial insights into the molecular mechanisms of insecticide resistance.
• The findings are relevant to a broad range of pest species due to overlapping resistance mechanisms.
• This research contributes to developing more effective pest management strategies for both agriculture and public health.