Sentinel plot surveillance of cotton leaf curl disease in Pakistan- a case study at the cultivated cotton-wild host plant interface

Abstract

A sentinel plot case study was carried out to identify and map the distribution of begomovirus-betasatellite complexes in sentinel plots and commercial cotton fields over a four-year period using molecular and high-throughput DNA ‘discovery’ sequencing approaches. Samples were collected from 15 study sites in the two major cotton-producing areas of Pakistan. Whitefly- and leafhopper-transmitted geminiviruses were detected in previously unreported host plant species and locations. The most prevalent begomovirus was cotton leaf curl Kokhran virus-Burewala (CLCuKoV-Bu). Unexpectedly, a recently recognized recombinant, cotton leaf curl Multan virus-Rajasthan (CLCuMuV-Ra) was prevalent in five of 15 sites. cotton leaf curl Alabad virus (CLCuAlV) and cotton leaf curl Kokhran virus-Kokhran, ‘core’ members of CLCuD-begomoviruses that co-occurred with CLCuMuV in the ‘Multan’ epidemic were detected in one of 15 sentinel plots. Also identified were chickpea chlorotic dwarf virus and ‘non-core’ CLCuD-begomoviruses, okra enation leaf curl virus, squash leaf curl virus, and tomato leaf curl New Delhi virus. Cotton leaf curl Multan betasatellite (CLCuMuB) was the most prevalent CLCuD-betasatellite, and less commonly, two ‘non-core’ betasatellites. Recombination analysis revealed previously uncharacterized recombinants among helper virus-betasatellite complexes consisting of CLCuKoV, CLCuMuV, CLCuAlV and CLCuMuB. Population analyses provided early evidence for CLCuMuV-Ra expansion and displacement of CLCuKoV-Bu in India and Pakistan from 2012-2017. Identification of ‘core’ and non-core CLCuD-species/strains in cotton and other potential reservoirs, and presence of the now predominant CLCuMuV-Ra strain are indicative of ongoing diversification. Investigating the phylodynamics of geminivirus emergence in cotton-vegetable cropping systems offers an opportunity to understand the driving forces underlying disease outbreaks and reconcile viral evolution with epidemiological relationships that also capture pathogen population shifts. © 2023 The Author(s)