Biology subjects

Colvin, J.

Publications and source records attributed to Colvin, J..

4 recordsLinked to original sources

bioRxiv2026

Creating resistance to the whitefly Bemisia tabaci in cassava through RNAi-mediated targeting of multiple insect metabolic processes

It is commonplace in East Africa for 100% of cassava fields to be infected with Cassava mosaic disease (CMD) and/or Cassava brown streak disease (CBSD), resulting in annual losses of more than US$1.25 billion and reduced food and economic security for farming households. The vector of both diseases is the African cassava species of the whitefly Bemisia tabaci. Since the late 1990s, there has been an unprecedented increase in whitefly populations, to the extent that they are referred to as "super-abundant". Research efforts since the late 1990s has focused mainly on developing plant resistance to the viral pathogens and paid scant attention to understanding the root causes of disease epidemics or the control of whitefly infestation. Here, we aimed at developing long-term whitefly-control solutions using an in-planta RNA interference (RNAi) approach. First, transcriptome analysis identified candidate genes that play key roles in whitefly biology: osmoregulation, sugar metabolism and transport, symbiosis with endosymbiotic bacteria and detoxification of phytotoxins. Then, fifteen RNAi inverted repeat constructs were produced, designed to target the candidate genes and 140 independent transgenic lines were generated in cassava variety NASE 13. Whole plant bioassays showed insecticidal activity of transgenic plants, reaching 58% lethality for adults within 7 days and 75-90% lethality of nymphs after 25 days, compared to control plants. Target genes were confirmed to be downregulated by up to 2.5-fold in adult whiteflies and nymphs. We used population dynamics modelling to predict the potential of the RNAi technology to control whiteflies under field conditions in East Africa.

bioengineering↗

bioRxiv2020

A high-throughput amplicon sequencing approach for population-wide species diversity and composition survey

Management of agricultural pests requires an understanding of pest species diversity, their interactions with beneficial insects and spatial-temporal patterns of pest abundance. Invasive and agriculturally important insect pests can build up very high populations, especially in cropping landscapes. Traditionally, sampling effort for species identification involves small sample sizes and is labour intensive. Here, we describe a multi-primer high throughput sequencing (HTS) metabarcoding method and associated analytical workflow for a rapid, intensive, high-volume survey of pest species compositions. We demonstrate our method using the taxonomically challenging Bemisia pest cryptic species complex as examples. The whiteflies Bemisia including the tabaci species are agriculturally important capable of vectoring diverse plant viruses that cause diseases and crop losses. Our multi-primer metabarcoding HTS amplicon approach simultaneously process high volumes of whitefly individuals, with efficiency to detect rare (i.e., 1%) test-species, while our improved whitefly primers for metabarcoding also detected beneficial hymenopteran parasitoid species from whitefly nymphs. Field-testing our redesigned Bemisia metabarcoding primer sets across the Tanzania, Uganda and Malawi cassava cultivation landscapes, we identified the sub-Saharan Africa 1 Bemisia putative species as the dominant pest species, with other cryptic Bemisia species being detected at various abundances. We also provide evidence that Bemisia species compositions can be affected by host crops and sampling techniques that target either nymphs or adults. Our multi-primer HTS metabarcoding method incorporated two over-lapping amplicons of 472bp and 518bp that spanned the entire 657bp 3 barcoding region for Bemisia, and is particularly suitable to molecular diagnostic surveys of this highly cryptic insect pest species complex that also typically exhibited high population densities in heavy crop infestation episodes. Our approach can be adopted to understand species biodiversity across landscapes, with broad implications for improving trans-boundary biosecurity preparedness, thus contributing to molecular ecological knowledge and the development of control strategies for high-density, cryptic, pest-species complexes.

evolutionary biology↗

bioRxiv2019

On species delimitation, hybridization and population structure of cassava whitefly in Africa

The Bemisia cassava whitefly complex includes species that cause severe crop damage through vectoring cassava viruses in eastern Africa. Currently, the cassava whitefly complex is divided into species and subgroups based on very limited molecular markers that did not allow clear definition of species and population structure. Based on 14,358 genome-wide SNPs from 63 cassava whitefly individuals belonging to sub-Saharan African (SSA1, SSA2 and SSA4) species, and using a well-curated mtCOI gene database, we show clear incongruities in previous taxonomic approaches underpinned by effects from pseudogenes. We show that the SSA4 species is part of the SSA2 species, and that populations of the SSA1 species comprise of south-eastern (Madagascar, Tanzania) and north-western (Nigeria, Democratic Republic of Congo and Burundi) sub-species that show signatures of allopatric incipient speciation, with a hybrid zone separating these adjacent sub-species. These findings provide the first genomic insights into the evolution and molecular ecology of a highly cryptic hemipteran insect complex in African, and allow the systematic use of genomic data in management and control strategies for this important cassava pest.

ecology↗

bioRxiv2019

Mosquito and primate ecology predict human risk of yellow fever virus spillover in Brazil

Many (re)emerging infectious diseases in humans arise from pathogen spillover from wildlife or livestock, and accurately predicting pathogen spillover is an important public health goal. In the Americas, yellow fever in humans primarily occurs following spillover from non-human primates via mosquitoes. Predicting yellow fever spillover can improve public health responses through vector control and mass vaccination. Here, we develop and test a mechanistic model of pathogen spillover to predict human risk for yellow fever in Brazil. Our environmental risk model, based on the ecology of mosquito vectors and non-human primate hosts, distinguished municipality-months with yellow fever spillover from 2001 to 2016 with high accuracy (AUC = 0.71). Incorporating hypothesized cyclical dynamics of infected primates improved accuracy (AUC = 0.79). Using boosted regression trees to identify gaps in the mechanistic model, we found that important predictors include current and lagged (mechanistic) environmental risk, vaccine coverage, population density, temperature, and precipitation. More broadly, we show that for a widespread human viral pathogen, the ecological interactions between environment, vectors, reservoir hosts, and humans can predict spillover with surprising accuracy, suggesting the potential to improve preventative action to reduce yellow fever spillover and prevent onward epidemics in humans.

ecology↗