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Disentangling detoxification: gene expression analysis of feeding mountain pine beetle illuminates molecular-level host chemical defense detoxification mechanisms.

ABSTRACT: The mountain pine beetle, Dendroctonus ponderosae, is a native species of bark beetle (Coleoptera: Curculionidae) that caused unprecedented damage to the pine forests of British Columbia and other parts of western North America and is currently expanding its range into the boreal forests of central and eastern Canada and the USA. We conducted a large-scale gene expression analysis (RNA-seq) of mountain pine beetle male and female adults either starved or fed in male-female pairs for 24 hours on lodgepole pine host tree tissues. Our aim was to uncover transcripts involved in coniferophagous mountain pine beetle detoxification systems during early host colonization. Transcripts of members from several gene families significantly increased in insects fed on host tissue including: cytochromes P450, glucosyl transferases and glutathione S-transferases, esterases, and one ABC transporter. Other significantly increasing transcripts with potential roles in detoxification of host defenses included alcohol dehydrogenases and a group of unexpected transcripts whose products may play an, as yet, undiscovered role in host colonization by mountain pine beetle.

SUBMITTER: Robert JA

PROVIDER: S-EPMC3815198 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Disentangling detoxification: gene expression analysis of feeding mountain pine beetle illuminates molecular-level host chemical defense detoxification mechanisms.

Robert Jeanne A JA Pitt Caitlin C Bonnett Tiffany R TR Yuen Macaire M S MM Keeling Christopher I CI Bohlmann Jörg J Huber Dezene P W DP

PloS one 20131101 11

The mountain pine beetle, Dendroctonus ponderosae, is a native species of bark beetle (Coleoptera: Curculionidae) that caused unprecedented damage to the pine forests of British Columbia and other parts of western North America and is currently expanding its range into the boreal forests of central and eastern Canada and the USA. We conducted a large-scale gene expression analysis (RNA-seq) of mountain pine beetle male and female adults either starved or fed in male-female pairs for 24 hours on ...[more]

PMID: 24223726

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Project description:Planning forest management relies on predicting insect outbreaks such as mountain pine beetle, particularly in the intermediate-term future, e.g., 5-year. Machine-learning algorithms are potential solutions to this challenging problem due to their many successes across a variety of prediction tasks. However, there are many subtle challenges in applying them: identifying the best learning models and the best subset of available covariates (including time lags) and properly evaluating the models to avoid misleading performance-measures. We systematically address these issues in predicting the chance of a mountain pine beetle outbreak in the Cypress Hills area and seek models with the best performance at predicting future 1-, 3-, 5- and 7-year infestations. We train nine machine-learning models, including two generalized boosted regression trees (GBM) that predict future 1- and 3-year infestations with 92% and 88% AUC, and two novel mixed models that predict future 5- and 7-year infestations with 86% and 84% AUC, respectively. We also consider forming the train and test datasets by splitting the original dataset randomly rather than using the appropriate year-based approach and show that this may obtain models that score high on the test dataset but low in practice, resulting in inaccurate performance evaluations. For example, a k-nearest neighbor model with the actual performance of 68% AUC, scores the misleadingly high 78% on a test dataset obtained from a random split, but the more accurate 66% on a year-based split. We then investigate how the prediction accuracy varies with respect to the provided history length of the covariates and find that neural network and naive Bayes, predict more accurately as history-length increases, particularly for future 1- and 3-year predictions, and roughly the same holds with GBM. Our approach is applicable to other invasive species. The resulting predictors can be used in planning forest and pest management and planning sampling locations in field studies.

Dataset Information

Disentangling detoxification: gene expression analysis of feeding mountain pine beetle illuminates molecular-level host chemical defense detoxification mechanisms.

Publications

Disentangling detoxification: gene expression analysis of feeding mountain pine beetle illuminates molecular-level host chemical defense detoxification mechanisms.

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