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A single point mutation causes one-way alteration of pheromone receptor function in two Heliothis species.

ABSTRACT: The sex pheromone processing system of moths has been a major focus of research on olfaction and speciation, as it is highly specific and closely related to reproductive isolation. The two noctuid moths Heliothis virescens and Heliothis subflexa have been used as a model for deciphering the mechanisms underlying differentiation in pheromone communication, but no information exist regarding the functions of the pheromone receptors (PRs) of H. subflexa. Here, we functionally characterized all candidate PRs of H. subflexa, and found that only the response profile of OR6 differed between the two species. Through domain swapping and site-directed mutation followed by functional characterization, we identified a critical amino acid in OR6 caused a one-way alteration in specificity. This result suggests HsubOR6 evolved from an ancestral OR6 gene with a HvirOR6-like function and implies that the evolutionary direction of the receptor specificity was from the H. virescens-like pattern to H. subflexa-like pattern.

SUBMITTER: Cao S

PROVIDER: S-EPMC8403742 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundA family of parsimonious Gaussian mixture models for the biclustering of gene expression data is introduced. Biclustering is accommodated by adopting a mixture of factor analyzers model with a binary, row-stochastic factor loadings matrix. This particular form of factor loadings matrix results in a block-diagonal covariance matrix, which is a useful property in gene expression analyses, specifically in biomarker discovery scenarios where blood can potentially act as a surrogate tissue for other less accessible tissues. Prior knowledge of the factor loadings matrix is useful in this application and is reflected in the one-way supervised nature of the algorithm. Additionally, the factor loadings matrix can be assumed to be constant across all components because of the relationship desired between the various types of tissue samples. Parameter estimates are obtained through a variant of the expectation-maximization algorithm and the best-fitting model is selected using the Bayesian information criterion. The family of models is demonstrated using simulated data and two real microarray data sets. The first real data set is from a rat study that investigated the influence of diabetes on gene expression in different tissues. The second real data set is from a human transcriptomics study that focused on blood and immune tissues. The microarray data sets illustrate the biclustering family's performance in biomarker discovery involving peripheral blood as surrogate biopsy material.ResultsThe simulation studies indicate that the algorithm identifies the correct biclusters, most optimally when the number of observation clusters is known. Moreover, the biclustering algorithm identified biclusters comprised of biologically meaningful data related to insulin resistance and immune function in the rat and human real data sets, respectively.ConclusionsInitial results using real data show that this biclustering technique provides a novel approach for biomarker discovery by enabling blood to be used as a surrogate for hard-to-obtain tissues.

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A single point mutation causes one-way alteration of pheromone receptor function in two Heliothis species.

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