Alternative splicing of G protein-coupled receptors: physiology and pathophysiology

The G protein-coupled receptors (GPCRs) are a superfamily of transmembrane receptors that have a broad distribution and can collectively recognise a diverse array of ligands. Activation or inhibition of GPCR signalling can affect many (patho)physiological processes, and consequently they are a major target for existing and emerging drug therapies. A common observation has been that the pharmacological, signalling and regulatory properties of GPCRs can differ in a cell- and tissue-specific manner. Such “phenotypic” diversity might be attributable to post-translational modifications and/or association of GPCRs with accessory proteins, however, post-transcriptional mechanisms are also likely to contribute. Although approximately 50% of GPCR genes are intronless, those that possess introns can undergo alternative splicing, generating GPCR subtype isoforms that may differ in their pharmacological, signalling and regulatory properties. In this review we shall highlight recent research into GPCR splice variation and discuss the potential consequences this might have for GPCR function in health and disease

Summary of the mean expected loss and gain.

<p>Table values indicate mean expected loss and gain (percentage of suitable cells) as compared to the consensus model for the 20th century climate. Asterisk in the species code (*) indicate that the predictor data set was extended by Tmax and PSeason. Model specific values (GAM, GLM and RF) are based on averaging across all future projections of the three climate models. Climate model specific values (ECHAM5, HADCM3 and IPSL-CM4) are based on averaging across all corresponding projections of the three SDMs. “Mean” indicates loss and gain resulting from comparison between the consensus future projection merging all SDMs and all climate change models and the consensus model for the 20th century climate.</p

Mean permutation importance of the predictor groups, cluster id's and the corresponding cluster membership grades.

<p>Table values indicate average permutation importance in percent of each predictor group: climate (C), altitude (H) and hydromorphology (HY). Members of the C group are AnnTMean, Isotherm, TSeason and AnnPMean, in the HY group are Strahler, CumLenkm and RtypMost while H is based on the permutation importance of AltMean. Degree to which each species belong to a Fuzzy cluster Fc is indicated by the membership grade MG.</p

Potential distribution patterns for 2050s under A1b scenario for <i>C. gobio</i>.

<p>Each row shows the results of the particular climate model: (A–D) ECHAM5, (E–H) HADCM3 and (I–L) IPSL-CM4. The first column shows the result of the mean consensus model based on all repetitions of GAM, GLM and RF, followed by the mean future projections using all repetitions of the individual models: GAM, GLM and RF, respectively. Gray dots indicate centroids of the grid cells across the whole study area (Germany).</p

Current species distributions and their model representations.

<p>Each row shows species specific results: (A–D) <i>A. alburnus</i>, (E–H) <i>C. gobio</i>, (I–L) <i>L. lota</i>, (M–P) <i>S. lucioperca</i> and (Q–T) <i>S. glanis</i>. The first column indicates current species distribution and is followed by the model representations using GAM, GLM and RF, respectively. Gray dots indicate centroids of the grid cells across the whole study area (Germany).</p

Major characteristics of the bioclimatic predictor variables for the 20th century climate and for the future climate projections (2050s).

<p>See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0040530#pone-0040530-t002" target="_blank">Table� 2</a> for abbreviation explanations.</p

Lorenz Curve and the empirical probability distributions.

<p>(A, D) <i>L. fluviatilis</i>, (B, E) <i>S. glanis</i> (b, e) and (C, F) <i>B. barbus</i>. The ROC score of 0.85 for AltMean (<i>L. fluviatilis</i>), 0.73 for AnnTMean (<i>S. glanis</i>) and 0.8 for Strahler (<i>B. barbus</i>) indicate high discriminatory power of the individual predictors in describing species presence (shaded bars) and absence patterns.</p

Summary of GAM, GLM and RF models based on AnnTMean, Isotherm, TSeason and AnnPMean.

<p>ROC score, sensitivities and specificities indicate averages over all model repetitions. Standard deviation of the ROC score estimates for the validation data sets ranges from 0.01 to 0.03 for GLM and GAM based models and from 0.01 to 0.02 for RF. The “ROC score change” is the difference between the mean ROC for the models based on climatic factors and the mean ROC for models considering climatic, topographic and hydromorphologic predictors (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0040530#pone-0040530-t003" target="_blank">Table 3</a>).</p

Environmental predictors and their discriminatory power.

<p>“ROC score” is the mean ROC score based on the univariate analysis; “PI” is the mean permutation importance of each individual predictor for GLM, GAM, RF and ME based multivariate SDMs; “No” is the number of species for which the respective predictor variable was identified as statistically significant (the total number of considered species is 38).</p

Uncertainty estimation of the future species distributions.

<p>Table values indicate percentage of cells with projected values outside the species calibration range (either lower than observed minimum or higher than the observed maximum). The column “total” is the total number of cells where for at least one model parameter the projected values are outside the species calibration range.</p