Environmental factors influence cross-talk between a heat shock protein and an oxidative stress protein modification in the lizard Gallotia galloti

Abstract

Better understanding how organisms respond to their abiotic environment, especially at the biochemical level, is critical in predicting population trajectories under climate change. In this study, we measured constitutive stress biomarkers and protein post-translational modifications associated with oxidative stress in Gallotia galloti, an insular lizard species inhabiting highly heterogeneous environments on Tenerife. Tenerife is a small volcanic island in a relatively isolated archipelago off the West coast of Africa. We found that expression of GRP94, a molecular chaperone protein, and levels of protein carbonylation, a marker of cellular stress, change across different environments, depending on solar radiation-related variables and topology. Here, we report in a wild animal population, cross-talk between the baseline levels of the heat shock protein-like GRP94 and oxidative damage (protein carbonylation), which are influenced by a range of available temperatures, quantified through modelled operative temperature. This suggests a dynamic trade-off between cellular homeostasis and oxidative damage in lizards adapted to this thermally and topologically heterogeneous environment.

Fig 1. Map showing the approximate separation of the two morphotypes (subspecies) on the island, and approximate genetic separation, with labels showing the expected morphotype and clade in each area, based on Brown et al.

[67]. Environmental zoning approximated from Algar & López‐Darias [61], showing the different environment types quantified on Tenerife. Environment C (green) occupies a similar distribution as the "eisentrauti" morphotype. Black squares indicate sampling sites with elevation in metres above sea level. Map outline and landmark features “El Teide” and “Santa Cruz de Tenerife” were traced from arcGIS (Esri, “Topographic” [basemap]. “World Topographic Map”).

Fig 2

A) Representative western blot for HSPs with two bands appearing for GRP94, and one for HSP70. Molecular weights are labelled, and Ponceau S staining from the same membrane as a loading control is included in the lower panel. B) GRP94 and C) HSP70 expression measured in arbitrary units (A.U.) in G. galloti across different localities in Tenerife, from the lowest to the highest elevation. Sex is indicated by colour, where blue = males and red = females. D) and E) show significant interaction terms determined from model selection for GRP94 expression. GRP94 expression is plotted on the log₁₀ scale. The main predictor is plotted on the x-axis and the moderating predictor, relative humidity (RH) is plotted as three separate lines, with a mean and ± 1 standard deviation (SD), with 50% confidence intervals. Points plotted are partial residuals which account for all variables in the model. The shade of the points corresponds to the moderator variable value.

Fig 3

Representative Western blots (one of four biological replicates) for total carbonylation (A) and total 3-NT (B), labelled with molecular weights, and corresponding Ponceau S stain used as a loading control in the lower panel. Arrows and red boxes represent the bands quantified and the antibodies are labelled (α-DNP for the carbonylation assay and α-3-NT for 3-NT). C) Total carbonylation and D) 3-NT levels measured in arbitrary units (A.U) in G. galloti across different localities in Tenerife, from lowest to highest elevation. Sex is indicated by colour, where blue = males and red = females. E) Significant interaction terms determined from model selection for carbonylation, plotted on the log₁₀ scale. Elevation is plotted on the x-axis and the moderating predictor (solar radiation) is plotted as three separate lines, with a mean and ± 1 standard deviation (SD), with 50% confidence intervals. Points plotted are partial residuals which account for all variables in the model. The shade of the points corresponds to the moderator variable value. F) Estimated marginal means between sexes for total carbonylation, where the central light blue point indicates the estimated marginal mean, and the error bars represent the standard error of estimate.

Fig 4

A) Negative correlation between relative total carbonylation and relative GRP94 expression with a linear fit and a 95% confidence interval. Raw observations are plotted with colours referring to localities Spearman correlation coefficient and p-value are calculated. B) Total carbonylation and GRP94 expression plotted against modelled mean (24 h) operative temperature°C (T_e), Observed values for each protein are plotted and coloured, and linear fit show 95% confidence intervals. R correlation coefficient and p-value calculated from Spearman’s correlation coefficient for total carbonylation (left) and GRP94 (right).