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Detecting processes of local adaptation in forest trees and identifying environmental selective drivers are of primary importance for forest management and conservation. Transplant experiments, functional genomics and population genomics are complementary tools to efficiently characterize heritable phenotypic traits and to decipher the genetic bases of adaptive traits. Using an integrative approach combining phenotypic assessment in common garden, transcriptomics and landscape genomics, we investigated leaf adaptive traits in Coffea mauritiana , a forest tree endemic to Reunion Island. Eight populations of C. mauritiana originating from sites with contrasted environmental conditions were sampled in common garden to assess several leaf morphological traits, to analyze the leaf transcriptome and leaf cuticular wax composition. The relative alkane content of cuticular waxes was significantly correlated with major climatic gradients, paving the way for further transcriptome-based analyses. The expression pattern of cuticle biosynthetic genes was consistent with a modulation of alkane accumulation across the population studied, supporting the hypothesis that the composition of cuticular wax is involved in the local adaptation of C. mauritiana. Association tests in landscape genomics performed using RNA-seq-derived single-nucleotide polymorphisms revealed that genes associated with cell wall remodeling also likely play an adaptive role. By combining these different approaches, this study efficiently identified local adaptation processes in a non-model species. Our results provide the first evidence for local adaptation in trees endemic to Reunion Island and highlight the importance of cuticle composition for the adaptation of trees to the high evaporative demand in warm climates. [ABSTRACT FROM AUTHOR]

In contrast to desiccation-tolerant 'orthodox' seeds, so-called 'intermediate' seeds cannot survive complete drying and are short-lived. All species of the genus Coffea produce intermediate seeds, but they show a considerable variability in seed desiccation tolerance (DT), which may help to decipher the molecular basis of seed DT in plants. We performed a comparative transcriptome analysis of developing seeds in three coffee species with contrasting desiccation tolerance. Seeds of all species shared a major transcriptional switch during late maturation that governs a general slow-down of metabolism. However, numerous key stress-related genes, including those coding for the late embryogenesis abundant protein EM6 and the osmosensitive calcium channel ERD4, were up-regulated during DT acquisition in the two species with high seed DT, C. arabica and C. eugenioides. By contrast, we detected up-regulation of numerous genes involved in the metabolism, transport, and perception of auxin in C. canephora seeds with low DT. Moreover, species with high DT showed a stronger down-regulation of the mitochondrial machinery dedicated to the tricarboxylic acid cycle and oxidative phosphorylation. Accordingly, respiration measurements during seed dehydration demonstrated that intermediate seeds with the highest DT are better prepared to cease respiration and avoid oxidative stresses. [ABSTRACT FROM AUTHOR]

Introduction: Software to predict the impact of aging on physical appearance is increasingly popular. But it does not consider the complex interplay of factors that contribute to skin aging., Objectives: To predict the +15-year progression of clinical signs of skin aging by developing Causal Bayesian Belief Networks (CBBNs) using expert knowledge from dermatologists., Material and Methods: Structures and conditional probability distributions were elicited worldwide from dermatologists with experience of at least 15 years in aesthetics. CBBN models were built for all phototypes and for ages ranging from 18 to 65 years, focusing on wrinkles, pigmentary heterogeneity and facial ptosis. Models were also evaluated by a group of independent dermatologists ensuring the quality of prediction of the cumulative effects of extrinsic and intrinsic skin aging factors, especially the distribution of scores for clinical signs 15 years after the initial assessment., Results: For easiness, only models on African skins are presented in this paper. The forehead wrinkle evolution model has been detailed. Specific atlas and extrinsic factors of facial aging were used for this skin type. But the prediction method has been validated for all phototypes, and for all clinical signs of facial aging., Conclusion: This method proposes a skin aging model that predicts the aging process for each clinical sign, considering endogenous and exogenous factors. It simulates aging curves according to lifestyle. It can be used as a preventive tool and could be coupled with a generative AI algorithm to visualize aging and, potentially, other skin conditions, using appropriate images., (© 2024 L'Oréal. Skin Research and Technology published by John Wiley & Sons Ltd.)