Elucidating the lipidome of Archaea is essential to understand their tolerance to extreme environmental conditions. Previous characterizations of the lipid composition of Pyrococcus species, a model genus of hyperthermophilic archaea belonging to the Thermococcales order, led to conflicting results, which hindered the comprehension of their membrane structure and the putative adaptive role of their lipids. In an effort to clarify the lipid composition data of the Pyrococcus genus, we thoroughly investigated the distribution of both the core lipids (CL) and intact polar lipids (IPL) of the model Pyrococcus furiosus and, for the first time, of Pyrococcus yayanosii, the sole obligate piezophilic hyperthermophilic archaeon known to date. We showed a low diversity of IPL in the lipid extract of P. furiosus, which nonetheless allowed the first report of phosphatidyl inositol-based glycerol mono- and trialkyl glycerol tetraethers. With up to 13 different CL structures identified, the acid methanolysis of Pyrococcus furiosus revealed an unprecedented CL diversity and showed strong discrepancies with the IPL compositions reported here and in previous studies. By contrast, P. yayanosii displayed fewer CL structures but a much wider variety of polar heads. Our results showed severe inconsistencies between IPL and CL relative abundances. Such differences highlight the diversity and complexity of the Pyrococcus plasma membrane composition and demonstrate that a large part of its lipids remains uncharacterized. Reassessing the lipid composition of model archaea should lead to a better understanding of the structural diversity of their lipidome and of their physiological and adaptive functions.
a b s t r a c tIn the theory of sufficient dimension reduction, Sliced Inverse Regression (SIR) is a famous technique that enables us to reduce the dimensionality of regression problems. This semiparametric regression method aims at determining linear combinations of a pdimensional explanatory variable x related to a response variable y. However it is based on a crucial condition on the marginal distribution of the predictor x, often called the linearity condition. From a theoretical and practical point of view, this condition appears to be a limitation. Using an idea of Li, Cook, and Nachtsheim (2004) in the Ordinary Least Squares framework, we propose in this article to cluster the predictor space so that the linearity condition approximately holds in the different partitions. Then we apply SIR in each cluster and finally estimate the dimension reduction subspace by combining these individual estimates. We give asymptotic properties of the corresponding estimator. We show with a simulation study that the proposed approach, referred as cluster-based SIR, improves the estimation of the e.d.r. basis. We also propose an iterative implementation of cluster-based SIR and show in simulations that it increases the quality of the estimator. Finally the methodology is applied on the horse mussel data and the comparison of the prediction reached on test samples shows the superiority of cluster-based SIR over SIR.
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