Thermoacclimation and genome adaptation of the membrane lipidome in marine Synechococcus

Abstract

The marine cyanobacteria of the genus Synechococcus are important primary producers, displaying a wide latitudinal distribution that is underpinned by diversification into temperature ecotypes. The physiological basis underlying these ecotypes is poorly known. In many organisms, regulation of membrane fluidity is crucial for acclimating to variations in temperature. Here, we reveal the detailed composition of the membrane lipidome of the model strain Synechococcus sp. WH7803 and its response to temperature variation. Unlike freshwater strains, membranes are almost devoid of C18, mainly containing C14 and C16 chains with no more than two unsaturations. In response to cold, we observed a rarely observed process of acyl chain shortening that likely induces membrane thinning, along with specific desaturation activities. Both of these mechanisms likely regulate membrane fluidity, facilitating the maintenance of efficient photosynthetic activity. A comprehensive examination of 53 Synechococcus genomes revealed clade-specific gene sets regulating membrane lipids. In particular, the genes encoding desaturase enzymes, which is a key to the temperature stress response, appeared to be temperature ecotype-specific, with some of them originating from lateral transfers. Our study suggests that regulation of membrane fluidity has been among the important adaptation processes for the colonization of different thermal niches by marine Synechococcus.

Unveiling membrane thermoregulation strategies in marine picocyanobacteria

“…PG is present in heterotrophic bacteria, complicating the interpretation of the PG data of the three nonaxenic strains. In cyanobacteria, this minor lipid is thought to play roles in the stabilization of photosystems (Itoh et al , ; Mizusawa and Wada, ; Boudière et al , ) rather than being directly involved in membrane thermoacclimation processes, as supported by the results for the axenic strains WH8102 and WH7803 (Pittera et al , ; Fig. S6).…”

“…Reads were first recruited against a database of 155 protein sequences of the four lipid desaturases (DesC3, DesC4, DesA2, DesA3) extracted from the 53 Synechococcus / Cyanobium genomes of the information system Cyanorak v.2 (http://sb-roscoff.fr/Phyto/cyanorak; Pittera et al , ; Dataset S1) using Blastx analyses (v.2.7.1+; Altschul et al , ) with default parameters, limiting the results to one target sequence (‐‐max_target_seqs 1), and considering only the results with an e‐value < 0.001. The recruited reads were then mapped with Blastn (with the same options as above) against a database including the 155 above mentioned Synechococcus / Cyanobium desaturases as well as many sequences used as outgroups, including 117 Prochlorococcus fatty acid desaturase sequences (Dataset S2), 185 outgroup cyanobacterial genomes from Cyanobase plus 537 genomes from proGenomes (Dataset S3), 6092 additional nonpicocyanobacterial fatty acid desaturase sequences retrieved from NCBI (Dataset S4) as well as all marine picocyanobacterial desaturases from Cyanorak.…”

The lipid metabolism in thraustochytrids

“…What elicits the formation of zoospores and what induces their maturation, or how the population size of a colony is controlled [53] are also unanswered questions. Another open and intriguing question is why marine microalgae and marine protists contain such high quantities of VLCPUFAs, a feature not or rarely found in photosynthetic eukaryotes of the green lineage (Viridiplantae, first endosymbiosis) nor in cyanobacteria which are supposed to be at the origin of the first endosymbiosis, as shown in marine Synechococcus [142]. This characteristic may result from a specific adaption to the marine environment.…”

Synergic Effects of Temperature and Irradiance on the Physiology of the Marine Synechococcus Strain WH7803

“…Pigment content was measured by HPLC for all treatments except the L/D cycles ( Supplementary Table S1) using the protocol described in Pittera et al (2018). Furthermore, previously published lipid data, corresponding to the LLLT and LLHT treatments (see Supplementary Figures S3,S4 in Pittera et al, 2018) were also used for correlation analyses (see below).…”

“…A signed adjacency matrix between genes was calculated and Pearson correlations were weighted by taking their absolute value and raising them to the power β = 12 to optimize the scale-free topology network fit. In order to identify groups of genes, whose expression was correlated to the biological, physical or chemical traits available for this experiment (including previously published lipid data; Pittera et al, 2018), the pairwise Pearson correlation coefficients between the principal component of each module, referred to as the module eigenvalue (ME), and these traits was then calculated using the R package WGCNA (Langfelder and Horvath, 2008). Modules were then filtered using a partial least square (PLS) regression, a dimensionality-reduction method that aims at determining predictor combinations with maximum covariance with the response variable.…”