Dataset Information

Metabolic adaptation of diatoms to hypersalinity

ABSTRACT:

Microalgae are important primary producers and form the basis for the marine food web. As global climate changes, so do salinity levels that algae are exposed to. A metabolic response of algal cells partly alleviates the resulting osmotic stress. Some metabolites involved in the response are well studied, but the full metabolic implications of adaptation remain unclear. Improved analytical methodology provides an opportunity for additional insight. We can now follow responses to stress in major parts of the metabolome and derive comprehensive charts of the resulting metabolic re-wiring. In this study, we subjected three species of diatoms to high salinity conditions and compared their metabolome to controls in an untargeted manner. The three well-investigated species with sequenced genomes Phaeodactylum tricornutum, Thalassiosira pseudonana and Skeletonema marinoi were selected for our survey. The microalgae react to salinity stress with common adaptations in the metabolome by amino acid up-regulation, production of saccharides and inositols. But also species-specific dysregulation of metabolites is common. Several metabolites previously not connected with osmotic stress reactions are identified, including 4-hydroxyproline, pipecolinic acid, myo-inositol, threonic acid and acylcarnitines. This expands our knowledge about osmoadaptation and calls for further functional characterization of metabolites and pathways in algal stress physiology.

INSTRUMENT(S): Liquid Chromatography MS - alternating - hilic, Liquid Chromatography MS - positive - hilic, Gas Chromatography MS - positive, Liquid Chromatography MS - negative - hilic

SUBMITTER: Daniel Stettin Steffen Neumann Vera Nikitashina Mahnoor Zulfiqar

PROVIDER: MTBLS3177 | MetaboLights | 2023-09-04

REPOSITORIES: MetaboLights

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Dataset's files

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			Action	DRS
	MTBLS3177	Other
	FILES	Other
	a_MTBLS3177_GC-MS_positive__metabolite_profiling.txt	Txt
	a_MTBLS3177_LC-MS_alternating_hilic_metabolite_profiling.txt	Txt
	a_MTBLS3177_LC-MS_negative_hilic_metabolite_profiling.txt	Txt

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Publications

Metabolic adaptation of diatoms to hypersalinity.

Nikitashina Vera V Stettin Daniel D Pohnert Georg G

Phytochemistry 20220604

PMID: 35671808

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Project description:The invasive marine mussel Mytilus galloprovincialis has displaced the native congener Mytilus trossulus from central and southern California, but the native species remains dominant at more northerly sites that have high levels of freshwater input. To determine the extent to which interspecific differences in physiological tolerance to low salinity might explain limits to the invasive species’ biogeography, we used an oligonucleotide microarray to compare the transcriptional responses of these two species to an acute decrease in salinity. Among 6,777 genes on the microarray, 117 genes showed significant changes that were similar between species, and 12 genes showed significant species-specific responses to salinity stress. Osmoregulation and cell cycle control were important aspects of the shared transcriptomic response to salinity stress, whereas the genes with species-specific expression patterns were involved in mRNA splicing, polyamine synthesis, exocytosis, translation, cell adhesion, and cell signaling. Forty-five genes that changed expression significantly during salinity stress also changed expression during heat stress, but the direction of change in expression was typically opposite for the two forms of stress. These results (i) provide insights into the role of changes in gene expression in establishing physiological tolerance to acute decreases in salinity, and (ii) indicate that transcriptomic differences between M. galloprovincialis and M. trossulus in response to salinity stress are subtle and involve only a minor fraction of the overall suite of gene regulatory responses. Two species (Mytilus galloprovincialis, Mytilus trossulus), hypo-osmotic shock for four hours (850 mOs/kg), one control group (1000 mOs/kg) sampled at the end of the treatment exposure (850 mOs/kg), one control group (1000 mOs/kg) sampled at the beginning. Biological replicates: 6 in each treatment group, 6 in each control group. Heterologous and homologous hybridization to a microarray constructed from Mytilus californianus and Mytilus galloprovincialis sequences. A reference design that used separate pools of reference RNA for each species was employed. Reference amplified RNA (aRNA) was created for each species by pooling RNA before and after amplification. The reference pool was made up of RNA from six different samples: two base-line control samples from the beginning of the experiment, two treatment samples from the end of the four-hour hypo-osmotic exposure, and two time-control samples from the end of the four-hour exposure time. To accurately compare the transcriptomes of Mytilus galloprovincialis and M. trossulus, we chose to develop a common microarray format that could be used for both species. This microarray design consisted of probe sequences generated from the out-group species, M. californianus. M. trossulus and M. galloprovincialis are approximately 7.5 million years divergent from M. californianus, yet only 3.5 million years divergent from each other (Seed, 1992). Therefore, heterologous hybridization to the microarray allowed us to compare transcriptional responses of M. galloprovincialis and M. trossulus without the inherent sequence biases that would result from a microarray that was designed from sequences of either M. galloprovincialis or M. trossulus. A limited number of sequences (556) from ESTs from M. galloprovincialis that matched M. californianus ESTs were included on the microarray to test for the effects of sequence mismatches. Only probes that performed well for both M. galloprovincialis and M. trossulus were used in our analyses. In order to determine significant changes in expression, we conducted a two-way ANOVA, in which salinity and species were modeled as fixed effects, and focused on genes that were significant for the salinity effect or the species x temperature interaction. We ignored the species term from the ANOVA as this effect could have highlighted genes that differentially bound to probes on the microarray due to differences in sequence homology, thus not reflecting true differences in gene expression. In accordance with statistical convention, all genes with a significant species x temperature interaction were deemed not to have significant temperature effects, even if the temperature term from the ANOVA had a low P-value. All genes with FDR-corrected (Benjamini and Hochberg, 1995) P-values less than 0.05 were considered significant. Analyses were conducted in the R statistical programming environment (R Development Core Team, 2009).