Additional file 2 of Integration of spatio-temporal variations of surface metabolomes and epibacterial communities highlights the importance of copper stress as a major factor shaping host-microbiota interactions within a Mediterranean seaweed holobiont
Creators: name: Paix, Benoît
name: Layglon, Nicolas
name: Le Poupon, Christophe
name: D’Onofrio, Sébastien
name: Misson, Benjamin
name: Garnier, Cédric
name: Culioli, Gérald
name: Briand, Jean-François
Publisher: name: figshare
Year: 2021
Subjects: subject: 59999 Environmental Sciences not elsewhere classified
subject: FOS: Earth and related environmental sciences
subject: 39999 Chemical Sciences not elsewhere classified
subject: FOS: Chemical sciences
subject: Ecology
subject: FOS: Biological sciences
subject: 69999 Biological Sciences not elsewhere classified
subject: Marine Biology
subject: Plant Biology

description: Additional file 1: Table S1 Environmental parameters and sampling dates (Part 1: from February to April 2017). For trace metals, (d) and (t) indicated dissolved and total acid leachable fractions, respectively. n.d.: not determined. Table S2 Multivariate pairwise results (p values) examining differences between “sample types” for the 16S rRNA gene dataset with all samples (999 permutations). Table S3 Multivariate pairwise results (p values) examining differences between “months” and between “sites” for the 16S rRNA gene dataset with Taonia atomaria samples (999 permutations). Table S4 Multivariate pairwise results (p values) examining differences between “months” and between “sites” for the 16S rRNA gene dataset with rocky biofilm samples (999 permutations). Table S5 Multivariate pairwise results (p values) examining differences between “months” and between “sites” for the 16S rRNA gene dataset with seawater samples (999 permutations). Table S6 SIMPER results of the most contributing genera to the dissimilarities between seawater and algal samples. Only the first 50% of the cumulative contribution is shown. p values were calculated with a permutation test constructed with 999 permutations and corresponded to the probability of getting a larger or equal average contribution in random permutation of the group factor. Cum. sum corresponded to the ordered cumulative contribution. Table S7 SIMPER results of the most contributing genera to the dissimilarities between rocky biofilm samples and algal samples. Only the first 50% of the cumulative contribution is showed. p values were calculated with a permutation test constructed with 999 permutations and corresponded to the probability of getting a larger or equal average contribution in random permutation of the group factor. Cum. sum corresponded to the ordered cumulative contribution. Table S8 Multivariate pairwise results (p values) examining differences between “months” and between “sites” for the LC-(+)-ESI-MS metabolomics dataset (999 permutations). Table S9 List of biomarkers (VIP score &gt; 2) identified by LC-HRMS and involved in the discrimination between months within surface extracts of T. atomaria (Part 1). P values corresponded to results of one-way ANOVA tests using “Month” as factor. Color codes corresponded to mean normalized concentrations (see Part2: Table S12). List of biomarkers (VIP score &gt; 2) identified by LC-HRMS and involved in the discrimination between months within surface extracts of T. atomaria (Part 2). P values corresponded to results of one-way ANOVA tests using “Month” as factor. Color codes corresponded to mean normalized concentrations. Table S10 List of biomarkers (VIP score &gt; 2) identified by LC-HRMS and involved in the discrimination between sites within surface extracts of T. atomaria. P values corresponded to results of one-way ANOVA tests using “Sites” as factor. Color codes corresponded to mean normalized concentrations. Figure S1 Discrimination of heterotrophic prokaryotes by flow cytometry. A successive 3-steps workflow was used. A: Samples were first screened for the presence of potential doublets or aggregates. Sample dilution was eventually adjusted in order to keep doublets below 5% of the total signal. B: Particles showing a red fluorescence (FL3) were excluded in order to keep only strict heterotrophs (i.e. presenting only the SYBR green-induced fluorescence). C: High side scatter signal harboring particles were excluded in order to enumerate only prokaryotes. Figure S2 Variations of thalli length (cm) conducted with all algal samples collected during the study and pictures of thalli collected at S1 (one replicate per month). Figure S3 Variations of cells densities at the surface of T. atomaria for each site (A) and heteroprokaryotic cell abundances in seawater samples (B). p values corresponded to results of one-way ANOVA using “Month” as the factor. Figure S4 Spatiotemporal dynamics of prokaryotic cell density (A), α-diversity (B), percentage of algal-core (C) and algal-enriched (D) taxa at the surface of T. atomaria. p values corresponded to results of one-way ANOVA using “Month” as factor. Figure S5 NMDS (Bray-Curtis index) showing prokaryotic β-diversity algal samples. Figure S6 NMDS (Bray-Curtis index) showing prokaryotic β-diversity of rocky biofilm (A) and seawater (B) samples. p values corresponded to results of one-way PERMANOVA using whether “Month” or “Site” as factors. Figure S7 Cladogram obtained from the LEfSe analysis built with the 16S rRNA gene dataset of all samples and revealing discriminant prokaryotic taxa specific to T. atomaria, to rocky biofilms and to seawater samples (LDA threshold set to 4). Figure S8 Cladogram obtained from the LEfSe analysis built with the 16S rRNA gene dataset of T. atomaria samples and revealing discriminant epibacterial taxa specific to each sampling month (LDA threshold set to 4.5). Figure S9 Cladogram obtained from the LEfSe analysis built with the 16S rRNA gene dataset of T. atomaria samples and revealing discriminant epibacterial taxa specific to each sampling site (LDA threshold set to 3.5). Figure S10 Percentage of core community sequences (A) and algal-enriched community sequences (B). *: “Other” corresponded to unaffiliated families and families below 1%. Figure S11 Plots from the db-RDA scoring (A) environmental parameters together with algal samples (identical to Fig. 3B), and (B) environmental parameters together with OTUs represented by dashed circles. Dashed circles size was proportional to their relative percentage within the whole algal dataset. Dashed circles filled in pink corresponded to core OTUs, while those filled in dark blue were not affiliated as core members. The name of the 5 main core OTUs were annotated above their corresponding dashed circles. Figure S12 GNPS molecular network built with LC-ESI-(+)-MS/MS data. Levels of annotations were attributed according to Schymanski et al., 2014. Abbreviations: DGTA: diacylglycerylhydroxymethyl-N,N,N-trimethyl-b-alanine, DGCC: monoacylglyceryl-3-O-carboxy-(hydroxymethyl)-choline, DG: diacylglycerol, MG: monoacylglycerol, GGG: geranylgeranylglycerol, DGDG : digalactosyldiacylglycerol, MGDG : monogalactosyldiacylglycerol. Figure S13 Scatter plots showing the normalized concentration of DMSP, and the relative abundances of OTUs from the correlation network affiliated to Rhodobacteraceae (Figure S5), as a function of the temperature. Scatter plots were overlayed with smoothed conditional means plots using geom_smooth() function (loess regression, span = 0.6). Figure S14 Scatter plots showing the normalized concentration of fucoxanthin and β-carotene, and the relative abundances of OTUs from the correlation network affiliated to Kordia and Roseibacillus (Figure S5), as a function of the dissolved copper concentration. Scatter plots were overlayed with smoothed conditional means plots using geom_smooth() function (loess regression, span = 0.6).