DOI: 10.6084/m9.figshare.12519239.v1
title: Additional file 1 of Low UV-C stress modulates Chlamydomonas reinhardtii biomass composition and oxidative stress response through proteomic and metabolomic changes involving novel signalers and effectors

description: Additional file 1: Table S1. Physiological parameters related to photosynthesis –chlorophyll a (Chl a), chlorophyll b (Chl b), carotenoids (Car), total chlorophyll content (Chl a + b), Fv/Fm and Chla/b ratios-, culture total biomass-average cell density (cell/mL) and fresh weight per mL of culture (FW)-, biomass composition-Lipid weight (LW), starch, soluble sugars, free amino acids and phenolics abundance- and oxidative stress-related malondialdehyde (MDA) content. Pigment (Chl a, Chl b, Car), biomass composition measures (LW, starch, free amino acids, soluble sugars and phenolics) and MDA abundance were expressed as µmol, mg and nmol per mg of FW respectively. For each parameter is represented the harvesting time mean and SD. Significative differences were detected through a one-way ANOVA (⍺ = 0.05) over z-centered data. Tukey HSD post hoc test (⍺ = 0.05) was performed in order define differences between the different harvesting times. Table S2: List of the 885 quantified proteins with their abundances estimated by the integration of precursor’s areas. Protein are designated with their respective Chlamydomonas JGI v5.5, Viridiplantae-UniProt or Augustus database accession. Percent of coverage, number of unique peptides used for identification, residues and protein molecular weight are indicated. Displayed data underwent filtering, imputation and sample abundance-based balancing. The mean abundance ± SD for each sampling time is indicated as well as their ANOVA p- and q-values (5% FDR), and the post hoc Tukey HSD test p-values calculated over Log10 transformed data. Deflines, symbols and MapMan bins were manually curated. Table S3: List of the 68 quantified metabolites. Metabolite names were included along their Golm metabolome database identifiers used as the main names of uncharacterized compounds. Retention time (RT) was included along the mass/charge ratios (m/z 1 and m/z 2) of the two most characteristic fragmentation ions for each compound. Metabolite abundance was estimated from the peak areas of the indicated characteristic ions. Abundance data underwent filtering, imputation and sample abundance-based balancing. The mean abundance respect to control ± SD for each sampling time are indicated as well as ANOVA p-values and post hoc Tukey (HSD) test p-values calculated over z-centered data. MapMan bins and deflines were manually curated. Table S4: PCA of the proteome dataset. Scores for the nine generated components for each sample are showed along proportion of the total sample variance explained by each component and loadings relating each protein variable contribution to each generated component. Proteins were identified by their respective Phytozome v5.5, Viridiplantae-UniProt or Augustus identifier. Table S5: PCA of the metabolome dataset. Scores of the nine generated components for each sample are showed along proportion of the total sample variance explained by each component and loadings relating each metabolite variable contribution to each generated component. Metabolites were identified by their respective names or their Golm metabolome database identifiers. Table S6: sPLS-based Integration proteome and metabolome datasets. Model was tuned keeping 125 protein (X) and 9 metabolite/physiology (Y) variables. Loadings relating each keep protein (X) or metabolite and physiological variables (Y) contribution to each two generated components are listed along the variable identifiers Proteins were identified by their respective Phytozome v5.5, UNIPROT-Viridiplantae or Augustus identifier. Metabolites were identified by their respective name or their Golm database identifier.