Metabolic evidence for biogeographic isolation of the extremophilic bacterium Salinibacter ruber.

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1 Supplementary information: Metabolic evidence for biogeographic isolation of the extremophilic bacterium Salinibacter ruber. Ramon Rosselló-Mora 1, Marianna Lucio², Arantxa Peña 3, Jocelyn Brito-Echeverría 1, Aránzazu López-López 1, Maria Valens-Vadell 1, Moritz Frommberger², Josefa Antón 3, Philippe Schmitt-Kopplin². 1 Marine Microbiology Group, Institut Mediterrani d Estudis Avançats, IMEDEA (CSIC-UIB), C/Miquel Marqués 21, D Esporles, Spain ² GSF - National Research Center for Environment and Health, Institute of Ecological Chemistry, Ingolstädter Landstraße 1, D Neuherberg, Germany 3 División de Microbiología, Departamento de Fisiología, Genética y Microbiología, Universidad de Alicante, Alicante, Spain This supplementary material file contains three additional tables, three additional figures, and an explanatory text of the statistical analysis that we considered to be relevant for all the results presented. Supplementary tables: Table S1: Genes and designed amplification primers. Gene Size T m for PCR Primers for amplification GO Nr eno 1,140pb 60ºC eno_f: 5 -tcg acg agg cta tcc gac eno_r: 5 -ggc gtg ccg cta tac cgc-3 tuf 996pb 48ºC tuf_f: 5 -gac cac ggg aag acg-3 tuf_r: 5 -gtt atc ccc cgg cat pyrg 1,697pb 50ºC pyrg_f: 5 -ccg tgc aga cca agt-3 pyrg_r: 5 -cat gtc gac cga cgc-3 gap 980pb 48ºC gap_f: 5 -gct tgg aat taa tgg-3 gap_r: 5 -gcc gct cca cga gat-3 glya 1,257pb 48ºC glya_f: 5 -cgc tcc gca acc aag-3 glya_r: 5 -cgt aca gcg ggt gct-3 rpse 523pb 48ºC rpse_f: 5 -ggc gga tcg aaa aga-3 rpse_r: 5 -ccc gtt gaa cac ctt-3 rpsc 688pb 48ºC rpsc_f: 5 -ccc gtg ggc cag aaa-3 rpsc_r: 5 -gct ggg gcg act cct

2 Table S2: Analysis of Variance (one-way ANOVA) for the sample M8 (5 replicates), M31 (5 replicates), Pola13 (3 replicates), Pola18 (3 replicates) and IL3 (3 replicates). For each group of replicates the p-value was greater that the significance level of 0.01, than at the 0.01 level, the population means were not significantly different (NS). Where the Sum of Squares measures variation present in the data, it is calculated by summing squared deviations, the mean square is the sum of squares divided by its associated degrees of freedom, the F value is the F statistic for testing the null hypothesis (the means are the same) and the Pr > F is the probability of obtaining a greater F statistic than that observed if the null hypothesis is true. Sample Sum of squares Mean squares F value P value M8 6.69x x (NS) M x x (NS) Pola x x (NS) Pola x x (NS) Il3 5.27x x (NS) Table S3: List of discriminative m/z values and their corresponding metabolite elementary compositions [M+H] + calculated with a tolerance of 1 ppm. These were compared to the available databases ( and KEGG). Mediterranean Formula Atlantic Formula Peruvian Formula C3H5O3S C11H16N2O C4H8O C3H7NO2S C13H20O C8H18O C7H11NO C9H7NO C8H5NO C7H15NO C10H11NO C9H7NO C4H12N4O C10H10O C6H11NO C6H4N2O C14H8O C10H16N C7H6O C12H24O C8H6O C5H4N2O C15H22O C7H6N2O C9H16O C10H20O C5H10O C10H20O C12H14O C9H10O C8H5NO C10H20O C7H16N2O C9H10O C15H24O C8H7NO C10H14O C16H22O C9H11NO C3H6O6S C17H32O C9H13N2O C6H6N2O2S C16H30O C4H6O4S C6H13N3O C16H32O C8H6O C10H20O C13H14N2O C10H10O C12H19NO C18H34O C14H10O C13H12O C12H22O C7H14O C12H12N2O C20H38O C11H12N2O C11H6O C22H42O C14H24O C8H14N2O C25H48O C9H8O6

3 C13H14O C26H52O C11H23NOS C10H18O C23H46O C12H20O C12H30N C31H58O C11H19NO C11H9NO C21H28O C13H18O C8H13NO C33H64O C14H26O C9H9N5O C31H60O C15H30O C12H13NO C32H62O C7H14N2O6S C11H13N3O C16H22O C8H16N2O C14H16O C15H14O C16H28O C13H22O C18H24O C10H12O C11H9N5O C14H12O C17H26O C9H10O C18H30O C11H20O C13H22O C15H24O C20H26O C15H10O C18H36O C8H14O C17H18O C10H22O C19H26O C10H12N2O C20H28O C13H25NO C16H30O C16H24O C17H24O C13H20O C18H30O C14H16N2O C22H30O C15H28O C19H34O C17H13N2O C19H28O3S C17H14N2O C21H36O C18H30O C18H27NO C14H20O C22H30O C16H28O C17H23NO5S C16H19N3S C21H24O C18H28O C23H34O C18H32O C25H42O C20H40O C25H32O C19H38O C23H34O C10H12N4O5S C24H40O C17H16O C22H34O C20H30O C20H30O5S C19H30O C28H46O C19H32O C25H24N2O C21H40O C22H44O C19H22NO C25H44O C21H32O C26H34O C19H26O C30H38O C13H20O C26H44O C20H25N3O C28H38O C22H34O C29H40O C22H23NS C26H29N5O C20H30O C17H28O C21H25IN2S C32H58O C20H23N3O C30H40O C22H42O C33H56O C17H26O C34H58O C22H34O C33H65NO3

4 C21H36O C22H42O C24H38O C21H42O C14H21N3O6S C20H24O C24H44O C23H42O C24H34O C18H30O C22H21N3O C25H36O C22H40O C25H22O C22H26O C22H26O C21H30N4O C27H46O C24H36O C27H48O C25H42O C24H36O C28H56O C27H40O C30H52O C31H42O C28H46O C27H44O C26H42O C30H42O C25H44O C29H46O C28H35NO C31H62O C29H42O C31H50O C23H36O C30H58O C31H60O C27H46O C27H48O C30H32O C28H40O C30H44O C30H32O C34H54O C34H56O3

5 Supplementary figures: Figure S1: Phylogenetic reconstruction based on a PHYML algorithm of a 6,513 nucleotide alignment corresponding to the 7 housekeeping genes listed in Table S3, in which SSU rrna was not used for reconstructions. Strains of different geographical areas are marked with their respective colors. The bar indicates 1% sequence divergence. It is remarkable that no geographical trend could be obtained. As indicated in the Material and Methods section, the same dataset was used to calculate reconstructions with several treeing approaches. Most of the trees gave congruent topologies independently of the use of PHYML or ARB (maximum likelihood and neighbor joining algorithms). Only ML showed slightly different topologies when including the indels in the analysis. In any case, none of the obtained tree topologies showed a clear geographic trend. Contrary to the same reconstruction where the SSU rrna gene was included, bootstrap values were lower. However, despite a lower robustness of the tree topology, there was no doubt about the common affiliations between the Peruvian and Atlantic strains, and between C9 and E3,

6 Figure S2: Score plot of the partial least square discriminative analysis (PLS-DA). The interpretation of the figure indicated that each class was "tight" and occupied a small and separate volume in Xspace (X represents the number of the variable). The discrimination derived from this discriminative plane (where the projected observations occur) well separated the tree groups according to the differentiation of the elementary compositions visualized in the Van Krevelen diagram (Figure 1D) and their geographic location.

7 Figure S3: Cladistic (left) and phenetic (right) analyses of the binary matrix compiling all variable peaks that proved not to be common to all organisms. The binary matrix contained 9,108 single peaks that were treated as independent covariant characters or as homologous positions for both analyses, respectively. The matrix had been obtained as a reduction of 29,012 peaks from raw spectra by selecting all peaks corresponding to masses smaller than 550, unifying single metabolites with different isotopic compositions, and by removing background noise, as clarified in the Material and Methods section. Colors listed in the legend indicate the origin of the strains: European (or Mediterranean), Atlantic (or Canary Islands), and Peruvian.

8 Supplementary Methods: Partial least squares discriminant analysis (PLS-DA): PLS-DA is optimal for the large number of predictors and the multicollineality (the presentation of the model is based on the description of Geladi et al., 1986). Supervised PLS-DA analysis uses independent (expression levels) and dependent variables (classes) for class comparisons. It applies multivariate statistical methods such as soft independent modeling of class analogy (SIMCA), and partial least squares modeling with latent variables, to allow simultaneous analysis of all variables (Nguyen, 2002). Additionally, PLS-DA provides a quantitative estimation of the discriminatory power of each descriptor by regression coefficient. The magnitude of the coefficient represents the relative importance of each data for the separation of the classes (Brindle, 2002), and measures the effect of particular substances identified by m/z value (Lee, 2003). Moreover interpretation of the regression coefficients provides information pertaining to the chemical explanation of class differences based on the fact that each coefficient is related to a certain m/z associated with a specific formula. The goodness of the model is summarized by R²Y(cum) values that represent the fraction of the variation of Y (all the responses) explained by the model after each component, and the Q²(cum) value, stands for the cumulative fractions of the variation of Y that can be predicted by the model according to the cross-validation. Q² calculation: (1.0 - PRESS/SS), where PRESS is the prediction error sum of the squares and SS is the residual sum of the squares of the previous component. Values of R²Y(cum) and Q²(cum) close to 1.0 indicates an excellent model. Scores and loading plots: The projections of the observations onto the new axes define their coordinates in the model. The values of the coordinates are called scores t. Plotting scores t, a visualization of the structure of the data (Figure S3), is permitted. The score vector t 1 can be thought of as a new variable (latent variable) that reflects the information in the original X- variables, and that is of relevance for modeling and predicting the response variable. The cosines of the angles between the old and the new coordinate systems are called loadings, p. Algebraically, they can be explained as how the variables are linearly combined to form the scores. The values of all observations projected onto the first principal component make up the vector t1, and the scores for PC2 another vector, t2. Similarly, the loadings calculated between the variables and PC1 constitute the vector p1, and between the variables and PC2 the vector p2. The vectors of all principal components together form the matrices T and P, which describe the matrix X. The relation is: X = T P T = t 1 p1 T + t 2 p2 T +

9 where the score matrices, T, and the loading matrices, P, together make up the principal component, (t1 p1t= first principal component, t2 p2t = second principal component). The plot of the scores describes the structure of the data. This plot is called a score scatter plot. Observations grouped together in a score scatter plot have similar properties, since they are described similarly by the principal components. Orthogonal-PLS (OPLS): The objective of OPLS is to accomplish a predictive model X Y (X is the matrix of spectral data and Y the response variables) where the systematic variation in the X-block is divided into two model parts, one part which models the correlations between X and Y and another part which expresses the variation in X that is not related (orthogonal) to Y (Eriksson et al., 2006). The logic is of a regular PLS model, which, after filtering, has been divided in two parts, a predictive part and an orthogonal part. The number of predictive and orthogonal components is decided with cross-validation (Wold, 1978). Supplementary references: Geladi, P. and B.R. Kowalski (1986). Partial Least-Squares regression: A Tutorial. Analytica Chimica Acta 185:1-17. Joanne T. Brindle, Henrik Antti, Elaine Holmes, George Tranter, Jeremy K. Nicholson, Hugh W.L. Bethell, Sarah Clarke, Peter M. Schofield, Elaine McKilligin, David E. Mosedale & David J. Grainger (2002) Rapid and noninvasive diagnosis of the presence and severity of coronary heart disease using 1H-NMR-based metabonomics. Nature Medicine 8: Kwan R. Lee, Xiwu Lin, Daniel C. Park, Sergio Eslava (2003) Megavariate data analysis of mass spectrometric proteomics data using latent variable projection method. Proteomics 3: Lennart Eriksson, Marianne Toft, Erik Johansson, Svante Wold and Johan Trygg (2006) Separating Y-predictive and Y-orthogonal variation in multi-block spectral data. J. Chemometrics 20: Nguyen DV, Rocke DM (2002): Partial least squares proportional hazard regression for application to DNA microarray survival data. Bioinformatics; 18: Wold S. (1978) Cross-validatory estimation of the number of components in factor and principal components models. Technometrics; 20: