IV. RESULTS AND DISCUSSION
1.3.3. Discriminative analysis of Mediterranean strains
1.3.3.Discriminative analysis of Mediterranean strains
An independent experiment was performed to evaluate the metabolomic composition of four replicates from five Mediterranean strains: P13 and P18 from Alicante, M8 and M31 from Majorca, and IL3 from
Results and Discussion: Chapter 1
elaborated to the discriminative analysis of the Mediterranean strains. The soluble cellular fraction was chosen as representative in the OPLS-‐DA model, which rendered equivalent but clearer results than PLS-‐
DA, showing a clear separation between the three groups of strains (Fig. 17). Therefore, these differences observed between different strains may be attributed to strain-‐specific metabolisms rather than sample-‐to sample variations. pathways (confirmed by KEGG and Japanese metabolome database).
In contrast to previous results (Peña et al., 2005), when searching for discriminative phenotypes at a more reduced geographical scale using the ICR-‐FT/ MS approach, a phenotypic segregation in individual locations was observed (Fig. 17a). The main discriminative metabolomics profile features were different from those giving resolution at a larger geographical scale.
A
B
Results and Discussion: Chapter 1
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In such cases, geographical differences were associated to strain-‐specific compositions of N-‐containing molecules (Fig. 17b). The confrontation of their exact masses with the KEGG and the Japanese metabolome database (www.metabolome.jp) indicated that the discriminative molecules were involved primarily in the core metabolism (that is, carbohydrate, amino acid, and fatty acid biosynthesis and metabolism).
1.4.Conclusions
These findings revealed that intraspecific metabolic diversity of S. ruber can be readily detected by the ICR-‐FT/MS approach and that such diversity can be associated to different geographical patterns at different metabolic levels, which could not be revealed by standard genetic methods previously used to assess biogeography of prokaryotes (Ramette & Tiedje, 2007; Whitaker et al., 2003).
MLSA approach, based on different gene data sets, did not resolve putative genetic-‐geographic patterns, as the genetic divergence may be too subtle for the given selection of genes. However, one must take into account that, despite the fact that large sets of concatenated genes tend to reflect the organismal phylogeny (Soria-‐Carrasco et al., 2007), perhaps only full genome sequences may reflect geographical isolation in the strain collection of S. ruber. This could result in accordance with the taxa segregation that correlates with the average nucleotide or amino-‐acid identity of shared genes (Konstantinidis &
Tiedje, 2005).However, the still sparse database of full genomes, makes the metabolomic approach a fast and less expensive alternative for revealing prokaryotic biogeography, with the added value of being discriminative at different levels at the geographical scale.
It seems clear that different studied regions led to the isolation of strains sharing common metabolic traits, such as, the distinct production of sulfonolipid derivates. However, differences were generally related to quantitative composition yields, rather than qualitative production of distinct compounds.
Nevertheless, at the molecular level, these facts could be also attributed to transcriptional or posttranscriptional regulations rather than composition changes in genes at the genomic level. In addition, the metabolic differences correlated with the geographical areas, influenced perhaps by environmental conditions such as climate and distance, since Peruvian and Mediterranean strains were found as the most different.
Results and Discussion: Chapter 1
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The major forces for these differences may be related to their distinct response to the environmental conditions of the sites where they had been isolated, since, for example, the Peruvian salterns are not only over 10,000 km away from the rest of the sampling sites, but they are also at an altitude of 3,500 m.
At this site, the temperature changes and solar radiation are clearly different from those at sea level.
Thus, discriminative metabolites identified as terpenoids or fatty acids might be related to membrane components, being the latter the most adaptable molecules in response to environmental conditions (Denich et al., 2003; Sajbidor, 1997). In brief, ICR-‐FT/MS approach showed to have a higher resolution power for revealing fine-‐scale diversity, bearing a great potential for the study of biogeographical patterns in many other non-‐extremophilic microorganisms.
CHAPTER 2
Results and Discussion: Chapter 2
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CHAPTER 2: Survival and response to adverse conditions in S. ruber M8 and M31 strains
2.1. Background
As described in the previous chapter, the use of the metabolomic approach by high-‐field ion cyclotron Fourier transform mass spectrometry (ICR-‐FT/MS) allowed to discern biogeographical patterns in S. ruber populations worldwide distributed. This permitted a non-‐targeted search for special metabolic traits considered as relevant in the organism’s phenotype, showing a higher resolution power than the molecular techniques previously applied in S. ruber population studies (Antón et al., 2008; Rosselló-‐Mora et al., 2008).
In this chapter, a combination of conventional cultivation methods, molecular techniques and a metabolomic approach were applied to study the main growth features of the closest S. ruber strains (M8 and M31) hitherto isolated and sequenced (Peña et al., 2010). Special attention has been focused on the changes occurring in the transition from the exponential growth to the stationary phase, and the response to three major different environmental stresses in which S. ruber may be exposed in its natural hypersaline habitat : i) oxygen depletion, ii) dilution of salts present in the culture medium, and iii) decrease in the incubation temperature.
2.2. Growth curves
Growth curves are widely used in Microbiology to study the increase in population size or biomass of a given microorganism. In liquid culture, most of bacterial growth displays a characteristic four-‐phase pattern of growth. The initial lag phase comprises a period of slow growth in which the bacteria are adapting to the new incubation conditions. This phase is followed by a logarithmic phase in which the growth is optimum and the cell numbers increase at ever increasing rates with time. After, a stationary phase occurs, when culture enters in a steady-‐state equilibrium in which the rate of cell growth is balanced by the rate of cell death. The death phase occurs because of a loss of limiting nutrients (due to their incorporation into cells during log-‐
phase growth) or a build-‐up of toxins (due to their release during log-‐phase growth, e.g., fermentative products).