IV. RESULTS AND DISCUSSION
2.7.2. Clustering analysis
2.7.2. Clustering analysis
The set of 341 masses was evaluated by building a similarity dendrogram elaborated with all data, but considering only the presence/absence of the masses (Figure 30). Although M8 and M31 strains formed their own clusters, inter-‐cluster similarities differences were observed. According to metabolomic analyses, M8 and M31 shared about 90% of characters. Also, as in PLS-‐DA models, samples under dilution stress showed the less similarity respect to the control conditions, and were grouped in a separate cluster, whereas temperature and oxygen at 40 h appeared on a branch close to the control condition in both strains (Fig 30).
Despite this analysis considers only the presence of masses and not their intensity values, the fact that dilution samples were grouped in an independent cluster is consistent with the previous metabolomic analyses where the osmotic stress was the stress condition with more significance than the others conditions (see Fig. 24). As mentioned in previous sections, S. ruber requires at least 15% for their optimal growth (Antón et al. 2002), so the growth at lower salinity could involve, among others, inactivation of membrane-‐
bound enzymes which are sensitive to the salt concentration in the medium, requiring high salt concentration for their optimal activity (Ventosa et al., 1998). In addition, many intracellular enzymes could be inactive since most S. ruber enzymes have their optimal activity at high concentrations of intracellular K+, which would be affected at low salinities (Oren & Mana, 2002). Also, the fact that temperature and oxygen at 40 h appears close to the control suggests that in both cases several adaptative processes can occur to modify their physiological properties, distinguishing them from the rest of samples but not from the control condition (Fig. 28). Thus, at low temperature, cells could be in a viable state (Coutard et al., 2007) as has been proven by the culturabilty changes (Figs 19 and 20) whereas cells could survive to the lack oxygen and low pH by photophosphorylation processes, as ocurrs in Halobacteria (Hartmann et al., 1980).
Thus, MALDI-‐TOF MS analysis permited the determination of the phenotypic response under stress conditions in the S. ruber strains. This phenotyphical response in most cases coincided with the metabolomic analysis, becoming in a rapid tool for phenotypic screening and recognition of transient metabolic states.
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Figure 30: MALDI-‐TOF similarity dendrogram based on binary data. Dendogram showing the Euclidean distance by considering the presence-‐absence of masses. The control condition is marked with an asterisk. Dotted boxes show the clear similarity of diluted samples which are in an independent cluster of the control, whereas grey boxes show the similarity of low temperature sample (40 hr) respect to control condition in M8, which was not observed in M31.
2.8. Conclusions
A metabolomic study by means of high resolution mass spectrometry (ICR-‐FT/MS) was performed to understand whether the modifications of the environmental conditions were mirrored by a metabolic change of the S. ruber cells, in a similar approach previously used for a biogeographycal study (Chapter 1). In addition, the study was complemented by means of some physiological data as culturablity, total cell numbers, and FISH-‐detectable cells (as an indication of its ribosomal content). During the stationary phase, only 9% of the DAPI stained cells grew onto the culture plates, whereas the FISH counts only dropped to 90%.
Gradual loss of bacterial culturability during stationary phase could be the result of the enhancing the cell capacity to manage oxidative stress, increasing the oxidized proteins and loosing gradually the ability of cells to reproduce (Nyström, 2001). But, another model suggests that the apparent loss of viability of starved cells is a programmed and adaptive response in which the cells enter a reversible non-‐culturable state i.e. the theory of the induction of viable but non-‐culturable (VBNC) cells (Nyström, 2001). Bacteria enter into this
“dormant” state in response to one or more environmental stresses which might otherwise ultimately be lethal to the cell (Oliver, 1999).
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Similarly, all stress conditions implied an abrupt drop in the culturability of both strains, reaching the minimal values after 16 h, but they retained similar FISH detection rates as occurred in the exponential–stationary phase transition. Contrarily to the anoxia and dilution stresses, the prolonged incubation for about 40 h at low temperature implied a 100% recovery of culturability. This fact could not be correlated to the metabolomic changes observed, since the differentiation between T16 and T40 was not possible. In addition, this phenomenon was observed even when the time of incubation at low temperature was prolonged up to 30 days (Fig. 20) suggesting that, under low temperature incubation, S. ruber may generate a survival strategy to re-‐adapt to the new environmental conditions, increasing the stability of cells after extended cold incubation (Weichart & Kjelleberg, 1996) and recovering the culturability as occurred in species such as V.
parahaemolyticus (Coutard et al., 2007).
In all the cases, the qualitative metabolome composition of both strains in the same metabolic conditions was at least 95% identical, and only <5% of the metabolites were unique in one or another strain. This is consistent with the fact that M8 and M31 shared around 90% of their genes, or in other words, there are 10% strain-‐specific genes (Peña et al., 2010). In this sense, previous metabolomic analysis have indicated that the cellular soluble and extracellular fractions of M8 contained more metabolites involved in amino acids, carbohydrates and fatty acids pathways than the equivalent fractions of M31 (Peña et al., 2010). In this thesis, isolating the common masses and focusing on those that were quantitatively characteristic (increased and/or decreased in their relative intensities), the degree of dissimilarity of the metabolomes was defined.
Among all conditions assayed, and particularly in the proposed models (Fig. 21 and Fig. 23), the metabolome composition during anoxia showed less significant differences respect to the control state, whereas the dilution and temperature stresses, and the transition from the exponential to the stationary phase, showed clearer common metabolome shifts. However, the reduced modifications of the metabolome under oxygen depletion in the soluble cellular fraction (Fig. 23) did not imply that there was no response to anoxia, as significant differences to the control state were found in the extracellular fraction (Table 17 and Figures S3-‐
S4). In this regard, the changes observed may not be only due to the oxygen depletion of the media, but also to a decrease in the pH from 7.2 to 5.8. Salinibacter ruber showed a decrease in the growth yields when pH was reduced to 6.0 (Antón et al., 2002). The strict aerobic nature of S. ruber, together with the growth inhibition due to acidification, suggest that the changes in the metabolomic composition of the soluble cellular fraction may be also related to the difficulties in its energy gain yields for maintaining an active metabolism since the ions, in particular the proton and /or the sodium electrochemical gradients across the membrane, are crucial for the bioenergetic conditions.
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The effect of 1 unit pH difference between cytoplasm and external medium is 59 mV at 25ºC, and 70mV at 80ºC and a large difference of pH (Δ pH) can only be maintained with a membrane that has very low proton permeability (Konings et al., 2002). However, as in Halobacteria, the energy production in the absence of oxygen could be accomplished by photophosphorylation (Hartmann et al., 1980). S. ruber retinal proteins (xanthorhodopsin) located in the cell membrane could mediate the first step in energy transduction, the conversion of light energy into a electrochemical gradient, emerging apparently in response to environmental conditions (Balashov & Lanyi, 2007).
ICR-‐FT/MS approach has been suitable to visualize, in a high dynamic range and with precision,
thousands of relevant metabolites out of the immense chemical diversity that was dynamically changing during the stress situations. However, accurate metabolite identification and differentiation at the isomer level can only be undertaken using classical analytical chemistry approaches. Altogether, the changes in the balance of molecules involved in the cell membrane components, which maintain an optimal fluidity and viscosity of the membrane, were more important than those occurring intracellularly. In this regard, other significant membrane changes such as outer and inner protein-‐membrane and LPS patterns were not detected during stress conditions. Only specific differences between strains were observed which not rule out that other changes, at functional level of these compounds may be occurring. In this regard, MALDI-‐TOF MS analysis showed small phenotypic differences in the protein pattern during the different studied stress conditions.
Specific differences in the protein patterns among both strains are consistent with their genomic differences and also with previous studies which showed that the main metabolomic differences among M8 and M31 strains are related to molecules released to the medium or loosely attached to the cell surface that could have been released during the sampling processing (Peña et al., 2010). Nevertheless, metagenomic analysis of saturated brines revealed the presence of three highly variable regions or metagenomic islands in the S.
ruber genome. These regions were constituted by genes involved in cell surface polysaccharide biosynthesis of the cell wall and DNA-‐related enzymes, which suggested that the variation at the level of cell envelopes in an environment devoid of grazing pressure probably reflecting a global strategy of survival, for example to escape phage predation (Pašic et al., 2009).
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Environmental changes may have diverse effects on the function of membrane associated enzymes, including those required for the synthesis of envelope components, such as lipid A and peptidoglycan (DiRusso et al.,
CHAPTER 3
Results and Discussion: Chapter 3
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CHAPTER 3: Dispersal mechanisms of extremely halophilic microorganisms