Effect of hydrogen peroxide production and the Fenton reaction on membrane composition of Streptococcus pneumoniae

AbstractAs part of its aerobic metabolism, Streptococcus pneumoniae generates high levels of H2O2 by pyruvate oxidase (SpxB), which can be further reduced to yield the damaging hydroxyl radicals via the Fenton reaction. A universal conserved adaptation response observed among bacteria is the adjustment of the membrane fatty acids to various growth conditions. The aim of the present study was to reveal the effect of endogenous reactive oxygen species (ROS) formation on membrane composition of S. pneumoniae. Blocking carbon aerobic metabolism, by growing the bacteria at anaerobic conditions or by the truncation of the spxB gene, resulted in a significant enhancement in fatty acid unsaturation, mainly cis-vaccenic acid. Moreover, reducing the level of OH· by growing the bacteria at acidic pH, or in the presence of an OH· scavenger (salicylate), resulted in increased fatty acid unsaturation, similar to that obtained under anaerobic conditions. RT-PCR results demonstrated that this change does not originate from a change in mRNA expression level of the fatty acid synthase II genes. We suggest that endogenous ROS play an important regulatory role in membrane adaptation, allowing the survival of this anaerobic organism at aerobic environments of the host

Differences in Membrane Fluidity and Fatty Acid Composition between Phenotypic Variants of Streptococcus pneumoniae

Phase variation in the colonial opacity of Streptococcus pneumoniae has been implicated as a factor in the pathogenesis of pneumococcal disease. This study examined the relationship between membrane characteristics and colony morphology in a few selected opaque-transparent couples of S. pneumoniae strains carrying different capsular types. Membrane fluidity was determined on the basis of intermolecular excimerization of pyrene and fluorescence polarization of 1,6-diphenyl 1,3,5-hexatriene (DPH). A significant decrease, 16 to 26% (P ≤ 0.05), in the excimerization rate constant of the opaque variants compared with that of the transparent variants was observed, indicating higher microviscosity of the membrane of bacterial cells in the opaque variants. Liposomes prepared from phospholipids of the opaque phenotype showed an even greater decrease, 27 to 38% (P ≤ 0.05), in the pyrene excimerization rate constant compared with that of liposomes prepared from phospholipids of bacteria with the transparent phenotype. These findings agree with the results obtained with DPH fluorescence anisotropy, which showed a 9 to 21% increase (P ≤ 0.001) in the opaque variants compared with the transparent variants. Membrane fatty acid composition, determined by gas chromatography, revealed that the two variants carry the same types of fatty acids but in different proportions. The trend of modification points to the presence of a lower degree of unsaturated fatty acids in the opaque variants compared with their transparent counterparts. The data presented here show a distinct correlation between phase variation and membrane fluidity in S. pneumoniae. The changes in membrane fluidity most probably stem from the observed differences in fatty acid composition

Metabolite Profiling and Integrative Modeling Reveal Metabolic Constraints for Carbon Partitioning under Nitrogen-Starvation in the Green Alga Haematococcus pluvialis.

The green alga Haematococcus pluvialis accumulates large amounts of the antioxidant astaxanthin under inductive stress conditions, such as nitrogen starvation. The response to nitrogen starvation and high-light leads to the accumulation of carbohydrates and fatty acids, as well as increased activity of the tricarboxylic acid cycle. Although the behavior of individual pathways is well-investigated, little is known about the systemic effects of the stress-response mechanism. Here we present time-resolved metabolite, enzyme activity, and physiological data that capture the metabolic response of H. pluvialis under nitrogen starvation and high-light. The data were integrated into a putative genome-scale model of the green alga to in silico test the hypothesis of underlying carbon partitioning. The model-based hypothesis testing reinforces the involvement of starch degradation to support fatty acid synthesis in the later stages of the stress response. In addition, our findings support a possible mechanism for the involvement of the increased activity of the tricarboxylic acid cycle in carbon repartitioning. Finally, the in vitro experiments and the in silico modeling presented here emphasize the predictive power of large-scale integrative approaches to pinpoint metabolic adjustment to changing environments

Combined network analysis and machine learning allows the prediction of metabolic pathways from tomato metabolomics data.

The identification and understanding of metabolic pathways is a key aspect in crop improvement and drug design. The common approach for their detection is based on gene annotation and ontology. Correlation-based network analysis, where metabolites are arranged into network formation, is used as a complentary tool. Here, we demonstrate the detection of metabolic pathways based on correlation-based network analysis combined with machine-learning techniques. Metabolites of known tomato pathways, non-tomato pathways, and random sets of metabolites were mapped as subgraphs onto metabolite correlation networks of the tomato pericarp. Network features were computed for each subgraph, generating a machine-learning model. The model predicted the presence of the β-alanine-degradation-I, tryptophan-degradation-VII-via-indole-3-pyruvate (yet unknown to plants), the β-alanine-biosynthesis-III, and the melibiose-degradation pathway, although melibiose was not part of the networks. In vivo assays validated the presence of the melibiose-degradation pathway. For the remaining pathways only some of the genes encoding regulatory enzymes were detected

Combined network analysis and machine learning allows the prediction of metabolic pathways from tomato metabolomics data.

The identification and understanding of metabolic pathways is a key aspect in crop improvement and drug design. The common approach for their detection is based on gene annotation and ontology. Correlation-based network analysis, where metabolites are arranged into network formation, is used as a complentary tool. Here, we demonstrate the detection of metabolic pathways based on correlation-based network analysis combined with machine-learning techniques. Metabolites of known tomato pathways, non-tomato pathways, and random sets of metabolites were mapped as subgraphs onto metabolite correlation networks of the tomato pericarp. Network features were computed for each subgraph, generating a machine-learning model. The model predicted the presence of the β-alanine-degradation-I, tryptophan-degradation-VII-via-indole-3-pyruvate (yet unknown to plants), the β-alanine-biosynthesis-III, and the melibiose-degradation pathway, although melibiose was not part of the networks. In vivo assays validated the presence of the melibiose-degradation pathway. For the remaining pathways only some of the genes encoding regulatory enzymes were detected

Additional File 1: Table S1.

Normalized full metabolic dataset. Table S2. Seed weight in response to salinity for both seasons. Table S3. Putative QTLs for maturation percent. Table S4. Putative QTLs for RMC in SDF. Table S5. Putative QTLs for RMC in SDS. (XLSX 882 kb

Additional File 2:

QTL Map of co-localized metabolite and germination QTLs in SDS. (PDF 230 kb

Additional File 3:

Correlation networks of 2010 for SDF. (PDF 545 kb