Uracil content of 16S rRNA of thermophilic and psychrophilic prokaryotes correlates inversely with their optimal growth temperatures

We report here the finding of a highly significant inverse correlation of the uracil content of 16S rRNA and the optimum growth temperature (T(opt)) of cultured thermophilic and psychrophilic prokaryotes. This correlation was significantly different from the weaker correlations between the contents of other nucleotides and T(opt). Analysis of the 16S rRNA secondary structure regions revealed a fall in the A:U base-pair content in step with the increase in T(opt) that was much steeper than that of mismatched base-pairs, which are thermodynamically less stable. These findings indicate that the 16S rRNA sequences of thermophiles and psychrophiles are under a strong thermo-adaptive pressure, and that structure–function constraints play a crucial role in determining their 16S rRNA nucleotide composition. The derived relationship between uracil content and T(opt) was used to develop an algorithm to predict the T(opt) values of uncultured prokaryotes lacking cultured close relatives and belonging to the phyla predominantly containing thermophiles. This algorithm may be useful in guiding the design of cultivation conditions for hitherto uncultured microbes

Reconciliation of Genome-Scale Metabolic Reconstructions for Comparative Systems Analysis

In the past decade, over 50 genome-scale metabolic reconstructions have been built for a variety of single- and multi- cellular organisms. These reconstructions have enabled a host of computational methods to be leveraged for systems-analysis of metabolism, leading to greater understanding of observed phenotypes. These methods have been sparsely applied to comparisons between multiple organisms, however, due mainly to the existence of differences between reconstructions that are inherited from the respective reconstruction processes of the organisms to be compared. To circumvent this obstacle, we developed a novel process, termed metabolic network reconciliation, whereby non-biological differences are removed from genome-scale reconstructions while keeping the reconstructions as true as possible to the underlying biological data on which they are based. This process was applied to two organisms of great importance to disease and biotechnological applications, Pseudomonas aeruginosa and Pseudomonas putida, respectively. The result is a pair of revised genome-scale reconstructions for these organisms that can be analyzed at a systems level with confidence that differences are indicative of true biological differences (to the degree that is currently known), rather than artifacts of the reconstruction process. The reconstructions were re-validated with various experimental data after reconciliation. With the reconciled and validated reconstructions, we performed a genome-wide comparison of metabolic flexibility between P. aeruginosa and P. putida that generated significant new insight into the underlying biology of these important organisms. Through this work, we provide a novel methodology for reconciling models, present new genome-scale reconstructions of P. aeruginosa and P. putida that can be directly compared at a network level, and perform a network-wide comparison of the two species. These reconstructions provide fresh insights into the metabolic similarities and differences between these important Pseudomonads, and pave the way towards full comparative analysis of genome-scale metabolic reconstructions of multiple species

Exploring the metabolic network of the epidemic pathogen Burkholderia cenocepacia J2315 via genome-scale reconstruction

<p>Abstract</p> <p>Background</p> <p><it>Burkholderia cenocepacia </it>is a threatening nosocomial epidemic pathogen in patients with cystic fibrosis (CF) or a compromised immune system. Its high level of antibiotic resistance is an increasing concern in treatments against its infection. Strain <it>B. cenocepacia </it>J2315 is the most infectious isolate from CF patients. There is a strong demand to reconstruct a genome-scale metabolic network of <it>B. cenocepacia </it>J2315 to systematically analyze its metabolic capabilities and its virulence traits, and to search for potential clinical therapy targets.</p> <p>Results</p> <p>We reconstructed the genome-scale metabolic network of <it>B. cenocepacia </it>J2315. An iterative reconstruction process led to the establishment of a robust model, <it>i</it>KF1028, which accounts for 1,028 genes, 859 internal reactions, and 834 metabolites. The model <it>i</it>KF1028 captures important metabolic capabilities of <it>B. cenocepacia </it>J2315 with a particular focus on the biosyntheses of key metabolic virulence factors to assist in understanding the mechanism of disease infection and identifying potential drug targets. The model was tested through BIOLOG assays. Based on the model, the genome annotation of <it>B. cenocepacia </it>J2315 was refined and 24 genes were properly re-annotated. Gene and enzyme essentiality were analyzed to provide further insights into the genome function and architecture. A total of 45 essential enzymes were identified as potential therapeutic targets.</p> <p>Conclusions</p> <p>As the first genome-scale metabolic network of <it>B. cenocepacia </it>J2315, <it>i</it>KF1028 allows a systematic study of the metabolic properties of <it>B. cenocepacia </it>and its key metabolic virulence factors affecting the CF community. The model can be used as a discovery tool to design novel drugs against diseases caused by this notorious pathogen.</p

A Multi-Platform Flow Device for Microbial (Co-) Cultivation and Microscopic Analysis

Novel microbial cultivation platforms are of increasing interest to researchers in academia and industry. The development of materials with specialized chemical and geometric properties has opened up new possibilities in the study of previously unculturable microorganisms and has facilitated the design of elegant, high-throughput experimental set-ups. Within the context of the international Genetically Engineered Machine (iGEM) competition, we set out to design, manufacture, and implement a flow device that can accommodate multiple growth platforms, that is, a silicon nitride based microsieve and a porous aluminium oxide based microdish. It provides control over (co-)culturing conditions similar to a chemostat, while allowing organisms to be observed microscopically. The device was designed to be affordable, reusable, and above all, versatile. To test its functionality and general utility, we performed multiple experiments with Escherichia coli cells harboring synthetic gene circuits and were able to quantitatively study emerging expression dynamics in real-time via fluorescence microscopy. Furthermore, we demonstrated that the device provides a unique environment for the cultivation of nematodes, suggesting that the device could also prove useful in microscopy studies of multicellular microorganisms

In-Vivo Expression Profiling of Pseudomonas aeruginosa Infections Reveals Niche-Specific and Strain-Independent Transcriptional Programs

Pseudomonas aeruginosa is a threatening, opportunistic pathogen causing disease in immunocompromised individuals. The hallmark of P. aeruginosa virulence is its multi-factorial and combinatorial nature. It renders such bacteria infectious for many organisms and it is often resistant to antibiotics. To gain insights into the physiology of P. aeruginosa during infection, we assessed the transcriptional programs of three different P. aeruginosa strains directly after isolation from burn wounds of humans. We compared the programs to those of the same strains using two infection models: a plant model, which consisted of the infection of the midrib of lettuce leaves, and a murine tumor model, which was obtained by infection of mice with an induced tumor in the abdomen. All control conditions of P. aeruginosa cells growing in suspension and as a biofilm were added to the analysis. We found that these different P. aeruginosa strains express a pool of distinct genetic traits that are activated under particular infection conditions regardless of their genetic variability. The knowledge herein generated will advance our understanding of P. aeruginosa virulence and provide valuable cues for the definition of prospective targets to develop novel intervention strategies

Genome-Scale Reconstruction and Analysis of the Pseudomonas putida KT2440 Metabolic Network Facilitates Applications in Biotechnology

A cornerstone of biotechnology is the use of microorganisms for the efficient production of chemicals and the elimination of harmful waste. Pseudomonas putida is an archetype of such microbes due to its metabolic versatility, stress resistance, amenability to genetic modifications, and vast potential for environmental and industrial applications. To address both the elucidation of the metabolic wiring in P. putida and its uses in biocatalysis, in particular for the production of non-growth-related biochemicals, we developed and present here a genome-scale constraint-based model of the metabolism of P. putida KT2440. Network reconstruction and flux balance analysis (FBA) enabled definition of the structure of the metabolic network, identification of knowledge gaps, and pin-pointing of essential metabolic functions, facilitating thereby the refinement of gene annotations. FBA and flux variability analysis were used to analyze the properties, potential, and limits of the model. These analyses allowed identification, under various conditions, of key features of metabolism such as growth yield, resource distribution, network robustness, and gene essentiality. The model was validated with data from continuous cell cultures, high-throughput phenotyping data, 13C-measurement of internal flux distributions, and specifically generated knock-out mutants. Auxotrophy was correctly predicted in 75% of the cases. These systematic analyses revealed that the metabolic network structure is the main factor determining the accuracy of predictions, whereas biomass composition has negligible influence. Finally, we drew on the model to devise metabolic engineering strategies to improve production of polyhydroxyalkanoates, a class of biotechnologically useful compounds whose synthesis is not coupled to cell survival. The solidly validated model yields valuable insights into genotype–phenotype relationships and provides a sound framework to explore this versatile bacterium and to capitalize on its vast biotechnological potential

Dynamics of reductive genome evolution in mitochondria and obligate intracellular microbes.

Reductive evolution in mitochondria and obligate intracellular microbes has led to a significant reduction in their genome size and guanine plus cytosine content (GC). We show that genome shrinkage during reductive evolution in prokaryotes follows an exponential decay pattern and provide a method to predict the extent of this decay on an evolutionary timescale. We validated predictions by comparison with estimated extents of genome reduction known to have occurred in mitochondria and Buchnera aphidicola, through comparative genomics and by drawing on available fossil evidences. The model shows how the mitochondrial ancestor would have quickly shed most of its genome, shortly after its incorporation into the protoeukaryotic cell and prior to codivergence subsequent to the split of eukaryotic lineages. It also predicts that the primary rickettsial parasitic event would have occurred between 180 and 425 million years ago (MYA), an event of relatively recent evolutionary origin considering the fact that Rickettsia and mitochondria evolved from a common alphaproteobacterial ancestor. This suggests that the symbiotic events of Rickettsia and mitochondria originated at different time points. Moreover, our model results predict that the ancestor of Wigglesworthia glossinidia brevipalpis, dated around the time of origin of its symbiotic association with the tsetse fly (50-100 MYA), was likely to have been an endosymbiont itself, thus supporting an earlier proposition that Wigglesworthia, which is currently a maternally inherited primary endosymbiont, evolved from a secondary endosymbiont

Genome-scale metabolic network analysis of the opportunistic pathogen Pseudomonas aeruginosa PAO1.

Pseudomonas aeruginosa is a major life-threatening opportunistic pathogen that commonly infects immunocompromised patients. This bacterium owes its success as a pathogen largely to its metabolic versatility and flexibility. A thorough understanding of P. aeruginosa's metabolism is thus pivotal for the design of effective intervention strategies. Here we aim to provide, through systems analysis, a basis for the characterization of the genome-scale properties of this pathogen's versatile metabolic network. To this end, we reconstructed a genome-scale metabolic network of Pseudomonas aeruginosa PAO1. This reconstruction accounts for 1,056 genes (19% of the genome), 1,030 proteins, and 883 reactions. Flux balance analysis was used to identify key features of P. aeruginosa metabolism, such as growth yield, under defined conditions and with defined knowledge gaps within the network. BIOLOG substrate oxidation data were used in model expansion, and a genome-scale transposon knockout set was compared against in silico knockout predictions to validate the model. Ultimately, this genome-scale model provides a basic modeling framework with which to explore the metabolism of P. aeruginosa in the context of its environmental and genetic constraints, thereby contributing to a more thorough understanding of the genotype-phenotype relationships in this resourceful and dangerous pathogen

Ex vivo transcriptional profiling reveals a common set of genes important for the adaptation of Pseudomonas aeruginosa to chronically infected host sites.

The opportunistic bacterium Pseudomonas aeruginosa is a major nosocomial pathogen causing both devastating acute and chronic persistent infections. During the course of an infection, P.  aeruginosa rapidly adapts to the specific conditions within the host. In the present study, we aimed at the identification of genes that are highly expressed during biofilm infections such as in chronically infected lungs of patients with cystic fibrosis (CF), burn wounds and subcutaneous mouse tumours. We found a common subset of differentially regulated genes in all three in vivo habitats and evaluated whether their inactivation impacts on the bacterial capability to form biofilms in vitro and to establish biofilm-associated infections in a murine model. Additive effects on biofilm formation and host colonization were discovered by the combined inactivation of several highly expressed genes. However, even combined inactivation was not sufficient to abolish the establishment of an infection completely. These findings can be interpreted as evidence that either redundant traits encode functions that are essential for in vivo survival and chronic biofilm infections and/or bacterial adaptation is considerably achieved independently of transcription levels. Supplemental screens, will have to be applied in order to identify the minimal set of key genes essential for the establishment of chronic infectious diseases.The opportunistic bacterium Pseudomonas aeruginosa is a major nosocomial pathogen causing both devastating acute and chronic persistent infections. During the course of an infection, P.  aeruginosa rapidly adapts to the specific conditions within the host. In the present study, we aimed at the identification of genes that are highly expressed during biofilm infections such as in chronically infected lungs of patients with cystic fibrosis (CF), burn wounds and subcutaneous mouse tumours. We found a common subset of differentially regulated genes in all three in vivo habitats and evaluated whether their inactivation impacts on the bacterial capability to form biofilms in vitro and to establish biofilm-associated infections in a murine model. Additive effects on biofilm formation and host colonization were discovered by the combined inactivation of several highly expressed genes. However, even combined inactivation was not sufficient to abolish the establishment of an infection completely. These findings can be interpreted as evidence that either redundant traits encode functions that are essential for in vivo survival and chronic biofilm infections and/or bacterial adaptation is considerably achieved independently of transcription levels. Supplemental screens, will have to be applied in order to identify the minimal set of key genes essential for the establishment of chronic infectious diseases