Vitor A. P. Martins dos Santos

Genome sequence of the ubiquitous hydrocarbon-degrading marine bacterium Alcanivorax borkumensis

Alcanivorax borkumensis is a cosmopolitan marine bacterium that uses oil hydrocarbons as its exclusive source of carbon and energy. Although barely detectable in unpolluted environments, A. borkumensis becomes the dominant microbe in oil-polluted waters. A. borkumensis SK2 has a streamlined genome with a paucity of mobile genetic elements and energy generation–related genes, but with a plethora of genes accounting for its wide hydrocarbon substrate range and efficient oil-degradation capabilities. The genome further specifies systems for scavenging of nutrients, particularly organic and inorganic nitrogen and oligo-elements, biofilm formation at the oil-water interface, biosurfactant production and niche-specific stress responses. The unique combination of these features provides A. borkumensis SK2 with a competitive edge in oil-polluted environments. This genome sequence provides the basis for the future design of strategies to mitigate the ecological damage caused by oil spills.

Gut microbiota disturbance during antibiotic therapy: a multi-omic approach

Objective Antibiotic (AB) usage strongly affects microbial intestinal metabolism and thereby impacts human health. Understanding this process and the underlying mechanisms remains a major research goal. Accordingly, we conducted the first comparative omic investigation of gut microbial communities in faecal samples taken at multiple time points from an individual subjected to β-lactam therapy. Methods The total (16S rDNA) and active (16S rRNA) microbiota, metagenome, metatranscriptome (mRNAs), metametabolome (high-performance liquid chromatography coupled to electrospray ionisation and quadrupole time-of-flight mass spectrometry) and metaproteome (ultra high performing liquid chromatography coupled to an Orbitrap MS2 instrument [UPLC-LTQ Orbitrap-MS/MS]) of a patient undergoing AB therapy for 14 days were evaluated. Results Apparently oscillatory population dynamics were observed, with an early reduction in Gram-negative organisms (day 6) and an overall collapse in diversity and possible further colonisation by ‘presumptive’ naturally resistant bacteria (day 11), followed by the re-growth of Gram-positive species (day 14). During this process, the maximum imbalance in the active microbial fraction occurred later (day 14) than the greatest change in the total microbial fraction, which reached a minimum biodiversity and richness on day 11; additionally, major metabolic changes occurred at day 6. Gut bacteria respond to ABs early by activating systems to avoid the antimicrobial effects of the drugs, while ‘presumptively’ attenuating their overall energetic metabolic status and the capacity to transport and metabolise bile acid, cholesterol, hormones and vitamins; host–microbial interactions significantly improved after treatment cessation. Conclusions This proof-of-concept study provides an extensive description of gut microbiota responses to follow-up β-lactam therapy. The results demonstrate that ABs targeting specific pathogenic infections and diseases may alter gut microbial ecology and interactions with host metabolism at a much higher level than previously assumed.

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.

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. aeruginosas 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 pathogens 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.

Industrial biotechnology of Pseudomonas putida and related species.

Since their discovery many decades ago, Pseudomonas putida and related subspecies have been intensively studied with regard to their potential application in industrial biotechnology. Today, these Gram-negative soil bacteria, traditionally known as well-performing xenobiotic degraders, are becoming efficient cell factories for various products of industrial relevance including a full range of unnatural chemicals. This development is strongly driven by systems biotechnology, integrating systems metabolic engineering approaches with novel concepts from bioprocess engineering, including novel reactor designs and renewable feedstocks.

Understanding the antimicrobial mechanism of TiO2-based nanocomposite films in a pathogenic bacterium

Titania (TiO2)-based nanocomposites subjected to light excitation are remarkably effective in eliciting microbial death. However, the mechanism by which these materials induce microbial death and the effects that they have on microbes are poorly understood. Here, we assess the low dose radical-mediated TiO2 photocatalytic action of such nanocomposites and evaluate the genome/proteome-wide expression profiles of Pseudomonas aeruginosa PAO1 cells after two minutes of intervention. The results indicate that the impact on the gene-wide flux distribution and metabolism is moderate in the analysed time span. Rather, the photocatalytic action triggers the decreased expression of a large array of genes/proteins specific for regulatory, signalling and growth functions in parallel with subsequent selective effects on ion homeostasis, coenzyme-independent respiration and cell wall structure. The present work provides the first solid foundation for the biocidal action of titania and may have an impact on the design of highly active photobiocidal nanomaterials.

3DM: Systematic analysis of heterogeneous superfamily data to discover protein functionalities

Ten years of experience with molecular class–specific information systems (MCSIS) such as with the hand‐curated G protein–coupled receptor database (GPCRDB) or the semiautomatically generated nuclear receptor database has made clear that a wide variety of questions can be answered when protein‐related data from many different origins can be flexibly combined. MCSISes revolve around a multiple sequence alignment (MSA) that includes “all” available sequences from the entire superfamily, and it has been shown at many occasions that the quality of these alignments is the most crucial aspect of the MCSIS approach. We describe here a system called 3DM that can automatically build an entire MCSIS. 3DM bases the MSA on a multiple structure alignment, which implies that the availability of a large number of superfamily members with a known three‐dimensional structure is a requirement for 3DM to succeed well. Thirteen MCSISes were constructed and placed on the Internet for examination. These systems have been instrumental in a large series of research projects related to enzyme activity or the understanding and engineering of specificity, protein stability engineering, DNA‐diagnostics, drug design, and so forth. Proteins 2010.

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.

In-silico-driven metabolic engineering of Pseudomonas putida for enhanced production of poly-hydroxyalkanoates

Here, we present systems metabolic engineering driven by in-silico modeling to tailor Pseudomonas putida for synthesis of medium chain length PHAs on glucose. Using physiological properties of the parent wild type as constraints, elementary flux mode analysis of a large-scale model of the metabolism of P. putida was used to predict genetic targets for strain engineering. Among a set of priority ranked targets, glucose dehydrogenase (encoded by gcd) was predicted as most promising deletion target. The mutant P. putida Δgcd, generated on basis of the computational design, exhibited 100% increased PHA accumulation as compared to the parent wild type, maintained a high specific growth rate and exhibited an almost unaffected gene expression profile, which excluded detrimental side effects of the modification. A second mutant strain, P. putida Δpgl, that lacked 6-phosphogluconolactonase, exhibited a substantially decreased PHA synthesis, as was also predicted by the model. The production potential of P. putida Δgcd was assessed in batch bioreactors. The novel strain showed an increase of the PHA yield (+80%), the PHA titer (+100%) and cellular PHA content (+50%) and revealed almost unaffected growth and diminished by-product formation. It was thus found superior in all relevant criteria towards industrial production. Beyond the contribution to more efficient PHA production processes at reduced costs that might replace petrochemical plastics in the future, the study illustrates the power of computational prediction to tailor microbial strains for enhanced biosynthesis of added-value compounds.

Redefining plant systems biology: from cell to ecosystem

Molecular biologists typically restrict systems biology to cellular levels. By contrast, ecologists define biological systems as communities of interacting individuals at different trophic levels that process energy, nutrient and information flows. Modern plant breeding needs to increase agricultural productivity while decreasing the ecological footprint. This requires a holistic systems biology approach that couples different aggregation levels while considering the variables that affect these biological systems from cell to community. The challenge is to generate accurate experimental data that can be used together with modelling concepts and techniques that allow experimentally verifying in silico predictions. The coupling of aggregation levels in plant sciences, termed Integral Quantification of Biological Organization (IQ(BiO)), might enhance our abilities to generate new desired plant phenotypes.