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Background Pseudomonas putida is a metabolically versatile, genetically accessible, and stress-robust species with outstanding potential to be used as a workhorse for industrial applications. While industry recognises the importance of robustness under micro-oxic conditions for a stable production process, the obligate aerobic nature of P. putida, attributed to its inability to produce sufficient ATP and maintain its redox balance without molecular oxygen, severely limits its use for biotechnology applications. Results Here, a combination of genome-scale metabolic modelling and comparative genomics is used to pinpoint essential $$\text {O}_{2}$$ O 2 -dependent processes. These explain the inability of the strain to grow under anoxic conditions: a deficient ATP generation and an inability to synthesize essential metabolites. Based on this, several P. putida recombinant strains were constructed harbouring acetate kinase from Escherichia coli for ATP production, and a class I dihydroorotate dehydrogenase and a class III anaerobic ribonucleotide triphosphate reductase from Lactobacillus lactis for the synthesis of essential metabolites. Initial computational designs were fine-tuned by means of adaptive laboratory evolution. Conclusions We demonstrated the value of combining in silico approaches, experimental validation and adaptive laboratory evolution for microbial design by making the strictly aerobic Pseudomonas putida able to grow under micro-oxic conditions.

Pseudomonas putida KT2440 is a metabolically versatile, HV1-certified, genetically accessible, and thus interesting microbial chassis for biotechnological applications. However, its obligate aerobic nature hampers production of oxygen sensitive products and drives up costs in large scale fermentation. The inability to perform anaerobic fermentation has been attributed to insufficient ATP production and an inability to produce pyrimidines under these conditions. Addressing these bottlenecks enabled growth under micro-oxic conditions, but does not lead to growth or survival under anoxic conditions. Here, a data-driven approach was used to develop a rational design for aP. putida KT2440 derivative strain capable of anaerobic respiration. To come to the design, data derived from a genome comparison of 1628 Pseudomonas strains was combined with genome-scale metabolic modelling simulations and a transcriptome dataset of 47 samples representing 14 environmental conditions from the facultative anaerobe Pseudomonas aeruginosa. The results indicate that the implementation of anaerobic respiration inP. putida KT2440 would require at least 61 additional genes of known function, at least 8 genes encoding proteins of unknown function, and 3 externally added vitamins.

Summary By the time the complete genome sequence of the soil bacterium Pseudomonas putida KT2440 was published in 2002 (Nelson et al., 2002) this bacterium was considered a potential agent for environmental bioremediation of industrial waste and a good colonizer of the rhizosphere. However, neither the annotation tools available at that time nor the scarcely available omics data—let alone metabolic modeling and other nowadays common systems biology approaches—allowed them to anticipate the astonishing capacities that are encoded in the genetic complement of this unique microorganism. In this work we have adopted a suite of state-of-the-art genomic analysis tools to revisit the functional and metabolic information encoded in the chromosomal sequence of strain KT2440. We identified 242 new protein-coding genes and re-annotated the functions of 1548 genes, which are linked to almost 4900 PubMed references. Catabolic pathways for 92 compounds (carbon, nitrogen and phosphorus sources) that could not be accommodated by the previously constructed metabolic models were also predicted. The resulting examination not only accounts for some of the known stress tolerance traits known in P. putida but also recognizes the capacity of this bacterium to perform difficult redox reactions, thereby multiplying its value as a platform microorganism for industrial biotechnology.

Genome-scale metabolic models (GEMs) are manually curated repositories describing the metabolic capabilities of an organism. GEMs have been successfully used in different research areas, ranging from systems medicine to biotechnology. However, the different naming conventions (namespaces) of databases used to build GEMs limit model reusability and prevent the integration of existing models. This problem is known in the GEM community, but its extent has not been analyzed in depth. In this study, we investigate the name ambiguity and the multiplicity of non-systematic identifiers and we highlight the (in)consistency in their use in 11 biochemical databases of biochemical reactions and the problems that arise when mapping between different namespaces and databases. We found that such inconsistencies can be as high as 83.1%, thus emphasizing the need for strategies to deal with these issues. Currently, manual verification of the mappings appears to be the only solution to remove inconsistencies when combining models. Finally, we discuss several possible approaches to facilitate (future) unambiguous mapping.

The human gut is colonized with a myriad of microbes, with substantial interpersonal variation. This complex ecosystem is an integral part of the gastrointestinal tract and plays a major role in the maintenance of homeostasis. Its dysfunction has been correlated to a wide array of diseases, but the understanding of causal mechanisms is hampered by the limited amount of cultured microbes, poor understanding of phenotypes, and the limited knowledge about interspecies interactions. Genome-scale metabolic models (GEMs) have been used in many different fields, ranging from metabolic engineering to the prediction of interspecies interactions. We provide showcase examples for the application of GEMs for gut microbes and focus on (i) the prediction of minimal, synthetic, or defined media; (ii) the prediction of possible functions and phenotypes; and (iii) the prediction of interspecies interactions. All three applications are key in understanding the role of individual species in the gut ecosystem as well as the role of the microbiota as a whole. Using GEMs in the described fashions has led to designs of minimal growth media, an increased understanding of microbial phenotypes and their influence on the host immune system, and dietary interventions to improve human health. Ultimately, an increased understanding of the gut ecosystem will enable targeted interventions in gut microbial composition to restore homeostasis and appropriate host-microbe crosstalk.

The filamentous fungus Rhizopus delemar naturally accumulates relatively high amounts of fumarate. Although the culture conditions that increase fumarate yields are well established, the network underlying the accumulation of fumarate is not yet fully understood. We set out to increase the knowledge about fumarate accumulation in R. delemar. To this end, we combined a transcriptomics and proteomics approach to identify key metabolic pathways involved in fumarate production in R. delemar, and propose that a substantial part of the fumarate accumulated in R. delemar during nitrogen starvation results from the urea cycle due to amino acid catabolism.

Many species of microalgae produce hydrocarbons, polysaccharides, and other valuable products in significant amounts. However, large-scale production of algal products is not yet competitive against non-renewable alternatives from fossil fuel. Metabolic engineering approaches will help to improve productivity, but the exact metabolic pathways and the identities of the majority of the genes involved remain unknown. Recent advances in bioinformatics and systems-biology modeling coupled with increasing numbers of algal genome-sequencing projects are providing the means to address this. A multidisciplinary integration of methods will provide synergy for a systems-level understanding of microalgae, and thereby speed up the improvement of industrially valuable strains. In this review we highlight recent advances, challenges, their application in microalgae research, and their potential.

Pseudomonas is a highly versatile genus containing species that can be harmful to humans and plants while others are widely used for bioengineering and bioremediation. We analysed 432 sequenced Pseudomonas strains by integrating results from a large scale functional comparison using protein domains with data from six metabolic models, nearly a thousand transcriptome measurements and four large scale transposon mutagenesis experiments. Through heterogeneous data integration we linked gene essentiality, persistence and expression variability. The pan-genome of Pseudomonas is closed indicating a limited role of horizontal gene transfer in the evolutionary history of this genus. A large fraction of essential genes are highly persistent, still non essential genes represent a considerable fraction of the core-genome. Our results emphasize the power of integrating large scale comparative functional genomics with heterogeneous data for exploring bacterial diversity and versatility.

By the time the complete genome sequence of the soil bacterium Pseudomonas putida KT2440 was published in 2002 (Nelson et al., ) this bacterium was considered a potential agent for environmental bioremediation of industrial waste and a good colonizer of the rhizosphere. However, neither the annotation tools available at that time nor the scarcely available omics data-let alone metabolic modeling and other nowadays common systems biology approaches-allowed them to anticipate the astonishing capacities that are encoded in the genetic complement of this unique microorganism. In this work we have adopted a suite of state-of-the-art genomic analysis tools to revisit the functional and metabolic information encoded in the chromosomal sequence of strain KT2440. We identified 242 new protein-coding genes and re-annotated the functions of 1548 genes, which are linked to almost 4900 PubMed references. Catabolic pathways for 92 compounds (carbon, nitrogen and phosphorus sources) that could not be accommodated by the previously constructed metabolic models were also predicted. The resulting examination not only accounts for some of the known stress tolerance traits known in P. putida but also recognizes the capacity of this bacterium to perform difficult redox reactions, thereby multiplying its value as a platform microorganism for industrial biotechnology.

Aspergillus niger is a filamentous fungus that is extensively used in industrial fermentations for protein expression and the production of organic acids. Inherent biosynthetic capabilities, such as the capacity to secrete these biomolecules in high amounts, make A. niger an attractive production host. Although A. niger is renowned for this ability, the knowledge of the molecular components that underlie its production capacity, intercellular trafficking processes and secretion mechanisms is far from complete. Here, we introduce a standardized set of tools, consisting of an N-terminal GFP-actin fusion and codon optimized eforRed chromoprotein. Expression of the GFP-actin construct facilitates visualization of the actin filaments of the cytoskeleton, whereas expression of the chromoprotein construct results in a clearly distinguishable red phenotype. These experimentally validated constructs constitute the first set of standardized A. niger biomarkers, which can be used to study morphology, intercellular trafficking, and secretion phenomena.

Background The use of in silico simulations as a basis for designing artificial biological systems (and experiments to characterize them) is one of the tangible differences between Synthetic Biology and “classical” Genetic Engineering. To this end, synthetic biologists have adopted approaches originating from the traditionally non-biological fields of Nonlinear Dynamics and Systems & Control Theory. However, due to the complex molecular interactions affecting the emergent properties of biological systems, mechanistic descriptions of even the simplest genetic circuits (transcriptional feedback oscillators, bi-stable switches) produced by these methods tend to be either oversimplified, or numerically intractable. More comprehensive and realistic models can be approximated by constructing “toy” genetic circuits that provide the experimenter with some degree of control over the transcriptional dynamics, and allow for experimental set-ups that generate reliable data reflecting the intracellular biochemical state in real time. To this end, we designed two genetic circuits (basic and tunable) capable of exhibiting synchronized oscillatory green fluorescent protein (GFP) expression in small populations of Escherichia coli cells. The functionality of the basic circuit was verified microscopically. High-level visualizations of computational simulations were analyzed to determine whether the reliability and utility of a synchronized transcriptional oscillator could be enhanced by the introduction of chemically inducible repressors. Results Synchronized oscillations in GFP expression were repeatedly observed in chemically linked sub-populations of cells. Computational simulations predicted that the introduction of independently inducible repressors substantially broaden the range of conditions under which oscillations could occur, in addition to allowing the frequency of the oscillation to be tuned. Conclusions The genetic circuits described here may prove to be valuable research tools for the study of synchronized transcriptional feedback loops under a variety of conditions and experimental set-ups. We further demonstrate the benefit of using abstract visualizations to discover subtle non-linear trends in complex dynamic models with large parameter spaces.

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 the Review article ‘Green genes: bioinformatics and systems-biology innovations drive algal biotechnology’ by Maarten J.M.F. Reijnders, Ruben G.A. van Heck, Carolyn M.C. Lam, Mark A. Scaife, Vitor A.P. Martins dos Santos, Alison G. Smith, and Peter J. Schaap, which was published in the December 2014 issue of Trends in Biotechnology, Tables 1 and 2Tables 1 and 2 contained typographical errors in the footnotes, and Table 2Table 2 contained errors regarding the genome size and available proteins of Nannochloropsis species. These do not affect the conclusions mentioned in the paper.Table 2A list of selected industrially useful microalgaeSpeciesGenome sizea (Mb)Available proteinsbReported industrially relevant characteristicsaRefsChlamydomonas reinhardtii12015 144Model system for unicellular green algaeMonoraphidium neglectum6816 761Up to 21% dry weight neutral lipid under nitrogen starvation[73]Nannochloropsis gaditanaNannochloropsis oceanica342815 361242Can produce high amounts of ω-3 long-chain polyunsaturated fatty acidsUp to 50% dry weight oil content[4]Phaeodactylum tricornutum2710 673Can produce antibacterial fatty acids (9Z)-hexadecenoic acid (palmitoleic acid; C16:1 n-7) and (6Z, 9Z, 12Z)-hexadecatrienoic acid (HTA; C16:3 n-4)[74]Chlorella variabilis469831The first sequenced Chlorella genomeA model genome for understanding other Chlorella species[75]Ostreococcus tauri12.69050Smallest sequenced microalgal genome with simple cellular structureChlorella protothecoides22.97039Up to 55% dry weight lipid content in heterotrophic growthHighest published biomass yield, average 3.37 gDW L−1 h−1 in heterotrophic growth[4,76,77]Chlorella vulgarisN.a.c292Up to 42% lipid content in photobioreactor with artificial waste waterUp to 26% total lipid in dry weight in heterotrophic growth[75,78]Dunaliella salinaN.a.238Up to 10% carotenoids in dry weight; 90% β-carotene in carotenoids[79]Haematococcus pluvialisN.a.60Highest reported yield of antioxidant astaxanthin (3.8% dry weight)[80]Botryococcus braunii∼166–21130Up to 57% total lipids in dry weightContains exopolysaccharides[81–83]Neochloris oleoabundansN.a.0Up to 56% total fatty acids in dry weight under nitrogen-deprivation[10] View Table in HTML aGenome size or characteristics are according to NCBI unless otherwise specified.bEstimated protein numbers are according to UniProt unless otherwise specified.cN.a., not available.The corrected version of the text in the footnote for Table 1 is: ‘aFor distantly’ instead of ‘aor distantly’. The corrected version of Table 2Table 2 and its footnotes is given below. The authors apologize for any inconvenience these errors might have caused.The errors as indicated in this erratum have been corrected in the online original article.