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Safety-by-design (SbD) is paramount for risk management in synthetic biology, with genetic safeguards being a key technology for its implementation. While attractive in theory, the integration of genetic safeguards into SbD strategies is rarely exercised in practice, despite technological advances. Here we question why and what might be done about it.

Tetradesmus obliquus is an oleaginous microalga with high potential for triacylglycerol production. We characterized the biochemical composition and the transcriptional landscape of T. obliquus wild-type and the starchless mutant (slm1), adapted to 16:8 h light dark (LD) cycles under nitrogen limitation. In comparison to the nitrogen replete conditions, the diurnal RNA samples from both strains also displayed a cyclic pattern, but with much less variation which could be related to a reduced transcription activity in at least the usually highly active processes. During nitrogen limitation, the wild-type continued to use starch as the preferred storage compound to store energy and carbon. Starch was accumulated to an average content of 0.25 g·gDW−1, which is higher than the maximum observed under nitrogen replete conditions. Small oscillations were observed, indicating that starch was being used as a diurnal energy storage compound, but to a lesser extent than under nitrogen replete conditions. For the slm1 mutant, TAG content was higher than for the wild-type (average steady state value was 0.26 g·gDW−1 for slm1 compared to 0.06 g·gDW−1 for the wild-type). Despite the higher TAG content in the slm1, the conversion efficiency of photons into biomass components for the slm1 was only half of the one obtained for the wild-type. This is related to the observed decrease in biomass productivity (from 1.29 gDW·L−1·day−1 for the wild-type to 0.52 gDW·L−1·day−1 for the slm1). While the transcriptome of slm1 displayed clear signs of energy generation by degrading TAG and amino-acids during the dark period, no significant variation of these metabolites could be measured. When looking through the diurnal cycle, the photosynthetic efficiency was lower for the slm1 mutant compared to the wild-type especially during the second half of the light period, where starch accumulation occurred in the wild-type. Paid Open Access

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.

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.

At the junction between systems and synthetic biology, genome streamlining provides a solid foundation both for increased understanding of cellular circuitry, and for the tailoring of microbial chassis towards innovative biotechnological applications. Iterative genomic deletions (targeted and random) helps to generate simplified, stabilized and predictable genomes, whereas multiplexing genome engineering reveals a broad functional genetic diversity. The decrease in oligo and gene synthesis costs promises effective combinatorial tools for the generation of chassis based on streamlined and tractable genomes. Here we review recent progresses in streamlining genomes through recombineering techniques aiming to generate insights into cellular mechanisms and responses towards the design and assembly of streamlined genome chassis together with new cellular modules in diverse biotechnological applications.

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.