Ingeniería Metabólica de Sistemas en el hongo industrial Ashbya gosypii: impulsando la producción de riboflavina, lípidos y nucleósidos

[EN]Ashbya gossypii is a filamentous fungus used by the industry to produce riboflavin. During the last years, the natural capability of this microorganism to produce the vitamin has been increased by different metabolic engineering approaches. Since this fungus presents general industrial advantages in upstream, downstream and the fermentation processes it has been recently proposed for other large scale applications such as recombinant proteins, bioethanol and folic acid production. The objectives of this work are not only focus on the improvement of riboflavin but also in exploiting some unique capabilities of this fungus to produce novel industrial products. For this aim we developed different systems metabolic engineering approaches, we generated the first genome scale metabolic model of Ashbya, we obtained systems biology data and set up novel synthetic biology tools. We generated strains producing 5 times more riboflavin than the wild type by combinational engineering of the riboflavin and purine pathway. Additionally, we modified Ashbya to make it accumulate huge amounts of lipids. Those lipids were enriched in desired fatty acids by engineering the elongation and desaturation systems, which generates candidate strains for biodiesel production. Besides, synthetic biology allowed us to produce polyunsaturated fatty acids, omega 3 and omega 6 in A. gossypii. We also proved that the metabolism of A. gossypii can be optimized to overproduce inosine and guanosine, important industrial precursors of flavour enhancers. To sum up, this works propose A. gossypii as a promising industrial fungus for white biotechnology approaches, not only riboflavin production but also fatty acids derivatives and flavour enhancers

Unraveling fatty acid transport and activation mechanisms in Yarrowia lipolytica

AbstractFatty acid (FA) transport and activation have been extensively studied in the model yeast species Saccharomyces cerevisiae but have rarely been examined in oleaginous yeasts, such as Yarrowia lipolytica. Because the latter begins to be used in biodiesel production, understanding its FA transport and activation mechanisms is essential. We found that Y. lipolytica has FA transport and activation proteins similar to those of S. cerevisiae (Faa1p, Pxa1p, Pxa2p, Ant1p) but mechanism of FA peroxisomal transport and activation differs greatly with that of S. cerevisiae. While the ScPxa1p/ScPxa2p heterodimer is essential for growth on long-chain FAs, ΔYlpxa1 ΔYlpxa2 is not impaired for growth on FAs. Meanwhile, ScAnt1p and YlAnt1p are both essential for yeast growth on medium-chain FAs, suggesting they function similarly. Interestingly, we found that the ΔYlpxa1 ΔYlpxa2 ΔYlant1 mutant was unable to grow on short-, medium-, or long-chain FAs, suggesting that YlPxa1p, YlPxa2p, and YlAnt1p belong to two different FA degradation pathways. We also found that YlFaa1p is involved in FA storage in lipid bodies and that FA remobilization largely depended on YlFat1p, YlPxa1p and YlPxa2p. This study is the first to comprehensively examine FA intracellular transport and activation in oleaginous yeast

Evaluating DFHBI-responsive RNA light-up aptamers as fluorescent reporters for gene expression

Protein-based fluorescent reporters have been widely used to characterize and localize biological processes in living cells. However, these reporters may have certain drawbacks for some applications, such as transcription-based studies or biological interactions with fast dynamics. In this context, RNA nanotechnology has emerged as a promising alternative, suggesting the use of functional RNA molecules as transcriptional fluorescent reporters. RNA-based aptamers can bind to nonfluorescent small molecules to activate their fluorescence. However, their performance as reporters of gene expression in living cells has not been fully characterized, unlike protein-based reporters. Here, we investigate the performance of three RNA light-up aptamers─F30-2xdBroccoli, tRNA-Spinach, and Tornado Broccoli─as fluorescent reporters for gene expression in Escherichia coli and compare them to a protein reporter. We examine the activation range and effect on the cell growth of RNA light-up aptamers in time-course experiments and demonstrate that these aptamers are suitable transcriptional reporters over time. Using flow cytometry, we compare the variability at the single-cell level caused by the RNA fluorescent reporters and protein-based reporters. We found that the expression of RNA light-up aptamers produced higher variability in a population than that of their protein counterpart. Finally, we compare the dynamical behavior of these RNA light-up aptamers and protein-based reporters. We observed that RNA light-up aptamers might offer faster dynamics compared to a fluorescent protein in E. coli. The implementation of these transcriptional reporters may facilitate transcription-based studies, gain further insights into transcriptional processes, and expand the implementation of RNA-based circuits in bacterial cells

Biovalorisation of crude glycerol and xylose into xylitol by oleaginous yeast Yarrowia lipolytica

Background Xylitol is a commercially important chemical with multiple applications in the food and pharmaceutical industries. According to the US Department of Energy, xylitol is one of the top twelve platform chemicals that can be produced from biomass. The chemical method for xylitol synthesis is however, expensive and energy intensive. In contrast, the biological route using microbial cell factories offers a potential cost-effective alternative process. The bioprocess occurs under ambient conditions and makes use of biocatalysts and biomass which can be sourced from renewable carbon originating from a variety of cheap waste feedstocks. Result In this study, biotransformation of xylose to xylitol was investigated using Yarrowia lipolytica, an oleaginous yeast which was firstly grown on a glycerol/glucose for screening of co-substrate, followed by media optimisation in shake flask, scale up in bioreactor and downstream processing of xylitol. A two-step medium optimization was employed using central composite design and artificial neural network coupled with genetic algorithm. The yeast amassed a concentration of 53.2 g/L xylitol using pure glycerol (PG) and xylose with a bioconversion yield of 0.97 g/g. Similar results were obtained when PG was substituted with crude glycerol (CG) from the biodiesel industry (titer: 50.5 g/L; yield: 0.92 g/g). Even when xylose from sugarcane bagasse hydrolysate was used as opposed to pure xylose, a xylitol yield of 0.54 g/g was achieved. Xylitol was successfully crystallized from PG/xylose and CG/xylose fermentation broths with a recovery of 39.5 and 35.3%, respectively. Conclusion To the best of the author’s knowledge, this study demonstrates for the first time the potential of using Y. lipolytica as a microbial cell factory for xylitol synthesis from inexpensive feedstocks. The results obtained are competitive with other xylitol producing organisms

Machine learning in biohydrogen production: a review

Biohydrogen is emerging as a promising carbon-neutral and sustainable energy carrier with high energy yield to replace conventional fossil fuels. However, biohydrogen commercial uptake is mainly hindered by the supply side. As a result, various operating parameters must be optimized to realize biohydrogen commercial uptake on a large-scale. Recently, machine learning algorithms have demonstrated the ability to handle large amounts of data while requiring less in-depth knowledge of the system and being capable of adapting to evolving circumstances. This review critically reviews the role of machine learning in categorizing and predicting data related to biohydrogen production. The accuracy and potential of different machine learning algorithms are reported. Also, the practical implications of machine learning models to realize biohydrogen uptake by the transportation sector are discussed. The review indicates that machine learning algorithms can successfully model non-linear and complex interactions between operational and performance parameters in biohydrogen production. Additionally, machine learning algorithms can help researchers identify the most efficient methods for producing biohydrogen, leading to a more sustainable and cost-effective energy source

A molecular toolkit of cross-feeding strains for engineering synthetic yeast communities

Engineered microbial consortia often have enhanced system performance and robustness compared with single-strain biomanufacturing production platforms. However, few tools are available for generating co-cultures of the model and key industrial host Saccharomyces cerevisiae. Here we engineer auxotrophic and overexpression yeast strains that can be used to create co-cultures through exchange of essential metabolites. Using these strains as modules, we engineered two- and three-member consortia using different cross-feeding architectures. Through a combination of ensemble modelling and experimentation, we explored how cellular (for example, metabolite production strength) and environmental (for example, initial population ratio, population density and extracellular supplementation) factors govern population dynamics in these systems. We tested the use of the toolkit in a division of labour biomanufacturing case study and show that it enables enhanced and tuneable antioxidant resveratrol production. We expect this toolkit to become a useful resource for a variety of applications in synthetic ecology and biomanufacturing

Bioproduction of succinic acid from xylose by engineered Yarrowia lipolytica without pH control

Background Xylose is the most prevalent sugar available in hemicellulose fraction of lignocellulosic biomass (LCB) and of great interest for the green economy. Unfortunately, most of the cell factories cannot inherently metabolize xylose as sole carbon source. Yarrowia lipolytica is a non-conventional yeast that produces industrially important metabolites. The yeast is able to metabolize a large variety of substrates including both hydrophilic and hydrophobic carbon sources. However, Y. lipolytica lacks effective metabolic pathway for xylose uptake and only scarce information is available on utilization of xylose. For the economica feasibility of LCB-based biorefineries, effective utilization of both pentose and hexose sugars is obligatory. Results In the present study, succinic acid (SA) production from xylose by Y. lipolytica was examined. To this end, Y. lipolytica PSA02004 strain was engineered by overexpressing pentose pathway cassette comprising xylose reductase (XR), xylitol dehydrogenase (XDH) and xylulose kinase (XK) gene. The recombinant strain exhibited a robust growth on xylose as sole carbon source and produced substantial amount of SA. The inhibition of cell growth and SA formation was observed above 60 g/L xylose concentration. The batch cultivation of the recombinant strain in a bioreactor resulted in a maximum biomass concentration of 7.3 g/L and SA titer of 11.2 g/L with the yield of 0.19 g/g. Similar results in terms of cell growth and SA production were obtained with xylose-rich hydrolysate derived from sugarcane bagasse. The fed-batch fermentation yielded biomass concentration of 11.8 g/L (OD600: 56.1) and SA titer of 22.3 g/L with a gradual decrease in pH below 4.0. Acetic acid was obtained as a main by-product in all the fermentations. Conclusion The recombinant strain displayed potential for bioconversion of xylose to SA. Further, this study provided a new insight on conversion of lignocellulosic biomass into value-added products. To the best of our knowledge, this is the first study on SA production by Y. lipolytica using xylose as a sole carbon source