Metabolic flux analysis model for optimizing xylose conversion into ethanol by the natural C5-fermenting yeast Candida shehatae

A genome-scale metabolic model of the effect of dissolved oxygen on 1,3-propanediol fermentation by Klebsiella pneumoniae

Although 1,3-propanediol (1,3-PD) is usually considered an anaerobic fermentation product from glycerol by Klebsiella pneumoniae, microaerobic conditions proved to be more conducive to 1,3-PD production. In this study, a genome-scale metabolic model (GSMM) specific to K. pneumoniae KG2, a high 1.3-PD producer, was constructed. The iZY1242 model contains 2090 reactions, 1242 genes and 1433 metabolites. The model was not only able to accurately characterise cell growth, but also accurately simulate the fed-batch 1,3-PD fermentation process. Flux balance analyses by iZY1242 was performed to dissect the mechanism of stimulated 1,3-PD production under microaerobic conditions, and the maximum yield of 1,3-PD on glycerol was 0.83 mol/mol under optimal microaerobic conditions. Combined with experimental data, the iZY1242 model is a useful tool for establishing the best conditions for microaeration fermentation to produce 1,3-PD from glycerol in K. pneumoniae.

Valorization of rice straw, sugarcane bagasse and sweet sorghum bagasse for the production of bioethanol and phenylacetylcarbinol

Open burning of agricultural residues causes numerous complications including particulate matter pollution in the air, soil degradation, global warming and many more. Since they possess bio-conversion potential, agro-industrial residues including sugarcane bagasse (SCB), rice straw (RS), corncob (CC) and sweet sorghum bagasse (SSB) were chosen for the study. Yeast strains, Candida tropicalis, C. shehatae, Saccharomyces cerevisiae, and Kluyveromyces marxianus var. marxianus were compared for their production potential of bioethanol and phenylacetylcarbinol (PAC), an intermediate in the manufacture of crucial pharmaceuticals, namely, ephedrine, and pseudoephedrine. Among the substrates and yeasts evaluated, RS cultivated with C. tropicalis produced significantly (p ≤ 0.05) higher ethanol concentration at 15.3 g L^-1 after 24 h cultivation. The product per substrate yield (Y_eth/s) was 0.38 g g^-1 with the volumetric productivity (Q_p) of 0.64 g L^-1 h^-1 and fermentation efficiency of 73.6% based on a theoretical yield of 0.51 g ethanol/g glucose. C. tropicalis grown in RS medium produced 0.303 U mL^-1 pyruvate decarboxylase (PDC), a key enzyme that catalyzes the production of PAC, with a specific activity of 0.400 U mg^-1 protein after 24 h cultivation. This present study also compared the whole cells biomass of C. tropicalis with its partially purified PDC preparation for PAC biotransformation. The whole cells C. tropicalis PDC at 1.29 U mL^-1 produced an overall concentration of 62.3 mM PAC, which was 68.4% higher when compared to partially purified enzyme preparation. The results suggest that the valorization of lignocellulosic residues into bioethanol and PAC will not only aid in mitigating the environmental challenge posed by their surroundings but also has the potential to improve the bioeconomy.

Microbial containment device: A platform for comprehensive analysis of microbial metabolism without sample preparation

Metabolomics is a mainstream strategy for investigating microbial metabolism. One emerging application of metabolomics is the systematic quantification of metabolic boundary fluxes – the rates at which metabolites flow into and out of cultured cells. Metabolic boundary fluxes can capture complex metabolic phenotypes in a rapid assay, allow computational models to be built that predict the behavior of cultured organisms, and are an emerging strategy for clinical diagnostics. One advantage of quantifying metabolic boundary fluxes rather than intracellular metabolite levels is that it requires minimal sample processing. Whereas traditional intracellular analyses require a multi-step process involving extraction, centrifugation, and solvent exchange, boundary fluxes can be measured by simply analyzing the soluble components of the culture medium. To further simplify boundary flux analyses, we developed a custom 96-well sampling system—the Microbial Containment Device (MCD)—that allows water-soluble metabolites to diffuse from a microbial culture well into a bacteria-free analytical well via a semi-permeable membrane. The MCD was designed to be compatible with the autosamplers present in commercial liquid chromatography-mass spectrometry systems, allowing metabolic fluxes to be analyzed with minimal sample handling. Herein, we describe the design, evaluation, and performance testing of the MCD relative to traditional culture methods. We illustrate the utility of this platform, by quantifying the unique boundary fluxes of four bacterial species and demonstrate antibiotic-induced perturbations in their metabolic activity. We propose the use of the MCD for enabling single-step metabolomics sample preparation for microbial identification, antimicrobial susceptibility testing, and other metabolic boundary flux applications where traditional sample preparation methods are impractical.

Role of Dissimilative Pathway of Komagataella phaffii (Pichia pastoris): Formaldehyde Toxicity and Energy Metabolism

Komagataella phaffii (aka Pichia pastoris) is a yeast able to grow in methanol as the sole carbon and energy source. This substrate is converted into formaldehyde, a toxic intermediary that can either be assimilated to biomass or dissimilated to CO₂ through the enzymes formaldehyde dehydrogenase (FLD) and formate dehydrogenase, also producing energy in the form of NADH. The dissimilative pathway has been described as an energy producing and a detoxifying route, but conclusive evidence has not been provided for this. In order to elucidate this theory, we generated mutants lacking the FLD activity (Δfld1) and used flux analysis to evaluate the metabolic impact of this disrupted pathway. Unexpectedly, we found that the specific growth rate of the Δfld1 strain was only slightly lower (92%) than the control. In contrast, the sensitivity to formaldehyde pulses (up to 8mM) was significantly higher in the Δfld1 mutant strain and was associated with a higher maintenance energy. In addition, the intracellular flux estimation revealed a high metabolic flexibility of K. phaffii in response to the disrupted pathway. Our results suggest that the role of the dissimilative pathway is mainly to protect the cells from the harmful effect of formaldehyde, as they were able to compensate for the energy provided from this pathway when disrupted.

Linear programming based gene expression model (LPM-GEM) predicts the carbon source for Bacillus subtilis

BACKGROUND

Elucidating cellular metabolism led to many breakthroughs in biotechnology, synthetic biology, and health sciences. To date, deriving metabolic fluxes by ¹³C tracer experiments is the most prominent approach for studying metabolic fluxes quantitatively, often with high accuracy and precision. However, the technique has a high demand for experimental resources. Alternatively, flux balance analysis (FBA) has been employed to estimate metabolic fluxes without labeling experiments. It is less informative but can benefit from the low costs and low experimental efforts and gain flux estimates in experimentally difficult conditions. Methods to integrate relevant experimental data have been emerged to improve FBA flux estimations. Data from transcription profiling is often selected since it is easy to generate at the genome scale, typically embedded by a discretization of differential and non-differential expressed genes coding for the respective enzymes.

RESULT

We established the novel method Linear Programming based Gene Expression Model (LPM-GEM). LPM-GEM linearly embeds gene expression into FBA constraints. We implemented three strategies to reduce thermodynamically infeasible loops, which is a necessary prerequisite for such an omics-based model building. As a case study, we built a model of B. subtilis grown in eight different carbon sources. We obtained good flux predictions based on the respective transcription profiles when validating with ¹³C tracer based metabolic flux data of the same conditions. We could well predict the specific carbon sources. When testing the model on another, unseen dataset that was not used during training, good prediction performance was also observed. Furthermore, LPM-GEM outperformed a well-established model building methods.

CONCLUSION

Employing LPM-GEM integrates gene expression data efficiently. The method supports gene expression-based FBA models and can be applied as an alternative to estimate metabolic fluxes when tracer experiments are inappropriate.

Xylose and yeasts: A story beyond xylitol production

Six different yeasts were used to study their metabolism of glucose and xylose, and mainly their capacity to produce ethanol and xylitol. The strains used were Candida guilliermondii, Debaryomyces hansenii, Saccharomyces cerevisiae, Kluyveromyces marxianus, Meyerozyma guilliermondii and Clavispora lusitaniae, four isolated from a rural mezcal fermentation facility. All of them produced ethanol when the substrate was glucose. When incubated in a medium containing xylose instead of glucose, only K. marxianus and M. guilliermondii were able to produce ethanol from xylose. On the other hand, all of them could produce some xylitol from xylose, but the most active in this regard were K. marxianus, M. guilliermondii, Candida lusitaniae, and C. guilliermondii with the highest amount of xylitol produced. The capacity of all strains to take up glucose and xylose was also studied. Xylose, in different degrees, produced a redox imbalance in all yeasts. Respiration capacity was also studied with glucose or xylose, where C. guilliermondii, D. hansenii, K. marxianus and M. guilliermondii showed higher cyanide resistant respiration when grown in xylose. Neither xylose transport nor xylitol production were enhanced by an acidic environment (pH 4), which can be interpreted as the absence of a proton/sugar symporter mechanism for xylose transport, except for C. lusitaniae. The effects produced by xylose and their magnitude depend on the background of the studied yeast and the conditions in which these are studied.

High-solid enzymatic hydrolysis of sugarcane bagasse and ethanol production in repeated batch process using column reactors

Alkaline sulfite pretreated sugarcane bagasse was enzymatically hydrolyzed in a packed-bed column reactor and a bubble column reactor was evaluated to produce ethanol from the hydrolysate. Initial solid loadings of 9-16% were used in column reactor in the hydrolysis step, and the use of lower value (9%) resulted in 41 g L^-1 of glucose in the hydrolysate, corresponding to 87% of cellulose hydrolysis yield. This yield was reduced to 65% for a solid loading of 16%, corresponding to a glucose concentration of 54 g L^-1. Subsequently, Saccharomyces cerevisiae and Scheffersomyces stipitis were used for ethanol production in medium based on hydrolysate previously obtained, using different aeration flowrates (0.3, 0.5 and 0.7 vvm). In simple batch fermentation using S. cerevisiae, higher ethanol yield (0.40 g.g^-1) and productivity (1.58 g.L^-1.h^-1) were achieved using 0.5 vvm. When S. stipitis was used in simple batch co-fermentations, the maximum ethanol productivities were obtained using 0.5 and 0.7 vvm (0.64 and 0.63 g.L^-1.h^-1, respectively). Successive repeated batches resulted in average ethanol concentration of 38 g.L^-1 and fermentation efficiency of 82%, when using S. cerevisiae. For S. stipitis, those values were, respectively, 36 g.L^-1 and 50%, with volumetric productivity increased along the cycles. Thus, the potential of the bioreactors as simple systems for use in the biological steps of biorefineries was demonstrated.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13205-021-02932-3.

Synergisms of machine learning and constraint-based modeling of metabolism for analysis and optimization of fermentation parameters

Recent noteworthy advances in the development of high-performing microbial and mammalian strains have enabled the sustainable production of bio-economically valuable substances such as bio-compounds, biofuels, and biopharmaceuticals. However, to obtain an industrially viable mass-production scheme, much time and effort are required. The robust and rational design of fermentation processes requires analysis and optimization of different extracellular conditions and medium components, which have a massive effect on growth and productivity. In this regard, knowledge- and data-driven modeling methods have received much attention. Constraint-based modeling (CBM) is a knowledge-driven mathematical approach that has been widely used in fermentation analysis and optimization due to its capabilities of predicting the cellular phenotype from genotype through high-throughput means. On the other hand, machine learning (ML) is a data-driven statistical method that identifies the data patterns within sophisticated biological systems and processes, where there is inadequate knowledge to represent underlying mechanisms. Furthermore, ML models are becoming a viable complement to constraint-based models in a reciprocal manner when one is used as a pre-step of another. As a result, more predictable model is produced. This review highlights the applications of CBM and ML independently and the combination of these two approaches for analyzing and optimizing fermentation parameters. This article is protected by copyright. All rights reserved.

Machine learning applied for metabolic flux-based control of micro-aerated fermentations in bioreactors

Various bio-based processes depend on controlled micro-aerobic conditions to achieve a satisfactory product yield. However, the limiting oxygen concentration varies according to the micro-organism employed, while for industrial applications, there is no cost-effective way of measuring it at low levels. This study proposes a machine learning procedure within a metabolic flux-based control strategy (SUPERSYS_MCU) to address this issue. The control strategy used simulations of a genome-scale metabolic model to generate a surrogate model in the form of an artificial neural network, to be used in a micro-aerobic fermentation strategy (MF-ANN). The meta-model provided setpoints to the controller, allowing adjustment of the inlet air flow to control the oxygen uptake rate. The strategy was evaluated in micro-aerobic batch cultures employing industrial Saccharomyces cerevisiae yeast, with defined medium and glucose as the carbon source, as a case study. The performance of the proposed control scheme was compared with a conventional fermentation and with three previously reported micro-aeration strategies, including respiratory quotient-based control and constant air flow rate. Due to maintenance of the oxidative balance at the anaerobiosis threshold, the MF-ANN provided volumetric ethanol productivity of 4.16 g·L^-1 ·h^-1 and a yield of 0.48 g_ethanol .g_substrate ^-1 , which were higher than the values achieved for the other conditions studied (maximum of 3.4 g·L^-1 ·h^-1 and 0.35-0.40 g_ethanol ·g_substrate ^-1 , respectively). Due to its modular character, the MF-ANN strategy could be adapted to other micro-aerated bioprocesses.

Pentose metabolism and conversion to biofuels and high-value chemicals in yeasts

Pentose sugars are widespread in nature and two of them, D-xylose and L-arabinose belong to the most abundant sugars being the second and third by abundance sugars in dry plant biomass (lignocellulose) and in general on planet. Therefore, it is not surprising that metabolism and bioconversion of these pentoses attract much attention. Several different pathways of D-xylose and L-arabinose catabolism in bacteria and yeasts are known. There are even more common and really ubiquitous though not so abundant pentoses, D-ribose and 2-deoxy-D-ribose, the constituents of all living cells. Thus, ribose metabolism is example of endogenous metabolism whereas metabolism of other pentoses, including xylose and L-arabinose, represents examples of the metabolism of foreign exogenous compounds which normally are not constituents of yeast cells. As a rule, pentose degradation by the wild-type strains of microorganisms does not lead to accumulation of high amounts of valuable substances; however, productive strains have been obtained by random selection and metabolic engineering. There are numerous reviews on xylose and (less) L-arabinose metabolism and conversion to high value substances; however, they mostly are devoted to bacteria or the yeast Saccharomyces cerevisiae. This review is devoted to reviewing pentose metabolism and bioconversion mostly in non-conventional yeasts, which naturally metabolize xylose. Pentose metabolism in the recombinant strains of S. cerevisiae is also considered for comparison. The available data on ribose, xylose, L-arabinose transport, metabolism, regulation of these processes, interaction with glucose catabolism and construction of the productive strains of high-value chemicals or pentose (ribose) itself are described. In addition, genome studies of the natural xylose metabolizing yeasts and available tools for their molecular research are reviewed. Metabolism of other pentoses (2-deoxyribose, D-arabinose, lyxose) is briefly reviewed.

Metabolic flux analysis for metabolome data validation of naturally xylose-fermenting yeasts

BACKGROUND

Efficient xylose fermentation still demands knowledge regarding xylose catabolism. In this study, metabolic flux analysis (MFA) and metabolomics were used to improve our understanding of xylose metabolism. Thus, a stoichiometric model was constructed to simulate the intracellular carbon flux and used to validate the metabolome data collected within xylose catabolic pathways of non-Saccharomyces xylose utilizing yeasts.

RESULTS

A metabolic flux model was constructed using xylose fermentation data from yeasts Scheffersomyces stipitis, Spathaspora arborariae, and Spathaspora passalidarum. In total, 39 intracellular metabolic reactions rates were utilized validating the measurements of 11 intracellular metabolites, acquired by mass spectrometry. Among them, 80% of total metabolites were confirmed with a correlation above 90% when compared to the stoichiometric model. Among the intracellular metabolites, fructose-6-phosphate, glucose-6-phosphate, ribulose-5-phosphate, and malate are validated in the three studied yeasts. However, the metabolites phosphoenolpyruvate and pyruvate could not be confirmed in any yeast. Finally, the three yeasts had the metabolic fluxes from xylose to ethanol compared. Xylose catabolism occurs at twice-higher flux rates in S. stipitis than S. passalidarum and S. arborariae. Besides, S. passalidarum present 1.5 times high flux rate in the xylose reductase reaction NADH-dependent than other two yeasts.

CONCLUSIONS

This study demonstrated a novel strategy for metabolome data validation and brought insights about naturally xylose-fermenting yeasts. S. stipitis and S. passalidarum showed respectively three and twice higher flux rates of XR with NADH cofactor, reducing the xylitol production when compared to S. arborariae. Besides then, the higher flux rates directed to pentose phosphate pathway (PPP) and glycolysis pathways resulted in better ethanol production in S. stipitis and S. passalidarum when compared to S. arborariae.

The Ability of a Novel Strain Scheffersomyces (Syn. Candida) shehatae Isolated from Rotten Wood to Produce Arabitol

Arabitol is a polyalcohol which has about 70% of the sweetness of sucrose and an energy density of 0.2 kcal/g. Similarly to xylitol, it can be used in the food and pharmaceutical industries as a natural sweetener, a texturing agent, a dental caries reducer, and a humectant. Biotechnological production of arabitol from sugars represents an interesting alternative to chemical production. The yeast Scheffersomyces shehatae strain 20BM-3 isolated from rotten wood was screened for its ability to produce arabitol from L-arabinose, glucose, and xylose. This isolate, cultured at 28°C and 150 rpm, secreted 4.03 ± 0.00 to 7.97 ± 0.67 g/l of arabitol from 17–30 g/l of L-arabinose assimilated from a medium containing 20–80 g/l of this pentose with yields of 0.24 ± 0.00 to 0.36 ± 0.02 g/g. An optimization study demonstrated that pH 4.0, 32°C, and a shaking frequency of 150 rpm were the optimum conditions for arabitol production by the investigated strain. Under these conditions, strain 20BM-3 produced 6.2 ± 0.17 g/l of arabitol from 17.5 g/l of arabinose after 4 days with a yield of 0.35 ± 0.01 g/g. This strain also produced arabitol from glucose, giving much lower yields, but did not produce it from xylose. The new strain can be successfully used for arabitol production from abundantly available sugars found in plant biomass.

Draft Genome Sequences of the Xylose-Fermenting Yeast Scheffersomyces shehatae NBRC 1983T and a Thermotolerant Isolate of S. shehatae ATY839 (JCM 18690)

Draft genome sequences of the type strain (NBRC 1983) and a thermotolerant isolate (ATY839) of the xylose-fermenting yeast Scheffersomyces shehatae were determined. The genome sizes and presumed open reading frames were highly similar between strains NBRC 1983^T and ATY839.

Identification of strain isolated from dates (Phœnix dactylifera L.) for enhancing very high gravity ethanol production

Ethanol production from by-products of dates in very high gravity was conducted in batch fermentation using two yeasts, Saccharomyces cerevisiae and Zygosaccharomyces rouxii, as well as a native strain: an osmophilic strain of bacteria which was isolated for the first time from the juice of dates (Phoenix dactylifera L.). The phylogenetic analysis based on the 16S ribosomal RNA and gyrB sequence and physiological analysis indicated that the strain identified belongs to the genus of Bacillus, B. amyloliquefaciens. The ethanol yields produced from the syrup of dates (175 g L^-1 and 360 g L^-1 of total sugar) were 40.6% and 29.5%, respectively. By comparing the ethanol production by the isolated bacteria to that obtained using Z. rouxii and S. cerevisiae, it can be concluded that B. amyloliquefaciens was suitable for ethanol production from the syrup of dates and can consume the three types of sugar (glucose, fructose, and sucrose). Using Z. rouxii, fructose was preferentially consumed, while glucose was consumed only after fructose depletion. From this, B. amyloliquefaciens was promising for the bioethanol industry. In addition, this latter showed a good tolerance for high sugar concentration (36%), allowing ethanol production in batch fermentation at pH 5.0 and 28 °C in date syrup medium. Promising ethanol yield produced to sugar consumed were observed for the two osmotolerant microorganisms, Z. rouxii and B. amyloliquefaciens, nearly 32-33%, which were further improved when they were cocultivated, leading to an ethanol to glucose yield of 42-43%.