Characterization of an evolved carotenoids hyper-producer of Saccharomyces cerevisiae through bioreactor parameter optimization and Raman spectroscopy

Abstract

An evolutionary engineering approach for enhancing heterologous carotenoids production in an engineered Saccharomyces cerevisiae strain was used previously to isolate several carotenoids hyper-producers from the evolved populations. β-Carotene production was characterized in the parental and one of the evolved carotenoids hyper-producers (SM14) using bench-top bioreactors to assess the impact of pH, aeration, and media composition on β-carotene production levels. The results show that with maintaining a low pH and increasing the carbon-to-nitrogen ratio (C:N) from 8.8 to 50 in standard YNB medium, a higher β-carotene production level at 25.52 ± 2.15 mg β-carotene g(-1) (dry cell weight) in the carotenoids hyper-producer was obtained. The increase in C:N ratio also significantly increased carotenoids production in the parental strain by 298 % [from 5.68 ± 1.24 to 22.58 ± 0.11 mg β-carotene g(-1) (dcw)]. In this study, it was shown that Raman spectroscopy is capable of monitoring β-carotene production in these cultures. Raman spectroscopy is adaptable to large-scale fermentations and can give results in near real-time. Furthermore, we found that Raman spectroscopy was also able to measure the relative lipid compositions and protein content of the parental and SM14 strains at two different C:N ratios in the bioreactor. The Raman analysis showed a higher total fatty acid content in the SM14 compared with the parental strain and that an increased C:N ratio resulted in significant increase in total fatty acid content of both strains. The data suggest a positive correlation between the yield of β-carotene per biomass and total fatty acid content of the cell.

Effects of Seawater on Carotenoid Production and Lipid Content of Engineered Saccharomyces cerevisiae

“…We previously demonstrated that increasing carbon-to-nitrogen (C:N) ratio from 8.8 to 50 improved carotenoid production of engineered yeast (including SM14) in benchtop bioreactors [14]. To determine if increasing the C:N ratio also increases carotenoid production in nutrient-reduced conditions, we compared the β-carotene production of SM14 in 0.1× YNB media with the normal C:N ratio (8.8) (use 5 g ammonium sulfate L −1 in the media as described in Materials and Methods) and high C:N ratio (50) (use 0.88 g ammonium sulfate L −1 in the media while keeping the concentration of other constituents the same as described in Materials and Methods) in freshwater, 1/3× seawater, and seawater ( Figure 3).…”

“…were selected based on their darker red color [12]. In subsequent work, we optimized bioreactor conditions for production using freshwater [14]. However, industrial fermentation requires a large amount of water, which generates pressures on freshwater [15].…”

Economic improvement of continuous pharmaceutical production via the optimal control of a multifeed bioreactor

“…Luna‐Flores et al were successful in developing and modeling the beta‐carotene production of a Xanthophyllomyces dendrorhous in both batch and fed‐batch production modes . Works by Reyes et al and Olson et al have shown that through chromosomal integration and adaptive evolution of a Saccharomyces cerevisiae SM14 strain it is possible to produce beta‐carotene in batch culture . Figure shows in detail the path added to the SM14 strain, using a branching fatty‐acid path from the production of acetyl‐CoA in the cell to make the desired beta‐carotene product.…”

“…43 Works by Reyes et al and Olson et al have shown that through chromosomal integration and adaptive evolution of a Saccharomyces cerevisiae SM14 strain it is possible to produce beta-carotene in batch culture. 44,45 Figure 3 shows in detail the path added to the SM14 strain, using a branching fatty-acid path from the production of acetyl-CoA in the cell to make the desired beta-carotene product. This strain, developed by Reyes et al, will be the focal point of this work.…”

Characterizing metabolic stress-induced phenotypes ofSynechocystisPCC6803 with Raman spectroscopy

“…In addition, the analysis is label free, requires minimal or no sample preparation, and there is no spectral interference from water (Zu et al, 2014;Freedman et al, 2016;Zu, Athamneh & Senger, 2016). The application of Raman spectroscopy is an expanding field and it includes fermentation monitoring (Sivakesava, Irudayaraj & Demirci, 2001;Ewanick et al, 2013;Olson et al, 2016;Zu et al, 2016), detection and identification of microorganisms (Nelson, Manoharan & Sperry, 1992;Kirschner et al, 2001;Pahlow et al, 2015), monitoring the kinetics of germination of individual Clostridium difficile spores (Wang et al, 2015) and detection of its toxins (Koya et al, 2018). It has been also demonstrated that Raman spectroscopy can be used in near-real time phenotyping of Escherichia coli exposed to alcohol (Zu et al, 2014;Zu, Athamneh & Senger, 2016) and antibiotics (Athamneh et al, 2014), single cell phenotyping (Wu et al, 2011;Serrano et al, 2014;Sun et al, 2015;García-Timermans et al, 2019), and characterizing phenotypic differences among E. coli enriched for 1-butanol tolerance (Freedman et al, 2016;Zu et al, 2014).…”