Enhanced hexose fermentation by Saccharomyces cerevisiae through integration of stoichiometric modeling and genetic screening

Towards a hybrid model-driven platform based on flux balance analysis and a machine learning pipeline for biosystem design

Metabolic modeling and machine learning (ML) are crucial components of the evolving next-generation tools in systems and synthetic biology, aiming to unravel the intricate relationship between genotype, phenotype, and the environment. Nonetheless, the comprehensive exploration of integrating these two frameworks, and fully harnessing the potential of fluxomic data, remains an unexplored territory. In this study, we present, rigorously evaluate, and compare ML-based techniques for data integration. The hybrid model revealed that the overexpression of six target genes and the knockout of seven target genes contribute to enhanced ethanol production. Specifically, we investigated the influence of succinate dehydrogenase (SDH) on ethanol biosynthesis in Saccharomyces cerevisiae through shake flask experiments. The findings indicate a noticeable increase in ethanol yield, ranging from 6 % to 10 %, in SDH subunit gene knockout strains compared to the wild-type strain. Moreover, in pursuit of a high-yielding strain for ethanol production, dual-gene deletion experiments were conducted targeting glycerol-3-phosphate dehydrogenase (GPD) and SDH. The results unequivocally demonstrate significant enhancements in ethanol production for the engineered strains Δsdh4Δgpd1, Δsdh5Δgpd1, Δsdh6Δgpd1, Δsdh4Δgpd2, Δsdh5Δgpd2, and Δsdh6Δgpd2, with improvements of 21.6 %, 27.9 %, and 22.7 %, respectively. Overall, the results highlighted that integrating mechanistic flux features substantially improves the prediction of gene knockout strains not accounted for in metabolic reconstructions. In addition, the finding in this study delivers valuable tools for comprehending and manipulating intricate phenotypes, thereby enhancing prediction accuracy and facilitating deeper insights into mechanistic aspects within the field of synthetic biology.

Amino acid metabolism and MAP kinase signaling pathway play opposite roles in the regulation of ethanol production during fermentation of sugarcane molasses in budding yeast

Sugarcane molasses is one of the main raw materials for bioethanol production, and Saccharomyces cerevisiae is the major biofuel-producing organism. In this study, a batch fermentation model has been used to examine ethanol titers of deletion mutants for all yeast nonessential genes in this yeast genome. A total of 42 genes are identified to be involved in ethanol production during fermentation of sugarcane molasses. Deletion mutants of seventeen genes show increased ethanol titers, while deletion mutants for twenty-five genes exhibit reduced ethanol titers. Two MAP kinases Hog1 and Kss1 controlling the high osmolarity and glycerol (HOG) signaling and the filamentous growth, respectively, are negatively involved in the regulation of ethanol production. In addition, twelve genes involved in amino acid metabolism are crucial for ethanol production during fermentation. Our findings provide novel targets and strategies for genetically engineering industrial yeast strains to improve ethanol titer during fermentation of sugarcane molasses.

Advances in Genome-Scale Metabolic Modeling toward Microbial Community Analysis of the Human Microbiome

A genome-scale metabolic model (GEM) represents metabolic pathways of an organism in a mathematical form and can be built using biochemistry and genome annotation data. GEMs are invaluable for understanding organisms since they analyze the metabolic capabilities and behaviors quantitatively and can predict phenotypes. The development of high-throughput data collection techniques led to an immense increase in omics data such as metagenomics, which expand our knowledge on the human microbiome, but this also created a need for systematic analysis of these data. In recent years, GEMs have also been reconstructed for microbial species, including human gut microbiota, and methods for the analysis of microbial communities have been developed to examine the interaction between the organisms or the host. The purpose of this review is to provide a comprehensive guide for the applications of GEMs in microbial community analysis. Starting with GEM repositories, automatic GEM reconstruction tools, and quality control of models, this review will give insights into microbe-microbe and microbe-host interaction predictions and optimization of microbial community models. Recent studies that utilize microbial GEMs and personalized models to infer the influence of microbiota on human diseases such as inflammatory bowel diseases (IBD) or Parkinson's disease are exemplified. Being powerful system biology tools for both species-level and community-level analysis of microbes, GEMs integrated with omics data and machine learning techniques will be indispensable for studying the microbiome and their effects on human physiology as well as for deciphering the mechanisms behind human diseases.

Metabolic fluxes-oriented control of bioreactors: a novel approach to tune micro-aeration and substrate feeding in fermentations

Background

Fine-tuning the aeration for cultivations when oxygen-limited conditions are demanded (such as the production of vaccines, isobutanol, 2–3 butanediol, acetone, and bioethanol) is still a challenge in the area of bioreactor automation and advanced control. In this work, an innovative control strategy based on metabolic fluxes was implemented and evaluated in a case study: micro-aerated ethanol fermentation.

Results

The experiments were carried out in fed-batch mode, using commercial Saccharomyces cerevisiae, defined medium, and glucose as carbon source. Simulations of a genome-scale metabolic model for Saccharomyces cerevisiae were used to identify the range of oxygen and substrate fluxes that would maximize ethanol fluxes. Oxygen supply and feed flow rate were manipulated to control oxygen and substrate fluxes, as well as the respiratory quotient (RQ). The performance of the controlled cultivation was compared to two other fermentation strategies: a conventional “Brazilian fuel-ethanol plant” fermentation and a strictly anaerobic fermentation (with ultra-pure nitrogen used as the inlet gas). The cultivation carried out under the proposed control strategy showed the best average volumetric ethanol productivity (7.0 g L⁻¹ h⁻¹), with a final ethanol concentration of 87 g L⁻¹ and yield of 0.46 g_ethanol g_substrate⁻¹. The other fermentation strategies showed lower yields (close to 0.40 g_ethanol g_substrate⁻¹) and ethanol productivity around 4.0 g L⁻¹ h⁻¹.

Conclusion

The control system based on fluxes was successfully implemented. The proposed approach could also be adapted to control several bioprocesses that require restrict aeration.

Genome-Scale Metabolic Modeling from Yeast to Human Cell Models of Complex Diseases: Latest Advances and Challenges

Genome-scale metabolic models (GEMs) are mathematical models that enable systematic analysis of metabolism. This modeling concept has been applied to study the metabolism of many organisms including the eukaryal model organism, the yeast Saccharomyces cerevisiae, that also serves as an important cell factory for production of fuels and chemicals. With the application of yeast GEMs, our knowledge of metabolism is increasing. Therefore, GEMs have also been used for modeling human cells to study metabolic diseases. Here we introduce the concept of GEMs and provide a protocol for reconstructing GEMs. Besides, we show the historic development of yeast GEMs and their applications. Also, we review human GEMs as well as their uses in the studies of complex diseases.

Rapid and efficient galactose fermentation by engineered Saccharomyces cerevisiae

In the important industrial yeast Saccharomyces cerevisiae, galactose metabolism requires energy production by respiration; therefore, this yeast cannot metabolize galactose under strict anaerobic conditions. While the respiratory dependence of galactose metabolism provides benefits in terms of cell growth and population stability, it is not advantageous for producing fuels and chemicals since a substantial fraction of consumed galactose is converted to carbon dioxide. In order to force S. cerevisiae to use galactose without respiration, a subunit (COX9) of a respiratory enzyme was deleted, but the resulting deletion mutant (Δcox9) was impaired in terms of galactose assimilation. Interestingly, after serial sub-cultures on galactose, the mutant evolved rapidly and was able to use galactose via fermentation only. The evolved strain (JQ-G1) produced ethanol from galactose with a 94% increase in yield and 6.9-fold improvement in specific productivity as compared to the wild-type strain. (13)C-metabolic flux analysis demonstrated a three-fold reduction in carbon flux through the TCA cycle of the evolved mutant with redirection of flux toward the fermentation pathway. Genome sequencing of the JQ-G1 strain revealed a loss of function mutation in a master negative regulator of the Leloir pathway (Gal80p). The mutation (Glu348*) in Gal80p was found to act synergistically with deletion of COX9 for efficient galactose fermentation, and thus the double deletion mutant Δcox9Δgal80 produced ethanol 2.4 times faster and with 35% higher yield than a single knockout mutant with deletion of GAL80 alone. When we introduced a functional COX9 cassette back into the JQ-G1 strain, the JQ-G1-COX9 strain showed a 33% reduction in specific galactose uptake rate and a 49% reduction in specific ethanol production rate as compared to JQ-G1. The wild-type strain was also subjected to serial sub-cultures on galactose but we failed to isolate a mutant capable of utilizing galactose without respiration. We concluded that the metabolic "death valley" (i.e. no galactose utilization by the Δcox9 mutant) is a necessary intermediate phenotype to facilitate galactose utilization without respiration in yeast. The results in this study demonstrate a promising approach for directing adaptive evolution toward fermentative metabolism and for generating evolved yeast strains with improved phenotypes under anaerobic conditions.