Xueyang Feng

Cell-free protein expression of norovirus virus-like-particles.

Hepatitis B is a major disease that chronically infects millions of people in the world, especially in developing countries. Currently, one of the effective vaccines to prevent Hepatitis B is the Hepatitis B Small Antigen (HBsAg), which is mainly produced by the recombinant yeast Saccharomyces cerevisiae. In order to bring down the price, which is still too high for people in developing countries to afford, it is important to understand key cellular processes that limit protein expression. In this study, we took advantage of yeast knockout collection and screened 194 S. cerevisiae strains with single gene knocked out in four major steps of the protein secretory pathway, i.e., endoplasmic-reticulum (ER)-associated protein degradation, protein folding, unfolded protein response and translocation and exocytosis. The screening showed that the single deletion of YPT32, SBH1, and HSP42 led to the most significant increase of HBsAg expression over the wild type while the deletion of IRE1 led to a profound decrease of HBsAg expression. The synergistic effects of gene knockout and gene overexpression were next tested. We found that simultaneously deleting YPT32 and overexpressing IRE1 led to a 2.12-fold increase in HBsAg expression over the wild type strain. The results of this study revealed novel genetic targets of protein secretory pathways that could potentially improve the manufacturing of broad scope vaccines in a cost-effective way using recombinant S. cerevisiae.

Life science is entering a new era of petabyte-level sequencing data. Converting such “big data” to biological insights represents a huge challenge for computational analysis. To this end, we developed DeepMetabolism, a biology-guided deep learning system to predict cell phenotypes from transcriptomics data. By integrating unsupervised pre-training with supervised training, DeepMetabolism is able to predict phenotypes with high accuracy (PCC>0.92), high speed (100 GB data using a single GPU), and high robustness (tolerate up to 75% noise). We envision DeepMetabolism to bridge the gap between genotype and phenotype and to serve as a springboard for applications in synthetic biology and precision medicine.

Sub‐cellular compartments create specialized reaction chambers in eukaryotes. These compartments provide favorable micro‐environments for many metabolic processes. Recently, metabolic engineers have explored the concept of pathway compartmentalization to enhance the performance of metabolic pathways. This strategy offers many unique advantages, including (i) increased local concentrations of enzymes and substrates, (ii) accessing alternate substrate pools, (iii) separation from competing reactions, and (iv) isolation of harmful intermediates or conditions needed for the pathway. In this review, the method of localizing metabolic pathways into specific organelles as well as the benefits of pathway compartmentalization in terms of enhancing the production of value‐added chemicals is discussed.

Metabolic engineering of industrial microorganisms to produce chemicals, fuels, and drugs has attracted increasing interest as it provides an environment-friendly and renewable route that does not depend on depleting petroleum sources. However, the microbial metabolism is so complex that metabolic engineering efforts often have difficulty in achieving a satisfactory yield, titer, or productivity of the target chemical. To overcome this challenge, ¹³C Metabolic Flux Analysis (¹³C-MFA) has been developed to investigate rigorously the cell metabolism and quantify the carbon flux distribution in central metabolic pathways. In the past decade, ¹³C-MFA has been widely used in academic labs and the biotechnology industry to pinpoint the key issues related to microbial-based chemical production and to guide the development of the appropriate metabolic engineering strategies for improving the biochemical production. In this chapter we introduce the basics of ¹³C-MFA and illustrate how ¹³C-MFA has been applied to synergize with metabolic engineering to identify and tackle the rate-limiting steps in biochemical production.

Multiplex and multi-directional control of metabolic pathways is crucial for metabolic engineering to improve product yield of fuels, chemicals, and pharmaceuticals. To achieve this goal, artificial transcriptional regulators such as CRISPR-based transcription regulators have been developed to specifically activate or repress genes of interest. Here, we found that by deploying guide RNAs to target on DNA sites at different locations of genetic cassettes, we could use just one synthetic CRISPR-based transcriptional regulator to simultaneously activate and repress gene expressions. By using the pairwise datasets of guide RNAs and gene expressions, we developed a data-driven predictive model to rationally design this system for fine-tuning expression of target genes. We demonstrated that this system could achieve programmable control of metabolic fluxes when using yeast to produce versatile chemicals. We anticipate that this master CRISPR-based transcription regulator will be a valuable addition to the synthetic biology toolkit for metabolic engineering, speeding up the design-build-test cycle in industrial biomanufacturing as well as generating new biological insights on the fates of eukaryotic cells.

Multiplex and multi-directional control of metabolic pathways is crucial for metabolic engineering to improve product yield of fuels, chemicals, and pharmaceuticals. To achieve this goal, artificial transcriptional regulators such as CRISPR-based transcription regulators have been developed to specifically activate or repress genes of interest. Here, we found that by deploying guide RNAs to target on DNA sites at different locations of genetic cassettes, we could use just one synthetic CRISPR-based transcriptional regulator to simultaneously activate and repress gene expressions. By using the pairwise datasets of guide RNAs and gene expressions, we developed a data-driven predictive model to rationally design this system for fine-tuning expression of target genes. We demonstrated that this system could achieve programmable control of metabolic fluxes when using yeast to produce versatile chemicals. We anticipate that this master CRISPR-based transcription regulator will be a valuable addition to the synthetic biology toolkit for metabolic engineering, speeding up the “design-build-test” cycle in industrial biomanufacturing as well as generating new biological insights on the fates of eukaryotic cells.

With the breakthroughs in biomolecular engineering and synthetic biology, many valuable biologically active compound and commodity chemicals have been successfully manufactured using cell-based approaches in the past decade. However, because of the high complexity of cell metabolism, the identification and optimization of rate-limiting metabolic pathways for improving the product yield is often difficult, which represents a significant and unavoidable barrier of traditional in vivo metabolic engineering. Recently, some in vitro engineering approaches were proposed as alternative strategies to solve this problem. In brief, by reconstituting a biosynthetic pathway in a cell-free environment with the supplement of cofactors and substrates, the performance of each biosynthetic pathway could be evaluated and optimized systematically. Several value-added products, including chemicals, nutraceuticals, and drug precursors, have been biosynthesized as proof-of-concept demonstrations of in vitro metabolic engineering. This mini-review summarizes the recent progresses on the emerging topic of in vitro metabolic engineering and comments on the potential application of cell-free technology to speed up the "design-build-test" cycles of biomanufacturing.

The fermentation inhibitors from the pretreatment of lignocellulosic materials, e.g., acetic acid and furfural, are notorious due to their negative effects on the cell growth and chemical production. However, the metabolic reprogramming of the cells under these stress conditions, especially metabolic response for resistance to mixed inhibitors, has not been systematically investigated and remains mysterious. Therefore, in this study, ¹³C metabolic flux analysis (¹³C-MFA), a powerful tool to elucidate the intracellular carbon flux distributions, has been applied to two Saccharomyces cerevisiae strains with different tolerances to the inhibitors under acetic acid, furfural, and mixed (i.e., acetic acid and furfural) stress conditions to unravel the key metabolic responses. By analyzing the intracellular carbon fluxes as well as the energy and cofactor utilization under different conditions, we uncovered varied metabolic responses to different inhibitors. Under acetate stress, ATP and NADH production was slightly impaired, while NADPH tended towards overproduction. Under furfural stress, ATP and cofactors (including both NADH and NADPH) tended to be overproduced. However, under dual-stress condition, production of ATP and cofactors was severely impaired due to synergistic stress caused by the simultaneous addition of two fermentation inhibitors. Such phenomenon indicated the pivotal role of the energy and cofactor utilization in resisting the mixed inhibitors of acetic acid and furfural. Based on the discoveries, valuable insights are provided to improve the tolerance of S. cerevisiae strain and further enhance lignocellulosic fermentation.