Simulating androgen receptor selection in designer yeast

Dynamics design of a non-natural transcription factor responding to androst-4-ene-3,17-dione

The production of androst-4-ene-3,17-dione (AD) by the steroidal microbial cell factory requires transcription factors (TFs) to participate in metabolic regulation. However, microbial cell factory lacks effective TFs that can respond to AD in its metabolic pathway. Additionally, finding and obtaining natural TFs that specifically respond to AD is a complex and onerous task. In this study, we devised an artificial TF that responds to AD, termed AdT, based on structure-guided molecular dynamics (MD) simulation. According to MD analysis of the conformational changes of AdT after binding to AD, an LBD in which the N- and C-termini exhibited convergence tendencies was used as a microswitch to guide the assembly of a DNA-binding domain lexA, a linker (GGGGS)2, and a transcription activation domain B42 into an artificial TF. As a proof of design, a AD biosensor was designed and constructed in yeast on the basis of the ligand-binding domain (LBD) of hormone receptor. In addition, the transcription factor activity of AdT was increased by 1.44-fold for its variant F320Y. Overall, we created non-natural TF elements for AD microbial cell factory, and expected that the design TF strategy will be applied to running in parallel to the signaling machinery of the host cell.

Modular Coculture to Reduce Substrate Competition and Off-Target Intermediates in Androstenedione Biosynthesis

Substrate competition within a metabolic network constitutes a common challenge in microbial biosynthesis system engineering, especially if indispensable enzymes can produce multiple intermediates from a single substrate. Androstenedione (4AD) is a central intermediate in the production of a series of steroidal pharmaceuticals; however, its yield via the coexpression of 3β-hydroxysteroid dehydrogenase (3β-HSD) and 17α-hydroxylase/17,20-lyase (CYP17A1) in a microbial chassis affords a nonlinear pathway in which these enzymes compete for substrates and produce structurally similar unwanted intermediates, thereby reducing 4AD yields. To avoid substrate competition, we split the competing 3β-HSD and CYP17A1 pathway components into two separate Yarrowia lipolytica strains to linearize the pathway. This spatial segregation increased substrate availability for 3β-HSD in the upstream strain, consequently decreasing the accumulation of the unwanted intermediate 17-hydroxypregnenolone (17OHP5) from 94.8 ± 4.4% in single-chassis monocultures to 24.8 ± 12.6% in cocultures of strains expressing 3β-HSD and CYP17A1 separately. Orthologue screening to increase CYP17A1 catalytic efficiency and the preferential production of desired intermediates increased the biotransformation capacity in the downstream pathway, further decreasing 17OHP5 accumulation to 3.9%. Furthermore, nitrogen limitation induced early 4AD accumulation (final titer, 7.71 mg/L). This study provides a framework for reducing intrapathway competition between essential enzymes during natural product biosynthesis as well as a proof-of-concept platform for linear steroid production.

Yeast-based evolutionary modeling of androgen receptor mutations and natural selection

Cancer progression is associated with the evolutionary accumulation of genetic mutations that are biologically significant. Mutations of the androgen receptor (AR) are associated with the development of prostate cancer (PCa) by responding to non-androgenic hormones, and the lack of annotations in their responsiveness to hormone ligands remains a daunting challenge. Here, we have used a yeast reporter system to quickly evaluate the responsiveness of all fifty clinical AR mutations to a variety of steroidal ligands including dihydrotestosterone (DHT), 17β-estradiol (E2), progesterone (PROG), and cyproterone acetate (CPA). Based on an AR-driven reporter that synthesizes histidine, a basic amino acid required for yeast survival and propagation, the yeast reporter system enabling clonal selection was further empowered by combining with a random DNA mutagenesis library to simulate the natural evolution of AR gene under the selective pressures of steroidal ligands. In a time-frame of 1-2 weeks, 19 AR mutants were identified, in which 11 AR mutants were validated for activation by tested steroidal compounds. The high efficiency of our artificial evolution strategy was further evidenced by a sequential selection that enabled the discovery of multipoint AR mutations and evolution directions under the pressure of steroidal ligands. In summary, our designer yeast is a portable reporter module that can be readily adapted to streamline high-throughput AR-compound screening, used as a PCa clinical reference, and combined with additional bioassay systems to further extend its potential.