De novo design of a transcription factor for a progesterone biosensor

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.

Identifying LasR Quorum Sensors with Improved Signal Specificity by Mapping the Sequence-Function Landscape

Programmable intercellular signaling using components of naturally occurring quorum sensing can allow for coordinated functions to be engineered in microbial consortia. LuxR-type transcriptional regulators are widely used for this purpose and are activated by homoserine lactone (HSL) signals. However, they often suffer from imperfect molecular discrimination of structurally similar HSLs, causing misregulation within engineered consortia containing multiple HSL signals. Here, we studied one such example, the regulator LasR from Pseudomonas aeruginosa. We elucidated its sequence-function relationship for ligand specificity using targeted protein engineering and multiplexed high-throughput biosensor screening. A pooled combinatorial saturation mutagenesis library (9,486 LasR DNA sequences) was created by mutating six residues in LasR's β5 sheet with single, double, or triple amino acid substitutions. Sort-seq assays were performed in parallel using cognate and noncognate HSLs to quantify each corresponding sensor's response to each HSL signal, which identified hundreds of highly specific variants. Sensor variants identified were individually assayed and exhibited up to 60.6-fold (p = 0.0013) improved relative activation by the cognate signal compared to the wildtype. Interestingly, we uncovered prevalent mutational epistasis and previously unidentified residues contributing to signal specificity. The resulting sensors with negligible signal crosstalk could be broadly applied to engineer bacteria consortia.

Development of a synthetic transcription factor-based S-adenosylmethionine biosensor in Saccharomyces cerevisiae

S-Adenosylmethionine (SAM) is a crucial small-molecule metabolite widely used in food and medicine. The development of high-throughput biosensors for SAM biosynthesis can significantly improve the titer of SAM. This paper constructed a synthetic transcription factor (TF)-based biosensor for SAM detecting in Saccharomyces cerevisiae. The synthetic TF, named MetJ-hER-VP16, consists of an Escherichia coli-derived DNA-binding domain MetJ, GS linker, the human estrogen receptor binding domain hER, and the viral activation domain VP16. The synthetic biosensor is capable of sensing SAM in a dose-dependent manner with fluorescence as the output. Additionally, it is tightly regulated by the inducer SAM and β-estradiol, which means that the fluorescence output is only available when both are present together. The synthetic SAM biosensor could potentially be applied for high-throughput metabolic engineering and is expected to improve SAM production.

Coronavirus: a comparative analysis of detection technologies in the wake of emerging variants

An outbreak of the coronavirus disease caused by a novel pathogen created havoc and continues to affect the entire world. As the pandemic progressed, the scientific community was faced by the limitations of existing diagnostic methods. In this review, we have compared the existing diagnostic techniques such as reverse transcription polymerase chain reaction (RT-PCR), antigen and antibody detection, computed tomography scan, etc. and techniques in the research phase like microarray, artificial intelligence, and detection using novel materials; on the prospect of sample preparation, detection procedure (qualitative/quantitative), detection time, screening efficiency, cost-effectiveness, and ability to detect different variants. A detailed comparison of different techniques showed that RT-PCR is still the most widely used and accepted coronavirus detection method despite certain limitations (single gene targeting- in context to mutations). New methods with similar efficiency that could overcome the limitations of RT-PCR may increase the speed, simplicity, and affordability of diagnosis. In addition to existing devices, we have also discussed diagnostic devices in the research phase showing high potential for clinical use. Our approach would be of enormous benefit in selecting a diagnostic device under a given scenario, which would ultimately help in controlling the current pandemic caused by the coronavirus, which is still far from over with new variants emerging.

Genetically encoded biosensors for microbial synthetic biology: From conceptual frameworks to practical applications

Genetically encoded biosensors are the vital components of synthetic biology and metabolic engineering, as they are regarded as powerful devices for the dynamic control of genotype metabolism and evolution/screening of desirable phenotypes. This review summarized the recent advances in the construction and applications of different genetically encoded biosensors, including fluorescent protein-based biosensors, nucleic acid-based biosensors, allosteric transcription factor-based biosensors and two-component system-based biosensors. First, the construction frameworks of these biosensors were outlined. Then, the recent progress of biosensor applications in creating versatile microbial cell factories for the bioproduction of high-value chemicals was summarized. Finally, the challenges and prospects for constructing robust and sophisticated biosensors were discussed. This review provided theoretical guidance for constructing genetically encoded biosensors to create desirable microbial cell factories for sustainable bioproduction.

Economic optimization of expression of soluble human epidermal growth factor in Escherichia coli

Human epidermal growth factor (hEGF) has multiple biological functions, such as promoting cell proliferation, differentiation, and migration. In addition, it is a very expensive polypeptide with attractive market prospects. However, the production of hEGF needs for high cost to manufacture polypeptide demands reinvestigations of process conditions so as to enhance economic benefits. Improving the expression of soluble hEGF is the fundamental method to reduce the cost. In this study, a non-extracellular engineered strain of expressed hEGF was constructed, using plasmid pET-22b(+) in Escherichia coli. Preliminary fermentation and high cell density cultivation were carried out in shake flasks and in a 5 L bioreactor, respectively. A high yield of 98 ± 10 mg/L of soluble hEGF and a dry cell weight (DCW) of 6.98 ± 0.3 g/L were achieved in shake flasks. Then, fermentation conditions were optimized for large-scale production, while taking into consideration the expensive equipment required for cooling and conforming to industrial standards. A yield of 285 ± 10 mg/L of soluble hEGF, a final cell density of 57.4 ± 2 g/L DCW (OD₆₀₀ 141.1 ± 4.9), and hEGF productivity of 14.3 mg/L/h were obtained using a bioreactor at 32 °C for 20 h. The production method developed in this study for the biosynthesis of soluble hEGF is efficient and inexpensive.

Light-driven progesterone production by InP-(M. neoaurum) biohybrid system

Progesterone is one of the classical hormone drugs used in medicine for maintaining pregnancy. However, its manufacturing process, coupled with organic reagents and poisonous catalysts, causes irreversible environmental pollution. Recent advances in synthetic biology have demonstrated that the microbial biosynthesis of natural products, especially difficult-to-synthesize compounds, from building blocks is a promising strategy. Herein, overcoming the heterologous cytochrome P450 enzyme interdependency in Mycolicibacterium neoaurum successfully constructed the CYP11A1 running module to realize metabolic conversion from waste phytosterols to progesterone. Subsequently, progesterone yield was improved through strategies involving electron transfer and NADPH regeneration. Mutant CYP11A1 (mCYP11A1) and adrenodoxin reductase (ADR) were connected by a flexible linker (L) to form the chimera mCYP11A1-L-ADR to enhance electron transfer. The chimera mCYP11A1-L-ADR, adrenodoxin (ADX), and ADR-related homolog ARH1 were expressed in M. neoaurum, showed positive activity and produced 45 mg/L progesterone. This electron transfer strategy increased progesterone production by 3.95-fold compared with M. neoaurum expressing mCYP11A1, ADR, and ADX. Significantly, a novel inorganic-biological hybrid system was assembled by combining engineered M. neoaurum and InP nanoparticles to regenerate NADPH, which was increased 84-fold from the initial progesterone titer to 235 ± 50 mg/L. In summary, this work highlights the green and sustainable potential of obtaining synthetic progesterone from sterols in M. neoaurum.

An Allosteric Transcription Factor DNA-Binding Electrochemical Biosensor for Progesterone

We describe an electrochemical strategy to transduce allosteric transcription factor (aTF) binding affinity to sense steroid hormones. Our approach utilizes square wave voltammetry to monitor changes in current output as a progesterone (PRG)-specific aTF (SRTF1) unbinds from the cognate DNA sequence in the presence of PRG. The sensor detects PRG in artificial urine samples with sufficient sensitivity suitable for clinical applications. Our results highlight the capability of using aTFs as the biorecognition elements to develop electrochemical point-of-care biosensors for the detection of small-molecule biomarkers and analytes.