Wiki Source, DNA binding motif of TetR
Antibiotics are often natural products discovered in other organisms, including but not limited to bacteria, fungi, plant, frogs and humans. Although they may have different functions in their natural setting, they are exploited by medicine for their ability to kill bacteria. Their discovery often relies on traditional testing for antimicrobial activity in a crude extract, followed by the purification of the active ingredient. We have progressed the search for new antibiotics and employ elegant new approaches to produce and detect antibiotic activity, combined with high throughput screening methods that permit us to find the’ needle in a haystack’ of tens of thousands of compounds.
Macrolides are a broad-spectrum, bacteriostatic class of antimicrobials that bind to the large 50S subunit of the ribosome and block the translocation step during protein synthesis. Erythromycin was discovered by an Eli Lilly researcher in the late 1940s, who discovered this antibiotic from a Streptomyces isolate recovered from a soil sample. This polyketide class of antimicrobials also has other important biological activities, including antifungal, antiprotozoal and even anticancer activity. Macrolides have complex structures and are synthesized by type I polyketide syntheses in a modular fashion. Rationally designing these enzymes for novel biosynthetic projects has proved challenging. Rather than engineer these complex modular enzymes to produce new macrolides, the work described here uses a biosensor strategy to detect macrolides with a tailored substrate specificity and increased sensitivity.
The MphR protein is a TetR repressor protein that represses the expression of a macrolide phosphotransferase resistance protein. In general, the TetR family are transcriptional repressors that bind and repress target promoters, but in the presence of a chemical effector, allow for activation of the target genes. This is a common regulatory mechanism used by bacteria to sense changing environmental conditions and then express appropriate genes to cope with the change.
In the presence of the macrolide erythromycin, MphR binds the antibiotic, causing MphR to fall off the target promoter and induce expression of the resistance gene. In this system, MphR is the only known bacterial protein that binds to and therefore ‘senses’ the presence of macrolide antibiotics in the cytoplasm. MphR is “de-repressed” in the presence of several natural and semi-synthetic macrolides, including erythromycin and others. Note, resistance to macrolides can also occur through other resistance mechanisms, including efflux pumps or by mutations or modification in the small ribosome subunit.
Rather than engineer and adjust the macrolide biosynthesis enzymes and pathways to create new novel macrolides, the authors attempted to engineer MphR so that it recognized a different range of macrolides, or better differentiated natural and synthetic compounds, and increased its sensitivity in order to create more effective biosensors.
The biosensor system was previously described and uses plasmid-based system in E. coli. The plasmid encodes both MphR (biosensor) and the gfp reporter under the control of an MphR- regulated promoter. In the presence of erythromycin, the E. coli biosensor now produces green fluorescence from turning on the gfp reporter. As a proof of concept, error prone PCR and multi-site saturation mutagenesis were performed on MphR, which led to variants of MphR that were up to 10-fold more sensitive to its’ natural ligand, erythromycin. Mutations were recovered in the coding sequence, as well as in the non-coding ribosome binding site (RBS). Utilizing the 3D structure of MphR, mutations were targeted to key amino acids in the repressor that are involved in recognizing macrolides.
It would be ideal to have an MphR biosensor that recognizes a specific macrolide structure within a complex mixture. Given that MphR has a relatively broad substrate specificity, the goal was to mutagenize MphR and select for variants with a narrowed specificity. The first attempt was to try and find mutants that could differentiate natural from semi-symthetic macrolide structures. After the error prone PCR libraries were constructed and introduced back into E. coli, FACS sorting was used to screen for constitutive gfp clones, which were excluded, and then erythromycin-responsive gfp expressing clones, followed by determining the kinetics and sensitivity levels. Randomly generated mutants were reconstructed with targeted mutations, which confirmed that MphR variants could now respond to a narrow range of macrolides. The amino acid mutations were mapped to the protein structure, but it wasn’t immediately obvious some of these changes led to a narrow specificity.
The next aim was to engineer MphR variants that could now recognize macrolides that the wild type does not, despite its broad substrate specificity. Several macrolides are produced by Streptomyces venezuelae, such as pikromcyin. The error prone PCR-generated libraries of MphR variants, were screened for those that could now detect pikromcyin. Resulting from this FACS-based screen, a biosensor strain grown in the presence of pikromycin that expressed a single amino acid variant of MphR, was 123 times more sensitive than the wild type protein.
Screening for new macrolide structures using conventional approaches and mass spectrometry is very complex, time consuming and costly. The ultimate goal of this project would be to use the relatively simple and high throughput MphR-based biosensor to detect a new macrolide structure. As a proof of concept, the culture supernatants from a bacterial strain that only produces erythromycin A and not the intermediate B and C products, were recovered and added to the macrolide biosensors. The engineered MphR biosensor was 5 times more sensitive to detect erythromycin A directly from bacterial supernatants that required no purification or concentration.
While the research described here 1 highlights the potential to engineer macrolide biosensors for detecting new substrates with greater sensitivity, others have used a similar approach with the wild type MphR biosensor to detect macrolides from Streptomyces supernatants 2. It is impressive that single or double amino acid mutants can be recovered that are capable of detecting very subtle differences between macrolide structures.
These kind of projects laid the groundwork for future macrolide discovery, relying on the elegant macrolide sensing mechanisms of transcriptional repressor proteins. Biosensors could be used to detect new antibiotics in large collections of semi-synthetic drug libraries or natural products. They are also useful in screening the complex soup of compounds within culture supernatants from other macrolide producing strains, possibly with engineered macrolide biosynthesis pathways, or by screening the culture supernatants from metagenomic libraries. In the latter approach, environmental DNA from any soil sample is cloned and expressed in a lab friendly bacterial host. A library of 1000s of isolates can effectively express all the DNA in a given soil sample, which overcomes our current ability to grow the vast majority of microbes in soil and in most environments. It therefore allows us to access and screen the diverse biosynthetic potential from microbes that are uncultivable. Many new antibiotics will undoubtedly be discovered from this “microbial dark matter“.
1. Development of Transcription Factor-Based Designer Macrolide Biosensors for Metabolic Engineering and Synthetic Biology. Kasey CM, Zerrad M, Li Y, Cropp TA, Williams GJ ACS Synth Biol. 2017 Oct 12. doi: 10.1021/acssynbio.7b00287. [Epub ahead of print] PMID:28950701
2. Biosensor-guided screening for macrolides. Möhrle V, Stadler M, Eberz G. Anal Bioanal Chem. 2007 Jul;388(5-6):1117-25. Epub 2007 May 12. PMID: 17497142