Synthetic metabolic and genetic networks for medical diagnostics – SynBioDiag

Organisms have evolved to sense large panels of metabolites and human have used these sensing abilities throughout history. Recently, synthetic biology has expanded and rationalized biosensing by engineering whole-cell biosensors (WCBs) with sophisticated signal processing capabilities. The main advantages of cell-based technologies over abiotic detection based on purified antibodies, nucleic acid hybridization, or metabolomics analysis are lower cost, improved stability, and the possibility to be ultimately used at the point-of-care or as a personal healthcare home device.

Our project aims to engineer a scalable, programmable WCB platform for the multiplexed detection of biomarkers in clinical samples. As a model system, we will focus on the detection of chronic diseases biomarkers, which clinical samples are abundant and multiple gold standard tests exist to which benchmark our platform. To this end, serum, urine and tissue samples will be collected from patients of the Montpellier University Hospital.

Our project is timely and highly relevant as the partners have recently addressed two main bottlenecks in developing WCB for the clinic namely: (i) scaling-up the number of analytes that can actually be detected and (ii) solving reliability issues of WCB operation in complex biofluids media.

Typical WCB are triggered by no more than half a dozen input signals. To palliate this shortcoming, the MICALIS partner has recently expanded the range of biologically detectable molecules up to a thousand by systematically designing and engineering metabolic pathways that transform non-detectable chemicals into molecules for which sensors already exist. The method has been successfully benchmarked to engineer biosensors that detected pollutants, drugs and biomarkers such as benzoic acid and hippuric acid.

The other main challenge limiting WCB deployment has been their unreliable operation and low signal to noise ratio in complex and heterogeneous samples like physiological fluids. The CBS partner solved this issue by developing a signal-processing platform using amplifying genetic switches and demonstrated the detection of abnormal glycosuria into the urine of diabetic patients. This work validates the operational capacity of WCB technology in clinical samples.

Here we will combine the advantages of the above recent developments by first sensing biomarkers, through synthetic metabolic pathways acting as analog devices and then triggering a digital response using genetic switches robustly operating in clinical samples. Such a hybrid analog/digital system has not yet been developed for biosensors.

Our WCBs will be implemented with bacteria (non-pathogenic strains of E. coli) and on paper-based cell-free systems. Bacterial sensors can encompass complex sensing circuits, do not need expensive resources even for long times scale operation and are cheap to produce. However, biomarkers that are toxic or that do not cross cell membrane are difficult to detect. Toxicity and transport issues are irrelevant with paper-based cell-free biosensors. With paper-based cell-free systems, biosensors can be stored when freeze dried for a long time, the sensing circuits can be easily optimized by simply changing the amount of DNA encoding the different functions, and since cell-free systems are abiotic they present less release concerns and regulatory hurdles.

While the platform will be benchmarked for detecting prostate neoplasia biomarkers, it will be set up through a robust engineering workflow supporting fast repurposing to many other medical diagnostics especially those sharing biomarkers with prostate disorder (such as schizophrenia, uremia, chronic renal failure).