To realise our living diagnostic tool Colourectal, we did a lot of research. We did experiments regarding interaction, detection, signalling, modularity and biosafety. During all these experiments we went through multiple Design-Build-Test-Learn (DBTL) cycles. On this page we highlight two DBTL cycles we were particularly fond of. One highlights the DBTL cycle of the chromoprotein secretion results page, while the other highlights and combines the DBTL cycle of the L-lactate detection results and modelling page.

To make our living diagnostic capable of detecting colorectal cancer, we set out to make it sensitive to two cancer biomarkers, one of which is L-lactate. In healthy colons L-lactate is present at concentrations ranging between 1.5 – 3 mM, while in colon tumours the concentration is higher ranging between 10 – 25 mM [1].

Some bacteria, such as Escherichia coli can use lactate as a substrate for growth. The operon responsible for this is the lldPRD operon [2]. This operon contains a transcription factor called LldR capable of recognizing two operator sequences located upstream and downstream of the operon’s promoter. By binding to these operator sequences LldR represses the expression of lldPRD. However, if lactate is present, LldR changes conformation which results in activation of gene expression. We used this concept to develop a synthetic biosensor in E. coli Nissle 1917 (EcN) to sense L-lactate. A problem with the native lldPRD operon promoter, however, is that it is repressed by the presence of glucose and absence oxygen, which are conditions found in the colon [3]. Fortunately, Zúñiga et al. developed a lactate sensitive promoter which is not repressed by the above-mentioned conditions, called A Lactate Promoter Operating in Glucose and Anoxia (ALPaGA) [4].

Using this concept we built a lactate-sensing genetic circuit (Figure 1). In this circuit a reporter gene, super folder green fluorescent protein (sfGFP) , was placed after the lactate inducible promoter ALPaGA, while LldR was constitutively expressed. A genetic construct like this should only show fluorescence when lactate is present.

Figure 1: Configuration of lactate sensing genetic circuit, the circuit consists of a transcription factor LldR (red), a lactate inducible promoter ALPaGA (A Promoter Operating in Glucose and Anoxia) and super folder green fluorescent protein (sfGFP) (Green). 1A When lactate is absent sfGFP is repressed. 1B When lactate is present sfGFP is expressed.

This genetic circuit was then introduced into EcN, and the fluorescence output was measured at different L-lactate concentrations. From this we learned that EcN was able to sense L-lactate using such a genetic circuit since an increase in fluorescence with increasing L-lactate concentrations was observed. However currently, the biosensor is not able to differentiate healthy levels of lactate (1-3 mM) from levels associated with cancer (10-30 mM), highlighted as the green and red areas in Figure 2. This is because at both healthy and cancer associated levels of L-lactate significant fluorescence is observed.

Figure 2: The response of ALPaGA, expressed as OD600 corrected fluorescence output, at different L-lactate concentrations. The green and red areas signify the L-lactate concentrations in the microenvironment of healthy and cancerous colon cells, respectively. ** P ≤ 0.01 *** P ≤ 0.001.

Because of this we set out to find a way to change the dose response in such a way that it will not respond to healthy levels of L-lactate anymore. When diving into literature we found that there are many tools to regulate gene expression in bacteria, of which two examples are Clustered Regularly Interspaced Palindromic Repeats interference (CRISPRi) and antisense RNA (asRNA) [5–7]. We hypothesized that we could modify the operational range of the dose response from Figure 2 by adding these two tools to the genetic circuit.

To test this hypothesis, we designed a CRISPRi-asRNA genetic circuit. This circuit is organized as follows: a constitutively expressed CRISPRi cassette represses sfGFP. The cassette consists of a deactivated CRISPR associated (dCas) protein and a single-guide RNA (sgRNA) complementary to sfGFP. Additionally, an asRNA complementary to the sgRNA would be made inducible by lactate. This can be made possible by placing the ALPaGA promoter in front of the asRNA and constitutively expressing LldR. Designing a genetic circuit in this way means that the reporter gene is repressed if lactate is absent but expressed when lactate is present. The initial hypothesis was that by changing the binding affinity between the asRNA-sgRNA complex, the activation efficiency would change. If the binding affinity was low, more asRNA, and thus more lactate, would need to be present to sufficiently counteract the CRISPRi cassette. This would thus result in the operational range shifting to higher lactate concentrations.

Figure 2: The response of ALPaGA, expressed as OD600 corrected fluorescence output, at different L-lactate concentrations. The green and red areas signify the L-lactate concentrations in the microenvironment of healthy and cancerous colon cells, respectively. ** P ≤ 0.01 *** P ≤ 0.001.

Due to insufficient time, we were not able to finish building and testing the construct. Fortunately, the construct was modelled in silico and fitted to experimental data from the previous experiment as well as data from literature [7].

The model predicted that only the sensitivity, meaning the slope of the dose response curve, would change when adding the CRISPRi-asRNA genetic circuit (Figure 3). The added CRISPRi and asRNA genetic components were not predicted to affect the operational range of the dose response curve. See Figure 4 for a comparison of the modelled dose responses. To find parameters that can change the operational range of the dose response, we performed sensitivity analysis.

Figure 4: Modelled dose responses for L-lactate of the genetic circuits with and without Clustered Regularly Interspaced Palindromic Repeats interference (CRISPRi) and antisense RNA (asRNA) in orange and teal, respectively. The green and red areas signify the concentration of L-lactate around healthy and cancerous colon cell microenvironments, respectively.

From the sensitivity analysis we learned that the parameter described as $k\_f_{LldRcomplex}$ was able to change the operational range, see Figure 5. This parameter describes the rate of activation of LldR. We hypothesized that, in biological terms, this rate is influenced by the intracellular L-lactate concentration. This means that, if we could control the internal concentration of L-lactate, we could control the operational range of the dose response. We expect we can control the intracellular concentration of L-lactate by knocking out or knocking in lactate dehydrogenases.

Figure 5: Modelled dose responses for L-lactate where the parameter k_f_LldRcomplex is increased or decreased by a factor of 10. The green and red areas signify the concentration of L-lactate around healthy and cancerous colon cell microenvironments, respectively.

To test this hypothesis, we dove into literature to find what enzymes are responsible for the degradation of L-lactate in EcN. We found that EcN, like other E. coli strains, contains two genes responsible for L-lactate dehydrogenases, namely lldD and ykgF [8]. Besides, EcN contains another lactate dehydrogenase nldH, not found in other E. coli strains [9]. We designed a strategy to knock out these genes in EcN. If time had allowed, we wanted to test our lactate biosensor in this knock out strain and compare it to the biosensor in a normal EcN strain.

During our experiments regarding the detection of cancer biomarker L-lactate we went through multiple DBTL cycles. We did this by combining literature, experimental data, and modelling to test multiple hypothesis and gain relevant insights into our L-lactate biosensor circuit.