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miRNA Detector Module
sensing Alzheimer's through multi-input miRNA-based logic
Description
miRNAs (microRNAs) are short, noncoding strands of RNA that facilitate gene silencing - a single miRNA degrades an mRNA through a process involving complementary base-pairing between the miRNA and part of the mRNA sequence. A cell’s miRNA profile comprises the relative levels of all the miRNAs produced by that cell. Because miRNAs play a key role in regulating gene expression, it ought to be expected that a liver cell’s miRNA profile would differ significantly from that of a neuron. But more surprisingly, miRNA profiles can discriminate between identical cells in different conditions.
Neurons afflicted with Alzheimer’s disease display an miRNA profile significantly different from that of healthy neurons (A blood based 12-miRNA signature of Alzheimer disease patients, Leidinger et al, 2013). The miRNA subgroup aimed to use this difference as an approach to detecting Alzheimer’s disease. Our goal was to build a set of genetic sensors to specifically detect the miRNA profile of a neuron with Alzheimer’s and initiate a specific biological response upon doing so.
Our strategy took its inspiration from a similar detection circuit demonstrated to respond to cancer onset (Multi-input RNAi-based logic circuit for identification of specific cancer cells, Xie et al, 2011) Through existing research, we identified six miRNAs that are critically up- or down-regulated in Alzheimer’s neurons. Using the inverting logic inherent to miRNAs, we designed detection circuits to release a response factor upon sensing either heightened or lowered levels of their target miRNA, and customized each circuit to use one of the six miRNAs as its input. Using the principles of combinational logic, we can integrate the inputs from all six of our miRNA sensors, and actuate our response only when all six miRNAs meet their critical threshold concentrations. This ensures excellent specificity for our circuit.
Outcome
The miRNA detection team built individual sensing constructs for each miRNA. We determined input-output relations for our sensors using flow cytometry and found that our sensors respond to miRNA levels by modulating the production of a fluorescent reporter, exactly as we had predicted.
Future work on our sensors will focus largely on implementation concerns - tuning as well as integration. Although we have shown that our sensors respond on a digital level, this does not accurately model the dynamic chemical conditions of the intracellular environment. We would thus like to refine our sensor control. In the ideal case, a small shift in a critical range of miRNA concentration will result in a large output signal, so that the treatment response is both specific and substantial.
We only tested binary combinatorial inputs for our sensors (one high and one low, or two of each). The ultimate goal is to use all six sensors in tandem with one another. When we use more sensors, we achieve greater precision, but as a tradeoff we gain more variables that require keeping track. There is also the complication that the various miRNAs are not biologically present at the same concentrations, meaning that each of our sensors must be individually tuned for optimal response to its own miRNA. Because all six of our sensors actuate the same response, we must also ensure that one sensor does not become overstimulated, activating our treatment even in the absence of input from the other sensors. These are all issues that can only be answered through extensive iterative testing.
The miRNA sensing team has established a conceptual grounding for a detection mechanism that responds to cellular conditions in the fashion of a true biological system. It is worthwhile to note that our strategy is not Alzheimer’s-specific, and can be implemented with any disease with a characteristic miRNA profile. This can be a novel approach for diseases with poorly understood etiologies, such as Parkinson’s (MicroRNA profiling of Parkinson's disease brains identifies early downregulation of miR-34b/c which modulate mitochondrial function, Minones-Moyano, 2011)
Experiments
Low Sensor Construction
By cloning an miRNA target site 3’ to a gene coding a reporter protein, we can easily create a sensor that produces reporter protein only when miRNA levels are low enough to permit translation. In our experiments, we used a fluorescent reporter as a placeholder for rtTA, which would activate our treatment circuit.
https://2014.igem.org/File:MIT_low_sensor_schematic.png
High Sensor Construction
Because miRNAs naturally silence genes, for our high sensor design we cloned miRNA target sites to a repressor protein that would block transcription of response protein at the low sensor. We chose to use the L7ae/K-turn to eliminate the possibility of crosstalk with other cellular activities.
https://2014.igem.org/File:MIT_high_sensor_schematic.png
Repression of L7ae
Before coming to any conclusions about the success of our constructs, we needed to make sure that the L7ae/k-turn system worked correctly. To do this we expressed k-turn:eGFP with and without the presence of constitutive L7ae. We used eBFP as our normalizing transfection marker.
https://2014.igem.org/File:L7ae_Cytometry.png
https://2014.igem.org/File:MIT_L7ae_Repression.png
In the absence of L7ae, eBFP and eGFP levels scale linearly with each other, as expected when co-expressing two constitutive fluorophores. However, upon the addition of L7ae, eGFP production is completely silenced, indicating proper function of the L7ae/k-turn system.
Single-Input Sensor Testing
We tested the sensitivity of our sensor constructs individually for each of the six miRNA that are upregulated or downregulated in Alzheimer neurons.
To test our high sensors, we ran the same experiment as before, but instead of using constitutive L7ae, we used our high sensor constructs, which produce L7ae at a level dependent on the concentration of miRNA being sensed. We used custom-designed siRNAs to control the level of input for the sensors.
To test the low sensors, we expressed only the low sensor constructs, and again used siRNAs to modulate our input.
[DATA, RESULTS/CONCLUSION]
Multi-Input Sensor Testing
Our next goal was to test multiple sensors at once to ensure that they interacted with one another in the manner we desired. We did not test all six sensors together, as this would have required too much time. Instead we ran tests of various sensor pairs: two high sensors together, two low sensors together, and one high with one low sensor. Together, these encompass all the possible pairwise interactions in our detection module.
[DATA, RESULTS/CONCLUSIONS]
Parts
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