Globally 339 million people are suffering from asthma and it’s the most common chronic disease amongst children [1]. Each day 1000 people are estimated to die from Asthma, and it affects low income areas the worst [2]. Asthma is a complex and polygenic disease and today only physiological diagnostic methods are available, such as measuring breathing capabilities using a spirometer. However, several studies show that these methods are unreliable and often lead to misdiagnosis [3].
With the advancement of bioinformatics and vast accumulation of data we strongly believe that we can use this to not only address the problem mentioned above but also make bioinformatics accessible to more people. We will therefore create software for transcriptome analysis and use this for biomarker discovery and biosensor optimization. This will allow not only for more accurate diagnosis but also an overall better understanding of the genetic components of the disease.
When designing our project we realized we wanted to divide the work into two distinct parts; programming and experimental. We also divided the entire project into two phases since our labs were held closed. The group that focused on programming has developed ClusteRsy, a software for statistical analysis of transcriptome data, specifically the analysis of differentially expressed genes. The modeling group then used ClusteRsy to model data from asthma patients and controls to predict new biomarkers for asthma diagnosis.
The experimental group focused instead on more theoretical work. They searched for promising biomarkers for asthma, figured out how to validate these, and developed a theoretical biosensor. Phase two will take the results from all of this to develop a biosensor for asthma diagnosis.
When designing ClusteRsy we found it important to work with relevant feedback in order to create the best software we possibly could. We, therefore, worked through a cycle of design, implementation, and feedback. In order to work efficiently with this feedback, we made sure to set up criteria to work towards when developing ClusteRsy. When designing the beta-testing setups and the protocols for the feedback, we always had these criteria in mind.
Throughout the project we had time for two iterations of the development cycle where the first one started with the overall design of ClusteRsy, continued with the implementation of this design, and ended with a beta-testing to find bugs and ways to improve the software. The feedback was then compiled before moving on to the next iteration of the cycle.
We then continued on with the next iteration, where we designed solutions to the problems found during the earlier beta-testing. These solutions were then implemented and at the end of this iteration of the development cycle, we had another beta-testing to evaluate the improvements made.
When trying to solve a problem it is crucial to understand the problem and what barriers one needs to overcome. In our case, this is to understand asthma and how we can use synthetic biology together with ClusteRsy to create solutions. The Experimental group focused on understand asthma and to add biological relevance and understanding of the ClusteRsy results. The group also focused on finding potential biomarkers, validating the results, and creating a theoretical biosensor as mentioned above.
When searching for biomarkers we focused on most cells involved in the inflammatory process, and their main focus was to find proteins specific for asthma. One issue when finding biomarkers for the inflammatory process is that it isn’t asthma specific. We are therefore looking for proteins that are differentially expressed in the inflammatory process in asthma patients. Since it is a complex disease only one biomarker would not be sufficient enough for a diagnosis and therefore our aim is to find an assay of biomarkers. Another important criterion was that there were available antibodies.
The proteins that we found were eosinophil derived neurotoxin (EDN), eosinophil cationic protein (ECP), and histamine N-methyl transferase (HNMT). Both ECP and EDN are released during eosinophil degranulation. They are usually found in combination with each other and several studies show that patients with asthma have increased levels of these two proteins. HNMT is an enzyme that binds to histamine and causes inactivation of the compound. In the last two weeks, we had access to the laboratories and made it possible for us to start expressing these proteins.
In order to improve the biosensor as well as the experimental workload for phase II we used advanced transcriptome analysis to predict novel biomarkers for asthma diagnosis.
To do this we collected RNA-sequencing data from Th2 cells with asthma patients as well as controls. We then inferred disease modules using ClusteRsy and performed disease analysis to find significantly expressed genes in asthma patients. From the modeling studies, we were successfully able to predict not only 13 biomarkers but also discovered the connection between asthma and RSV Bronchiolitis.
These potential biomarkers will be validated using a sandwich ELISA and finally, they will be used to improve our biosensor for asthma diagnosis. The connection that was discovered between asthma and RSV Bronchiolitis will be proposed as a way to screen infants with a high risk of developing severe RSV Bronchiolitis. If this high-risk group could be identified that could lead to treatment with palivizumab and by this reduce the severity of RSV Bronchiolitis.
It is crucial to validate the potential biomarkers found both from the literature study and from ClusteRsy. This is to confirm that they are indeed involved in asthma and therefore can be reliably used as biomarkers for the biosensor. To do this the best option would be to use an antibody-based assay. We have previously mentioned that one of the criteria when selecting the final biomarkers was to make sure that known antibodies existed. So the idea is to create antibodies specifically for the predicted biomarkers and then comparing blood samples from patients and controls using this assay.
One idea we have for phase II of the project is to create these antibodies ourselves. This will be done by using a protocol for antibody production created by the Rochester iGEM team this year and then optimizing this protocol for our purpose. The biomarkers that are shown to be significant during this validation will finally make it to the biosensor assay.
Our project has showcased that our ClusteRsy can effectively be used as a tool for biomarker discovery. We have chosen to focus on asthma and revealed 13 biomarkers that can be used for a potential biosensor for asthma diagnosis. ClusteRsy can be used for more than just biomarker discovery, such as gene expression optimization and giving new insights into complex diseases.
[1]. Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet. 2017;390: 1211–59.
[2]. Enilari O and Sinha S. The Global Impact of Asthma in Adult Populations. Annals of Global Health. 2019;85(1), p.2.
[3]. Kavanagh J, Jackson DJ, Kent BD. Over- and under-diagnosis in asthma. Breathe (Sheff). 2019;15(1):e20-e27. doi:10.1183/2073473
