Asthma is a common disease with no cure and is a disease that is becoming more and more common. The specific cause of asthma has not been determined but the general thought is that it is caused by genetic and environmental factors. To diagnose asthma, crude methods are being used such as the basis of symptoms, response to certain therapies, and the use of spirometry. Several studies have shown that these physiological methods often lead to misdiagnosis [1]. We strongly believe that biomarkers and biosensor technology could be used to address this problem.
In order to acquire molecular information about asthma we have focused our work on bioinformatics and more specifically RNA-sequencing and differentially expressed genes. The differentially expressed genes can tell us more molecular information about conditions and diseases. Currently, there is a gap between bioinformaticians and doctors. Most doctors do not have the time and routines to access the advanced algorithms and tools of bioinformatics. In our project, we will bridge this gap and also use it ourselves to develop a biosensor for the detection of asthma.
The aim of the programming group was to create a software that was so intuitive that a user with no previous knowledge of programming could utilize state-of-the-art algorithms to detect differentially expressed genes.
Advanced algorithms to detect differentially expressed genes in samples from RNA-sequencing have been developed at Linköpings University [2]. In order for the algorithms to be used knowledge regarding data pre-processing and optimizing parameters is key. This decreases the number of potential users to such an extent that only users with a background in bioinformatics and programming can use it. Meaning that the typical users with the RNA-sequencing such as clinicians and biologists have troubles using these algorithms for their research. Today the RNA-sequencing data is sent to a bioinformatician and analyzed. We in iGEM Linköping want to remove this stage and empower the clinicians and biologists to analyze their RNA-sequencing data by themselves. Therefore we have created the web-based software ClusteRsy.
With ClusteRsy the user can upload a raw count matrix of transcriptomic data and a PPI-network to be inferred, enriched, and visualized in four simple steps. The results can be visualized and the results of each disease are extracted for further analysis. The modules which are created can also be downloaded as Cytoscape objects for even further analysis of each gene is the disease set.
Since the project was decided to be two-phased, this year's experimental subgroup was mainly focused on preparing grounds for next year phase II. The purpose of this subgroup, therefore, was to research asthma and its biomarkers via the literature study and later come up with the idea of a biosensor that will help in asthma diagnosis. Our work was divided into three big steps. The goal of step one was to gain basic knowledge about asthma as a disease, its phenotypes, endotypes, and disease mechanism as well as finding three good asthma biomarkers. For step two we aimed to express and purify chosen protein biomarkers, which will be used as a control for the validation of the predicted biomarkers by ClusteRsy during phase II. And the final third step of phase I was to create a theoretical biosensor which will be developed by next year's team.
This section of our project is where we have used ClusteRsy for our purpose of designing a biosensor and finding potential biomarkers for the detection of asthma. In the process, we have designed a pipeline for the usage of ClusteRsy and making it possible for clinicians and other iGEM teams to easily follow the procedure. Raw transcriptome data is taken from the NCBI GEO database and modules are inferred by ClusteRsy, these can then be evaluated by PASCAL to check if they are significant. The significant modules are then used for disease analysis which is included in ClusteRsy to find significantly enriched genes that are involved in asthma.
From the inferred modules together with disease analysis we were able to find a strong connection between RSV Bronchiolitis and asthma as well as 13 predicted biomarkers for asthma diagnosis. The connection between RSV Bronchiolitis and asthma is very interesting for many reasons, after a meeting with Maria Jenmalm, Professor of Experimental Allergology, we came to the conclusion that our results could be used to identify infants who are at high risk to develop severe RSV Bronchiolitis. If this high-risk group could be identified, treatment with palivizumab could be considered to ease the severity of RSV Bronchiolitis. These results show the power of using a modular approach when inferring transcriptome data and the number of details in the resulting data is sufficient enough to find great detail in heterogenetic and complex diseases such as asthma.
In order to validate the predicted biomarkers, the experimental group looked into different options and proposed that a sandwich ELISA will be used. An antibody-based assay offers a great way to validate if the predicted biomarkers indeed are over or under-expressed in asthma patients. If an ELISA is to be used for validation, existing antibodies for the predicted biomarkers are crucial and therefore we made sure to check that there were available antibodies.
We are with these results able to propose not only 13 potential novel biomarkers for asthma diagnosis but also a way to identify infants who are at high risk for severe RSV Bronchiolitis.
[1]. Kavanagh J, Jackson DJ, Kent BD. Over- and under-diagnosis in asthma. Breathe (Sheff). 2019;15(1):e20-e27. doi:10.1183/2073473
[2]. de Weerd HA, Badam TVS, Enguita DM, Åkesson J, Muthas D, Gustafsson M, Lubovac-Pilav Z, MODifieR: an Ensemble R Package for Inference of Disease Modules from Transcriptomics Networks. Bioinformatics. 2020;36(12):3918-19. https://doi.org/10.1093/bioinformatics/btaa235
