We follow a line of investigation in which we map epigenetic changes in response to exposure to infections and inflammatory disorders. We are collecting information on the DNA methylome using next generation sequencing techniques, generating large datasets. Our ambition is to identify patterns, or biosignatures, in the DNA methylome of immune cells, that reflect epigenetic changes during diseases, with special focus on but not limited to respiratory disorders. We need access to the supercomputer in order to perform parallel computation using machine learning approaches.
We published research articles/preprints during the year 2020-21 using the SNIC resources and we have acknowledged that in our articles/preprints –
1. Karlsson et al (2021) A Differential DNA Methylome Signature of Pulmonary Immune Cells from Individuals Converting to Latent Tuberculosis Infection, accepted in Scientific Reports (September 2, 2021).
2. Das et al (2021) DNA methylome-based validation of induced sputum as an effective protocol to study lung immunity: construction of a classifier of pulmonary cell types, Epigenetics, https://doi.org/10.1080/15592294.2021.1969499
3. Pehrson et al (2021) DNA methylation profiling of immune cells from tuberculosis-exposed individuals overlaps with BCG-induced epigenetic changes and correlates with the emergence of anti-mycobacterial ‘corralling cells’, medrxiv, doi: https://doi.org/10.1101/2021.09.01.21262945
4. Pehrson et al, DNA methylomes derived from alveolar macrophages display distinct patterns in latent tuberculosis - implication for interferon gamma release assay status determination, medrxiv, doi: https://doi.org/10.1101/2021.03.16.21253725