Mingwan Sun
Current Focus
In the PhD period, I will mainly dedicate to investigating the mechanism of MHC ( Major Histocompatibility Complex) in shorebirds. MHC gene codes for proteins found on the surfaces of cells that present peptide fragments from both own cells and pathogen then activate the immune response. Because this gene plays a role in the army race between the immune system and the virus, this gene needs to keep a high diversity to tackle flexible infections.
According to the immune function of this gene, my hypothesis is MHC may have an impact on the microbiome flora in the body (i.e. gut microbiome). Here I will testify the correlation between MHC and gut microbiome in plover populations. Besides, MHC has been also proven to act as an olfactory factor in mammals, but not in avian species. And the change in the smell also influences their mating strategies. Therefore, for one I’m going to check whether MHC diversity contributes to the smell composition, for another I will check if this contribution may influence the mating choice. If you have any interest in this charming gene, any talk is welcomed!
During the previous three years, I have joined several projects about disease like cancer using the next-generation sequencing data. I’m familiar with transcriptome data and single-cell transcriptome data and has quite analysing experience.
LINKS
Previous Research and Other Interests
Curriculum Vitae
Education
2023-current: PhD in Biology, University of Bath, United Kingdom
2020-2023: MSc in Bioinformatics, Sun Yan-sen University, China
2016-2020: BSc in Biological Sciences, South China Agricultural University, China
Publications
Wang, W., Zhou, X., Wang, J., Yao, J., Wen, H., Wang, Y., Sun, M., Zhang, C., Tao, W., Zou, J. & Ni, T. (2023). Approximate estimation of cell-type resolution transcriptome in bulk tissue through matrix completion. Briefings in Bioinformatics, 24(5), bbad273. DOI: 10.1093/bib/bbad273 📄
Wang, W., Tan, H., Sun, M., Han, Y., Chen, W., Qiu, S., Zheng, K., Wei, G. & Ni, T. (2021). Independent component analysis based gene co-expression network inference (ICAnet) to decipher functional modules for better single-cell clustering and batch integration. Nucleic Acids Research, 49(9), e54-e54. DOI: 10.1093/nar/gkab089 📄