Pervasiveness of differential Isoform usage and their genetic modulators in infection and disease
Contact: saumya.dileepkumar(at)helmholtz-hzi.de
Differential isoform usage (DIU) has been described as an important mechanism for inter-individual phenotypic differences. It has also been associated to various immuno-pathologies e.g., dysregulated immune response to infection, autoimmune diseases [1]. In line with this, previous study in COVID had identified differential role of OAS1 isoforms against susceptibility and severity of COVID-19 infection [2]. While allele specific alternate splicing events are already examined at steady state, the exploration of DIU with ageing immune system and upon infection and stimulation is rather limited [3,4].
In this project, the candidate will examine the pervasiveness of isoform switch or (DIU) in infection and disease along with impact of DIU in ageing immune system using transcriptomics datasets generated from multiple cohorts in the lab. The candidate will first examine and establish the best methods and pipeline for the systematic analysis of DIU in these datasets. The project will also examine the genetic modulators of isoform usage QTL (isoQTL) that may be implicated in the disease and dysregulated immune system with age. Results from this project will provide insights in the regulation of disease pathogenesis and will also be experimentally validated.
- Zhang, X., Hassan, M. A. & Prendergast, J. G. D. Using machine learning to detect the differential usage of novel gene isoforms. BMC Bioinformatics 23, 45 (2022). doi:10.1186/s12859-022-04645-3
- Zhou, S. et al. A Neanderthal OAS1 isoform protects individuals of European ancestry against COVID-19 susceptibility and severity. Nat Med 27, 659–667 (2021). doi:10.1038/s41591-021-01281-1
- Lappalainen, T. et al. Transcriptome and genome sequencing uncovers functional variation in humans. Nature 501, 506–511 (2013). doi:10.1038/nature12531
- Ye, C. J. et al. Genetic analysis of isoform usage in the human anti-viral response reveals influenza-specific regulation of ERAP2 transcripts under balancing selection. Genome Res 28, 1812–1825 (2018). doi:10.1101/gr.234849.118
An interactive webtool to explore a large multi-ancestry consortium for immune functional genomics in health and disease
Contact: nienke.vanunen(at)helmholtz-hzi.de
The human immune system is a complex network designed to defend against pathogens while maintaining tolerance to self. It is highly variable among individuals, due to factors such as age, and sex, but also our genetics play a huge role in this. Using quantitative trait loci (QTL) mapping, links can be made between genetic variation and a quantitative phenotype, such as cytokine response to stimuli (cQTL) [1]. We are working on a consortium for a large-scale cQTL meta-analysis consisting of 12 cohorts and 6000 individuals across multiple ancestries and diseases. Doing so, we aim to make significant contributions to the understanding of the genetic basis of the immune response, which in turn can inform the development of personalized therapeutic interventions.
The task of the student will be to develop a webtool to visualise the consortium's summary statistics, similarly to the eQTLgen consortium [2]. Doing so, we aim to make our results accessible for others to search through and to inspire future research in this vital area of genomics and immunology. The tool will also aid easier data analysis and visualisation, which will allow the student to make new interesting findings as well. The tool can be made in a language of the student's choice; be it Rshiny or a Python framework such as Django.
- Li Y, Oosting M, Smeekens SP, et al. A Functional Genomics Approach to Understand Variation in Cytokine Production in Humans. Cell. 2016;167(4):1099-1110.e14. doi:10.1016/j.cell.2016.10.017
- Võsa U, Claringbould A, Westra HJ, et al. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat Genet. 2021;53(9):1300-1310. doi:10.1038/s41588-021-00913-z
Webtool Development for Exploring Spatial Transcriptomics Data of MTB Granuloma
Contact: xun.jiang(at)helmholtz-hzi.de
Mycobacterium tuberculosis (MTB) is the causative agent of tuberculosis (TB), a leading infectious disease that results in significant morbidity and mortality worldwide. A key feature of TB infection is the formation of granulomas, which are organized structures of immune cells that attempt to contain the bacteria. Studying the differential gene expression in various regions of these granulomas is crucial for understanding the mechanisms of immune response, pathogen survival, and disease progression.
Our project focuses on developing an interactive webtool to explore spatial transcriptomics data of MTB granuloma. This tool will visualize differential gene expression across different tissue regions, providing researchers and clinicians with an accessible platform to search through and analyze our data. This will inspire future research and aid in the understanding of the genetic basis of immune responses, which in turn can enhance the development of personalized therapeutic interventions.
The student will develop a webtool for visualizing the summary statistics on differential gene expression in different tissue regions associated with granuloma. This tool will facilitate data analysis and visualization, allowing users to make new findings in molecule mechanism and immunology. The webtool can be developed in a language of the student's choice, such as RShiny or a Python framework like Django.