Description: Endogenous retroviruses (ERVs) are genomic remnants of retroviral infections and subsequent expansion that are present in most organisms. In the human genome ERVs occupy approximately 8% of our DNA, some of which retain enhancer and promoter sequences to drive the expression of their viral-genes. However, many of these sequences also affect the expression of other nearby genes in the host genome. Recent research suggests that ERVs have been evolutionarily selected in genomic locations where they can drive or boost the expression of immune-related genes. They can also trigger an immune response by the transcription of their viral-genes.
Aberrant ERV transcription has been observed in several neurological diseases and upon neuroinflammation. Interestingly, an ERV-derived envelope protein has been found in cerebral spinal fluid in about a third of schizophrenia patients of a large cohort, and researchers at our lab have shown that the presence of this protein in the brain impairs synaptic maturation in mice models (DOI: 10.1126/sciadv.abc0708).
To study if this is the case in human neurons and provide a potential therapeutic model, the lab has been growing neuronal cultures transcriptionally activating ERVs using a CRISPR activation system. We have, so far, validated 3 guides using induced pluripotent stem cells and are waiting for the sequencing data of the neuronal cultures experiments.
This project represents a bioinformatic challenge given that the envelope protein identified at psychiatric patients has not been pinpointed to a specific genomic locus. We have or are in the process of creating several datasets related to this project including bulk RNAseq, single nuclei RNAseq, CUT&RUN for histone modifications and long read RNAseq.
We are looking for: Master student (preferably but not exclusively of 60cr thesis) who feels comfortable working on the terminal (bash), scripting with R, and using git. Knowledge on workflow management (Snakemake or Nextflow) is a plus.
You will gain experience with: workflow management, making your own pipelines and using others, working on a computing cluster, using git, using statistical models to identify differentially expressed genes between experimental conditions accounting for batch effects and other covariates, transposon biology and how to adequate your bioinformatical approaches for repetitive elements, scientific thinking, visualization, and writing.
If you find this project interesting, send me a message to raquel.garza@med.lu.se, tell me what you find interesting about it, and a little bit about your experience and ambitions!