Investigating the molecular mechanisms that underpin M. tuberculosis and Dendritic cell interactions

Tuberculosis (TB) has remained a considerable threat to human health particularly in countries with poorly developed healthcare systems, with one of the highest incidence of infection of any pathogen. During the pandemic a shift of public focus and medical resources has only increased infection rates, reaffirming the need to deepen our understanding of the interaction between Myobacterium tuberculosis (Mtb) and the human immune system. The macrophage is considered to be the prime target immune cell for Mtb because it functions as both an antigen-presenting cell and a phagocyte which facilitates intracellular survival and discreet transport of the Mtb throughout the body. The dendritic cell also fulfils both these rolls, with more of its function dedicated to antigen-presenting rather than phagocytosis, however, the focus of Mtb and dendritic cell interactions is vastly under reported compared to macrophages. My project aims to identify novel interactions between Mtb and dendritic cells that lead to changes in the function of the dendritic cell. Through this investigation I will attempt to determine whether these interactions are beneficial or fateful for the survival of the dendritic cell. I began by initially surveying publicly available data of datasets containing dendritic cells-challenged with different pathogens, including Mtb, to identify a consistent signature of highly (at least +/- LogFC 2) differentially expressed genes unique to Mtb-challenged dendritic cells. I also used gene-set enrichment analysis (GSEA) and pathway ontology to capture the broadest range of cellular changes that were common changes between the datasets containing Mtb. This provided me with a set of 105 genes and 10 pathways that were consistent across 9 conditions in three data sets. I then constructed a simple in vitro model using THP-1 cells and challenged these with 5 stimulants, three pathogens (M. tuberculosis, C. albicans and E. coli), LPS, dexamethasone, to see whether a unique response from Mtb-challenge could be identified using a small panel of genes that produce proteins that are known to have immune modulating effects e.g. TNFA and IL8. These results indicated that although some of the genes were commonly modulated by E.coli and M. tuberculosis, Mtb induced a contrasting effect on IL10. This data will comprise chapter 1 of my thesis. I next analysed bulk RNA-seq data of cells extracted from the lymph-nodes of TB-infected patients, to identify whether similar transcriptomic changes could be identified to chapter 1. As bulk RNA-seq data does not offer single-cell resolution, I used Louvain clustering of the genes in the dataset to identify genes that were closely associated through clustering. I created a novel R script that used the template signature identified in chapter 1 to identify whether other genes in the TB-lymph node data were associated with these genes by scanning through each of the clusters using different clustering parameter combinations. This identified 399 genes out of 5000+ that were consistently found in the same clusters. I next input a dendritic-cell signature I identified from GSEA of the TB-lymph node data into the script to validate whether my pipeline would identify the same cluster of genes. The script identified 411 genes that were consistently associated with the second signature, of which 386 were the same as those associated with the DC-signature from chapter 1. This indicated that while the two original signatures shared only 1 common gene, they were associated with the same cluster of genes, which overall could suggest that these clusters were related to dendritic cell function in the lymph nodes. To further confirm that my script was only identifying relevant genes, I generated a random signature of 30 genes from the data set and found that just 6 genes were consistently associated with this signature, indicating that there was little to no common association between randomly selected genes. The collec