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

研究结核分枝杆菌和树突状细胞相互作用的分子机制

• 批准号: 2455780
• 金额: --
• 依托单位: University of Southampton
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2455780
• 关键词: Investigating; molecular; mechanisms; underpin; M.

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

结核病（TB）仍然是对人类健康的一个相当大的威胁，特别是在医疗保健系统不发达的国家，是任何病原体感染发病率最高的国家之一。在大流行期间，公众关注点和医疗资源的转移只会增加感染率，这再次表明有必要加深我们对结核分枝杆菌（Mtb）与人类免疫系统之间相互作用的理解。巨噬细胞被认为是Mtb的主要靶免疫细胞，因为它既作为抗原呈递细胞又作为吞噬细胞，促进Mtb的细胞内存活和在全身的谨慎转运。树突状细胞也完成了这两个角色，其更多的功能是抗原呈递而不是吞噬，然而，与巨噬细胞相比，Mtb和树突状细胞相互作用的焦点被大大低估。我的项目旨在鉴定结核分枝杆菌和树突状细胞之间的新的相互作用，这些相互作用导致树突状细胞功能的改变。通过这项研究，我将试图确定这些相互作用对树突状细胞的存活是有益的还是致命的。我首先调查了公开数据集，这些数据集包含了树突状细胞被不同病原体（包括结核分枝杆菌）攻击后的数据，以确定高度（至少+/- LogFC 2）差异表达的基因的一致性特征，这些基因是结核分枝杆菌攻击的树突状细胞所特有的。我还使用了基因组富集分析（GSEA）和通路本体来捕获最广泛的细胞变化，这些变化是包含Mtb的数据集之间的常见变化。这为我提供了一组105个基因和10个通路，它们在3个数据集中的9种条件下是一致的。然后，我用THP-1细胞构建了一个简单的体外模型，并用5种刺激剂、3种病原体（M.结核C. albicans 和E. coli）、LPS、地塞米松，以观察是否可以使用一小组产生已知具有免疫调节作用的蛋白质（例如TNFA和IL 8）的基因来鉴定来自Mtb-攻击的独特应答。这些结果表明，虽然大肠杆菌和M.结核病，Mtb诱导了对IL 10的相反作用。这些数据将构成我论文的第一章。接下来，我分析了从TB感染患者的淋巴结中提取的细胞的大量RNA-seq数据，以确定是否可以确定与第1章相似的转录组变化。由于大量的RNA-seq数据不能提供单细胞分辨率，我使用了数据集中基因的Louvain聚类来识别通过聚类密切相关的基因。我创建了一个新的R脚本，它使用第1章中确定的模板签名，通过使用不同的聚类参数组合扫描每个聚类，来识别结核病淋巴结数据中的其他基因是否与这些基因相关。这鉴定了5000+个基因中的399个基因，这些基因一致地发现在相同的簇中。接下来，我将从TB淋巴结数据的GSEA中识别出的树突细胞特征输入脚本，以验证我的流水线是否能够识别出相同的基因簇。该脚本鉴定了411个与第二个信号一致相关的基因，其中386个与第一章中的DC信号相关的基因相同。这表明，虽然两个原始标记仅共享一个共同基因，但它们与相同的基因簇相关，这总体上可以表明这些簇与淋巴结中的树突状细胞功能相关。为了进一步证实我的脚本只是在识别相关基因，我从数据集中随机生成了30个基因的签名，发现只有6个基因与该签名一致相关，这表明随机选择的基因之间几乎没有共同的关联。学院