140 Chapter 5 Metabolism is the process through which the body transforms food and drink into energy by means of chemical reactions. Entire organisms and all living cells require energy, even when in a resting state. Glycolysis was the first metabolic pathway to be described in the 1940s [1]. Newsholme first studied the influence of metabolic fluxes on metabolic pathways [2] and postulated that when product supply and product formation are maintained, a steady-state condition is established with metabolic intermediates being at a constant level. However, in an organism, its organs, and the cells that compose them, steady-state conditions are almost never reached and levels of metabolites change depending on numerous circumstances. In fact, changes in cellular metabolism can contribute to complex pathological events, such as metabolic syndrome, diabetes, and cancer. The same is true the other way around; disease states result in changes in metabolism [3]. MACROPHAGE METABOLISM IS TISSUE-NICHE SPECIFIC. Macrophages are cells of the innate immune system that are present in all tissues and can rapidly answer to different stimuli. This requires them to adapt to different nutritional resources and change their energy source by going through a process defined as metabolic reprogramming. Macrophage metabolic reprogramming has mostly been associated with the classical and alternative activation states of macrophage polarization [4–7]. However, it is now believed that there is a continuous spectrum of macrophage phenotypes [8]. In this thesis, I show that the traditional view of macrophage metabolic reprogramming is too simplistic. A combination of environmental and pathogenic signals determines macrophage phenotype and metabolism, which in turn shapes the type and magnitude of the response. This aspect has been investigated in the current thesis (Chapters 3 and 4). To gain an overview of the impact of a given tissue niche on macrophage metabolism, I used single cell RNA-seq data from the human protein atlas database (proteinatlas. org) and Ensembl version 103.38 to investigate differences in macrophage metabolic gene expression in different tissues [9]. I filtered for macrophages only and used the Read.count values (representing transcript abundances) multiplied for the nTPM (normalized Transcripts Per Million for each human cell type, used to estimate the gene expression level) and averaged the results if more entries were available for each tissue. After analyzing the data, I created a Gene-Z-Score and corresponding heatmap. The Z score helps determine if a gene's expression level is higher or lower than the mean across all tissues. Interestingly, major differences were found in the expression levels of genes coding for proteins that play important roles in energy metabolism belonging to the TCA cycle, the electron transport chain, and the glycolytic pathway in 20 different tissues (as shown in Figure 1). Through
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