The contribution of signals 1, 2 & 3 on the metabolic phenotype of CD4+ T cells

T cell metabolism is differentially regulated in order to support their activation, replication and effector functions. For example, T cells preferentially upregulate glycolysis following activation and this is a crucial determining factor at the Th17/Treg axis. Productive T cell activation requires several signals namely 1) antigen, 2) co-stimulation, and 3) cytokines but if, and how, these influence the metabolic changes associated T cell activation is largely unknown. To investigate this we assessed the metabolic phenotypes of CD4+ T cells following activation with; 1) variable strengths of stimulation through antigen-T cell receptor (TCR) engagement, 2) incubation with CHO cells transfected with individual costimulatory molecules CD80 or CD86, 3) exposure to the inflammatory cytokine IL-6. Metabolism was assessed using NMR spectroscopy-based metabolomics and metabolic flux analysis to assess the balance between the major pathways of glycolysis and oxidative phosphorylation. We optimised the techniques for determining human CD4+ T cell metabolic phenotype and have observed quantitative and qualitative differences associated with variations in the activating signals 1 to 3. In particular, we found that exposure to IL-6 prior to TCR stimulation leads to an increase in glycolysis, a response which might prepare T cells for the metabolic burden following activation. Subsequent flow cytometry analysis demonstrates that IL-6 exposed cells have an increased proliferative phenotype, greater mitochondrial mass and markedly upregulate GLUT1, however, these changes were only observed in naïve cells and not memory. Abnormal T cell responses and systemically measurable dysregulated metabolism are hallmarks of autoimmune and inflammatory diseases such as rheumatoid arthritis. An improved understanding of T cell metabolism has implications for the treatment of these diseases and the mechanisms of action of biological therapies such as abatacept and tocilizumab which target T cell differentiation and function

Relationship Between Inflammation and Metabolism in Patients With Newly Presenting Rheumatoid Arthritis.

Background Systemic inflammation in rheumatoid arthritis (RA) is associated with metabolic changes. We used nuclear magnetic resonance (NMR) spectroscopy-based metabolomics to assess the relationship between an objective measure of systemic inflammation [C-reactive protein (CRP)] and both the serum and urinary metabolome in patients with newly presenting RA. Methods Serum (n=126) and urine (n=83) samples were collected at initial presentation from disease modifying anti-rheumatic drug naïve RA patients for metabolomic profile assessment using 1-dimensional H-NMR spectroscopy. Metabolomics data were analysed using partial least square regression (PLS-R) and orthogonal projections to latent structure discriminant analysis (OPLS-DA) with cross validation. Results Using PLS-R analysis, a relationship between the level of inflammation, as assessed by CRP, and the serum (p=0.001) and urinary (p<0.001) metabolome was detectable. Likewise, following categorisation of CRP into tertiles, patients in the lowest CRP tertile and the highest CRP tertile were statistically discriminated using OPLS-DA analysis of both serum (p=0.033) and urinary (p<0.001) metabolome. The most highly weighted metabolites for these models included glucose, amino acids, lactate, and citrate. These findings suggest increased glycolysis, perturbation in the citrate cycle, oxidative stress, protein catabolism and increased urea cycle activity are key characteristics of newly presenting RA patients with elevated CRP. Conclusions This study consolidates our understanding of a previously identified relationship between serum metabolite profile and inflammation and provides novel evidence that there is a relationship between urinary metabolite profile and inflammation as measured by CRP. Identification of these metabolic perturbations provides insights into the pathogenesis of RA and may help in the identification of therapeutic targets