Macrophages and CD4 T-cells in rheumatoid arthritis and their modulation by JAK inhibitors

Background: Rheumatoid arthritis (RA) is a chronic inflammatory arthritis that causes significant morbidity and mortality and has no cure. Although early treatment strategies and biologic therapies such as TNFα blocking antibodies have revolutionised treatment, there still remains considerable unmet need. JAK kinase inhibitors, which target multiple inflammatory cytokines, have shown efficacy in treating RA although their exact mechanism of action remains to be determined. Stratified medicine promises to deliver the right drug to the right patient at the right time by using predictive ‘omic biomarkers discovered using bioinformatic and “Big Data” techniques. Therefore, knowledge across the realms of clinical rheumatology, applied immunology, bioinformatics and data science is required to realise this goal. Aim: To use bioinformatic tools to analyse the transcriptome of CD14 macrophages derived from patients with inflammatory arthritis and define a JAK/STAT signature. Thereafter to investigate the role of JAK inhibition on inflammatory cytokine production in a macrophage cell contact activation assay. Finally, to investigate JAK inhibition, following RA synovial fluid stimulation of monocytes. Methods and Results: Using bioinformatic software such as limma from the Bioconductor repository, I determined that there was a JAK/STAT signature in synovial CD14 macrophages from patients with RA and this differed from psoriatic arthritis samples. JAK inhibition using a JAK1/3 inhibitor tofacitinib reduced TNFα production when macrophages were cell contact activated by cytokine stimulated CD4 T-cells. Other pro-inflammatory cytokines such as IL-6 and chemokines such as IP-10 were also reduced. RA synovial fluid failed to stimulate monocytes to phosphorylate STAT1, 3 or 6 but CD4 T-cells activated STAT3 with this stimulus. RNA sequencing of synovial fluid stimulated CD4 T-cells showed an upregulation of SOCS3, BCL6 and SBNO2, a gene associated with RA but with unknown function and tofacitinib reversed this. Conclusion: These studies demonstrate that tofacitinib is effective at reducing inflammatory mediator production in a macrophage cell contact assay and also affects soluble factor mediated stimulation of CD4 T-cells. This suggests that the effectiveness of JAK inhibition is due to inhibition of multiple cytokine pathways such as IL-6, IL-15 and interferon. RNA sequencing is a useful tool to identify non-coding RNA transcripts that are associated with synovial fluid stimulation and JAK inhibition but these require further validation. SBNO2, a gene that is associated with RA, may be biomarker of tofacitinib treatment but requires further investigation and validation in wider disease cohorts.

Filling in gaps of Drosophila melanogaster urate degradation metabolic pathway using metabolomics approaches: towards the core metabolome of the fruit fly

The primary goal of systems biology is to integrate complex omics data, and data obtained from traditional experimental studies in order to provide a holistic understanding of organismal function. One way of achieving this aim is to generate genome-scale metabolic models (GEMs), which contain information on all metabolites, enzyme-coding genes, and biochemical reactions in a biological system. Drosophila melanogaster GEM has not been reconstructed to date. Constraint-free genome-wide metabolic model of the fruit fly has been reconstructed in our lab, identifying gaps, where no enzyme was identified and metabolites were either only produced or consume. The main focus of the work presented in this thesis was to develop a pipeline for efficient gap filling using metabolomics approaches combined with standard reverse genetics methods, using 5-hydroxyisourate hydrolase (5-HIUH) as an example. 5-HIUH plays a role in urate degradation pathway. Inability to degrade urate can lead to inborn errors of metabolism (IEMs) in humans, including hyperuricemia. Based on sequence analysis Drosophila CG30016 gene was hypothesised to encode 5- HIUH. CG30016 knockout flies were examined to identify Malpighian tubules phenotype, and shortened lifespan might reflect kidney disorders in hyperuricemia in humans. Moreover, LC-MS analysis of mutant tubules revealed that CG30016 is involved in purine metabolism, and specifically urate degradation pathway. However, the exact role of the gene has not been identified, and the complete method for gap filling has not been developed. Nevertheless, thanks to the work presented here, we are a step closer towards the development of a gap-filling pipeline in Drosophila melanogaster GEM. Importantly, the areas that require further optimisation were identified and are the focus of future research. Moreover, LC-MS analysis confirmed that tubules rather than the whole fly were more suitable for metabolomics analysis of purine metabolism. Previously, Dow/Davies lab has generated the most complete tissue-specific transcriptomic atlas for Drosophila – FlyAtlas.org, which provides data on gene expression across multiple tissues of adult fly and larva. FlyAtlas revealed that transcripts of many genes are enriched in specific Drosophila tissues, and that it is possible to deduce the functions of individual tissues within the fly. Based on FlyAtlas data, it has become clear that the fly (like other metazoan species) must be considered as a set of tissues, each 2 with its own distinct transcriptional and functional profile. Moreover, it revealed that for about 30% of the genome, reverse genetic methods (i.e. mutation in an unknown gene followed by observation of phenotype) are only useful if specific tissues are investigated. Based on the FlyAtlas findings, we aimed to build a primary tissue-specific metabolome of the fruit fly, in order to establish whether different Drosophila tissues have different metabolomes and if they correspond to tissue-specific transcriptome of the fruit fly (FlyAtlas.org). Different fly tissues have been dissected and their metabolome elucidated using LC-MS. The results confirmed that tissue metabolomes differ significantly from each other and from the whole fly, and that some of these differences can be correlated to the tissue function. The results illustrate the need to study individual tissues as well as the whole organism. It is clear that some metabolites that play an important role in a given tissue might not be detected in the whole fly sample because their abundance is much lower in comparison to other metabolites present in all tissues, which prevent the detection of the tissue-specific compound.