Identifying the molecular signatures that shape the course of synovial pathology in inflammatory arthritis.

Advances in precision medicine offer exciting opportunities to improve healthcare provision and clinical decision-making. Here, developments in diagnostic capabilities provide greater insights into the mechanisms of disease progression and allow the stratification of patients for the selection of therapies for optimal treatment. Innovations in precision medicine, therefore, contribute to improved clinical outcomes, a patient’s quality of life, and health economics. Experiment presented in this investigated the development of bioinformatic tools that could be used to stratify patients based on transcriptomic data derived inflamed tissues. To support this approach, I used open access repository datasets from patients with rheumatoid arthritis. Rheumatoid arthritis (RA) is a chronic and systemic autoimmune disease that affects around 1% of the adult population. Here, inflammation of the joint (synovitis) drives disease progression and irreversible joint damage. The clinical presentation of synovitis is highly heterogeneous with distinct histological features that affects the response commonly used therapeutics (e.g., biological drugs against cytokines). Examination of synovial histopathology reveals three forms of the disease termed Follicular – with extensive infiltration and the presence of lymphoid aggregates; Diffuse – extensive infiltration but with relatively few B cells; and Pauci-immune – which is driven by the stromal tissue compartment. Using transcriptomic data from each of these pathologies I designed and validated a disease classifier that supports the stratification of disease and the interrogation of results from independent patient cohorts where batch effects often restrict interpretations. Thus, I now present a tool that allows discrimination of these pathologies according to synovial transcriptomic data. Results presented in this thesis identified two gene signatures that perform well as identifiers of follicular and pauci-immune synovitis. The characterisation of diffuse synovitis is, however, more challenging and the application of the disease classifier tools showed that this form of pathology comprises a spectrum of sub-pathologies that require further characterisation. In an extension of these studies I further show how these bioinformatic tools may be used to record patient responses to biological drug therapy and unearth the biological signal pathways responsible for disease progression. Whilst these studies have focussed on RA as a case study, the methodologies are disease agnostic, and offer exciting opportunities for additional applications in other disease settings

Determination of a microRNA signature of protective kidney ischemic preconditioning originating from proximal tubules

Ischemic preconditioning (IPC) is effective in limiting subsequent ischemic acute kidney injury in experimental models. MicroRNAs are an important class of post-transcriptional regulator and show promise as biomarkers of kidney injury. We evaluated the time- and dose-dependence of benefit from IPC in a rat model of functional (bilateral) ischemia–reperfusion injury (IRI). We found optimal protection from subsequent injury following short, repetitive sequences of preconditioning insult. We subsequently used hybridization array and microRNA sequencing to characterize microRNA signatures of protective IPC and of IRI. These approaches identified a profile of microRNA changes consequent on IRI, that were limited by prior IPC. To localize these signals within the kidney, we used laser capture microdissection and RT-qPCR to measure microRNA abundance in nephron segments, pinpointing microRNA changes principally to glomeruli and proximal tubules. Our data describe a unique microRNA signature for IRI in the rat kidney. Pulsatile IPC reduces kidney damage following IRI and diminishes this microRNA signal. We have also identified candidate microRNAs that may act as biomarkers of injury and therapeutic targets in this context

Tissue-specific transcriptional programming of macrophages controls the microRNA transcriptome targeting multiple functional pathways

Recent interest in the biology and function of peritoneal tissue resident macrophages (pMΦ) has led to a better understanding of their cellular origin, programming and renewal. The programming of pMΦ is dependent on microenvironmental cues and tissue specific transcription factors, including GATA6. However, the contribution of microRNAs remains poorly defined. We conducted a detailed analysis of the impact of GATA6-deficiency on microRNA expression in mouse pMΦ. Our data suggest that for many of the pMΦ, microRNA composition may be established during tissue specialization, and that the effect of GATA6 knockout is largely unable to be rescued in the adult by exogenous GATA6. The data are consistent with GATA6 modulating the expression pattern of specific microRNAs, directly or indirectly, and including miR-146a, -223, and -203 established by the lineage-determining transcription factor PU.1, to achieve a differentiated pMΦ phenotype. Lastly, we showed a significant dysregulation of miR-708 in pMΦ in the absence of GATA6 during homeostasis and in response to LPS/IFN-γ stimulation. Overexpression of miR-708 in mouse pMΦ in vivo altered 167 mRNA species demonstrating functional downregulation of predicted targets, including cell immune responses and cell cycle regulation. In conclusion, we demonstrate dependence of the microRNA transcriptome on tissue-specific programming of tissue macrophages as exemplified by the role of GATA6 in pMΦ specialization

Quantifying secondary structure changes in Calmodulin using 2D-IR spectroscopy

Revealing the details of biomolecular processes in solution needs tools that can monitor structural dynamics over a range of time and length scales. We assess the ability of 2D-IR spectroscopy in combination with multivariate data analysis to quantify changes in secondary structure of the multifunctional calcium-binding messenger protein Calmodulin (CaM) as a function of temperature and Ca2+ concentration. Our approach produced quantitative agreement with circular dichroism (CD) spectroscopy in detecting the domain melting transitions of Ca2+-free (apo) CaM (reduction in α-helix structure by 13% (CD) and 15% (2D)). 2D-IR also allows accurate differentiation between melting transitions and generic heating effects observed in the more thermally-stable Ca2+-bound (holo-) CaM. The functionally-relevant random-coil-α-helix transition associated with Ca2+ uptake that involves just 7-8 out of a total of 148 amino acid residues was clearly detected. Temperature-dependent Molecular Dynamics (MD) simulations show that apo-CaM exists in dynamic equilibrium with holo-like conformations while Ca2+ uptake reduces conformational flexibility. The ability to combine quantitative structural insight from 2D-IR with MD simulations thus offers a powerful approach for measuring subtle protein conformational changes in solution

Small Molecules That Inhibit Tnf Signalling by Stabilising an Asymmetric Form of the Trimer

Tumour necrosis factor (TNF) is a cytokine belonging to a family of trimeric proteins; it has been shown to be a key mediator in autoimmune diseases such as rheumatoid arthritis and Crohn\u27s disease. While TNF is the target of several successful biologic drugs, attempts to design small molecule therapies directed to this cytokine have not led to approved products. Here we report the discovery of potent small molecule inhibitors of TNF that stabilise an asymmetrical form of the soluble TNF trimer, compromising signalling and inhibiting the functions of TNF in vitro and in vivo. This discovery paves the way for a class of small molecule drugs capable of modulating TNF function by stabilising a naturally sampled, receptor-incompetent conformation of TNF. Furthermore, this approach may prove to be a more general mechanism for inhibiting protein-protein interactions

Th1 cells alter the inflammatory signature of IL-6 by channeling STAT transcription factors to Alu-like retroelements

Cytokines that signal via STAT1 and STAT3 transcription factors instruct decisions affecting tissue homeostasis, anti-microbial host defense, and inflammation-induced tissue injury. To understand the coordination of these activities, we applied RNA-seq, ChIP-seq, and ATAC-seq to identify the transcriptional output of STAT1 and STAT3 in peritoneal tissues during acute resolving inflammation and inflammation primed to drive fibrosis. Bioinformatics focussed on the transcriptional signature of the immuno-modulatory cytokine IL-6 in both settings and examined how pro-fibrotic IFNg- secreting CD4+ T-cells altered the interpretation of STAT1 and STAT3 cytokine cues. In resolving inflammation, STAT1 and STAT3 cooperated to drive stromal gene expression affecting anti-microbial immunity and tissue homeostasis. The introduction of IFNg-secreting CD4+ T-cells altered this transcriptional program and channeled STAT1 and STAT3 to a previously latent GAS motif in Alu-like elements. STAT1 and STAT3 binding to this conserved sequence revealed evidence of reciprocal cross-regulation and gene signatures relevant to pathophysiology. Thus, we propose that effector T-cells re-tune the transcriptional output of IL-6 by shaping a regulatory interplay between STAT1 and STAT3 in inflammation

The Use of Fast Free Energy Methods in Rational Drug Design

The computationally demanding nature and lack of generality of free energy methods are the main barriers to their common place use in rational drug design. This study investigates the possibility of producing protocols to accurately calculate the binding free energy of protein-ligand complexes more efficiently than presently established methods, using large scale distributed computing. There has been an explosion of useful nonequilibrium work methods recently, mainly due to the discovery of the Jarzynski equilibrium [Jarzynski(1997b)]. After an indepth investigation of these methods a subset, all with the possibility of large scale parallelisation, was chosen for further study. Also, replica exchange fast growth (REFG), was developed, a method which combines replica exchange and fast growth methods in a similar way to replica exchange thermodynamic integration (RET!) [Woods et al.(2003a)Woods, Essex & King]. These methods of interest-were applied to a large number of harmonic oscillator systems and compared to the established me~hod TI. Those methods deemed to perform best were then applied to some simple solute-solvent test systems and compared to the established method RET!. The best performing method from these studies was then.compared to RET! for the calculation of relative binding free energies of two sets of cogeneric inhibitors bound to their receptor proteins. REFG was found to perform as well as RET! and produce constantly predictive results. REFG was able to produce these results in significantly less wall clock time by using large scale distributed computing.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

The use of fast free energy methods in rational drug design

The computationally demanding nature and lack of generality of free energy methods are the main barriers to their common place use in rational drug design.  This study investigates the possibility of producing protocols to accurately calculate the binding free energy of protein-ligand complexes more efficiently than presently established methods, using large scale distributed computing.  There has been an explosion of useful nonequilibrium work methods recently, mainly due to the discovery of the Jarzynski equilibrium.  After an in depth investigation of these methods, a subset, all with the possibility of large scale parallelisation, was chosen for further study.  Also, replica exchange fast-growth (REFG), was developed, a method which combines replica exchange and fast growth methods in a similar way to replica exchange thermodynamic integration (RETI).  These methods of interest were applied to a large number of harmonic oscillator systems and compared to the established method TI.  Those methods deemed to perform best were then applied to some simple solute-solvent test systems and compared to the established method RETI.  The best performing method from these studies was then compared to RETI for the calculation of relative binding free energies of two sets of cogeneric inhibitors bound to their receptor proteins.  REFG was found to perform as well as RETI and produce constantly predictive results.  REFG was able to produce these results in significantly less wall clock time by using large scale distributed computing.</p