Florey Department of Neuroscience and Mental Health - Theses
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ItemInvestigating ligand- and cell-specific signal transduction at relaxin family peptide receptor 1( 2021)Relaxin, a peptide hormone and the endogenous agonist of the relaxin family peptide receptor 1 (RXFP1), has shown substantial promise for the therapeutic treatment of cardiovascular disorders and fibrosis. RXFP1 has received considerable therapeutic interest as a drug target over the years, and a lot of effort has been put into the development of novel ligands targeting this receptor. However, we do not understand how novel RXFP1 agonists work because we do not understand RXFP1 signalling in enough detail, and do not fully understand which pathways are important for the therapeutic actions, where they are activated in the signal transduction cascade, and in what cell types. Furthermore, development of novel ligands raises the question of biased signalling, which is the ability of different ligands for the same receptor to preferentially activate certain signal transduction pathways relative to one another. It may be possible to utilise bias to create effective drugs that have fewer side effects, but this requires us to first understand which effectors and signal transduction pathways are important for the therapeutic versus harmful actions. Furthermore, recent research has highlighted the importance of measuring the temporal aspects of signalling in order to understand bias, as signalling and bias can change over time, and using end point assays can produce a misleading picture of efficacy and bias depending on which time point was chosen. Additionally, validating findings from recombinant cells in physiologically-relevant cells is also important to understand how ligands signal across cell types, and to distinguish biased signalling from cell-specific signalling. The development of sensitive, real-time assays that can be used across cell types will aid our understanding of the molecular mechanisms underlying the actions of relaxin and other ligands targeting this promising receptor. The general aim of this thesis was to apply and develop bioluminescence resonance energy transfer (BRET)-based methods in conjunction with human native and primary cells in order to examine real-time signalling and bias at RXFP1. First, the functionality and versatility of the CAMYEL (cAMP sensor using YFP-Epac-Rluc) real-time BRET-based cAMP biosensor was demonstrated for RXFP1 and the related GPCRs RXFP2, RXFP3, and RXFP4. CAMYEL was a sensitive alternative to end point assays, as it detected concentration-dependent changes in cAMP activity at all receptors in recombinant cell lines, was dynamic and reversible, detected kinetic differences between different ligands for the same receptor, and showed potencies comparable to those seen in end point assays. CAMYEL was cloned into a lentiviral vector, and lentivirus was used to transduce THP-1 cells, which endogenously express low levels of RXFP1. THP-1 CAMYEL cells showed robust cAMP activation after relaxin stimulation and will therefore streamline the process of screening novel RXFP1 ligands. The lentiviral vector will also allow for the transduction of many mammalian cell types for real-time analysis of cAMP activity at various GPCRs, including in primary cells. However, it appeared that the CAMYEL assay was unable to detect a delayed, Gi3-mediated phase of RXFP1 cAMP activity that has been demonstrated using other assays, suggesting that CAMYEL might not detect cAMP generated in specific compartments of the cell. Second, we developed, validated, and characterised a BRET-based biosensor for cGMP activity, known as CYGYEL (cyclic GMP sensor using YFP-PDE5-Rluc8), based on the Forster/fluorescence resonance energy transfer (FRET) biosensor cGES-DE5. CYGYEL was cloned into a lentiviral vector, enabling its use across different mammalian cell types. CYGYEL was initially characterised in HEK293T cells, where it was shown to be sensitive, dynamic, reversible, and also very selective for the detection of cGMP over cAMP. CYGYEL was then used to detect cGMP after transduction of human primary vascular cells, namely endothelial and smooth muscle cells. CYGYEL detected differences in cGMP signalling kinetics both between cell types, and also between ligands that increased cGMP production via soluble versus membrane guanylate cyclases. So far we have been unsuccessful at detecting GPCR-mediated cGMP using CYGYEL, but further work is required in this area. Regardless, CYGYEL still has many uses for drug discovery. Finally, we used a variety of BRET-based assays for G protein association, second messenger activity, and ERK1/2 activity, as well as physiologically-relevant primary cells, in order to understand the mechanisms of action underlying the beneficial actions of the relaxin peptide analogue B7-33. According to previous work, B7-33 appeared to show cell-specific signalling and biological responses, whereby it had weak activity in recombinant and cancer cells, but potent activity in fibroblasts and vascular cells, as well as in vivo. Our results across several cell types indicated that B7-33 is a biased agonist that favours signalling via Gi3/cGMP over Gs/cAMP, relative to relaxin which signals potently via both pathways. These findings are consistent with B7-33’s actions as a potent vasodilator and anti-fibrotic, which depend on cGMP rather than cAMP. Relatedly, we demonstrated that B7-33 shows transient cAMP activity relative to relaxin in all cell types tested, and that in a real-time cAMP assay involving ligand washout, the cAMP response from B7-33 dropped drastically relative to relaxin, suggesting that B7-33 dissociated from RXFP1 far more readily than relaxin. We thus hypothesised that the bias shown by B7-33 is related to kinetics, whereby the relaxin/RXFP1 complex catalyses more cycles of Gs activation due to the sustained duration of the active receptor conformation, relative to B7-33 which has a faster off-rate associated with its weaker activation of Gs. However, both agonists equally activate Gi3 suggesting that the relative rates of activation and deactivation of the different G proteins may also be important. Finally, it was also observed that ERK1/2 is activated by its upstream effectors in a cell type-specific manner. Specifically, whereas previous findings have shown that ERK1/2 is primarily downstream of Gi/o in native cells, our findings show that ERK1/2 is activated downstream of Gs in HEK-RXFP1 cells, which explains B7-33’s weak ERK1/2 activation in HEK-RXFP1 cells but potent activation in native cells. These findings have implications for the development of novel biased drugs targeting RXFP1, as it is believed that the negative actions of exogenously-administered relaxin, including for example its ability to promote tumour growth in mouse models in vivo, are related to its potent cAMP activity. Conversely, equi-molar doses of B7-33 do not promote tumour growth but do retain the beneficial actions of relaxin which occur via Gi. Thus, we could potentially aim to retain the kinetic bias to maintain potent cGMP signalling, while minimising cAMP activity, and at the same time aim to develop compounds that are more drug-like with longer half-lives.
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ItemStudies on the mechanism of binding and activation of relaxin family peptide receptors( 2020)The peptide hormone relaxin is involved in reproductive processes but has also been investigated for several decades as a treatment for a range of disease states such as scleroderma, acute heart failure, and fibrotic conditions. The receptor for relaxin, RXFP1, is an integral membrane protein belonging to the G protein coupled receptor (GPCR) family. RXFP1 is therefore a therapeutically tractable target for which a thorough understanding of its mechanism of binding and activation is required to develop better relaxin-like drugs. The aims of these studies are to investigate the mechanism by which relaxin binds and activates RXFP1 using a variety of molecular pharmacology approaches in a HEK293T cell model system recombinantly expressing RXFP1 in various forms. Specifically, a hypothesis was tested that a homodimer of RXFP1 might be the minimal functional unit required for receptor activation. GPCR dimers are postulated to interact via their transmembrane helices, so initial investigations aimed to disrupt RXFP1 homodimerisation by incorporation of peptides representing single transmembrane segments of RXFP1 as well as recombinant expression of RXFP1 transmembrane domains. There was no evidence that RXFP1 homodimerisation is required for receptor activation. Following this, the evidence for RXFP1 homodimerisation was re-evaluated in the development of two methods which utilise principles of Bioluminescence Resonance Energy Transfer (BRET). Firstly, split Nanoluciferase was used to tag cell surface localised RXFP1 receptors in combination with mCitrine-tagged RXFP1 and BRET was measured to assess relative receptor proximity. This indicated that RXFP1 is unlikely to be a stable homodimer, intracellularly localised receptors predominate, and there is no change in receptor:receptor proximity upon relaxin stimulation. Secondly, Nanoluciferase-tagged RXFP1 receptors were used in combination with fluorescently labelled relaxin and BRET was measured to track relaxin:RXFP1 binding interactions. This allowed sensitive, real time measurements of the relaxin:RXFP1 binding interactions, demonstrating a multi-step mechanism of relaxin binding in which the linker domain of RXFP1 is critical for high-affinity interactions. Furthermore, there was no evidence of negative co-operativity of relaxin binding, contrary to previous reports which were used as evidence of RXFP1 homodimerisation. Overall, these studies indicate that relaxin does not activate RXFP1 via a mechanism involving a receptor homodimer. Several molecular tools were developed which will be useful for future investigations into RXFP1 pharmacology. This work adds incremental detail to the understanding of how relaxin activates RXFP1, hopefully leading to the development of novel therapeutically useful relaxin-like molecules in future.
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ItemDetection of Phenotypic Network Signatures in Cultured Neuronal Networks( 2017)Neuronal cultures grown on multielectrode arrays (MEAs) capture the electrical signals from neuronal networks of thousands of neurons in parallel, thus providing insight into interconnections and network activity. Though the MEA technology is advancing rapidly, the knowledge discovery pipeline is still at a stage where only a fraction of the information contained in these signals is being used. Therefore, this thesis introduces an end-to-end workflow that uses this network information to find mechanisms of action of drugs. It initially extracts features that characterize different aspects of neuronal activity that can be used to characterize network states. This utilizes existing feature extraction methods as well as novel methods that are adaptive to activity patterns in unperturbed and perturbed network states. These features are then used to build network signatures that allow novel compounds to be compared with compounds with known mechanisms of action. Through this process, the mechanisms of action of conolidine and cannabidiol, two plant-derived compounds with analgesic properties are identified as targeting N-Type calcium channels which are proven molecular targets responsible for creating analgesia. The usability of MEAs for drug discovery is explored further, by assessing whether common patterns exist between drugs with similar mechanisms of action and clinical use by using a set of antiepileptic drugs. In conclusion, this research proves that MEA-based workflows can assist in rapid and efficient screening of pharmacological compounds, making them a useful addition to drug development pipelines.
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Item‘Somnivore’ a user-friendly platform for automated scoring and analysis of polysomnography data( 2016)The low-throughput nature of manual scoring of polysomnography (sleep) data, both in terms of speed and consistency, is a major factor preventing sleep research from reaching its full efficiency and potential. Automated approaches developed previously have generally failed to provide sufficient accuracy or 'usability' for sleep scientists lacking specialist-engineering expertise. Moreover, all earlier approaches have only been validated using baseline data, suggesting a failure to embed in the algorithm the robustness to remain effective when used to analyse the effect on sleep of treatment or disease. Finally, no single approach has been validated for mouse, rat and human data. Therefore, the aim of my research was to develop a user-friendly platform for real-time automated scoring and analysis of polysomnography data. The program is known as ‘Somnivore’ (from Latin somnus, ‘sleep’, and vorare, ‘to devour’), and was developed using state of the art supervised machine learning technology, with support vector machine (SVM) at its core, and coded as a graphical user interface (GUI)-based solution in the Matlab™ ambient. Somnivore learns, in parallel, by surveying features from a variety of different inputs (including EEG, EMG, EOG and ECG) and outputs data into the various sleep stages (wake, NREM, N1, N2, N3, REM). The classifier is trained for each subject via a brief session of manual scoring. Design and development strategies were built around both theoretical and heuristic approaches. This led to a multi-layered system capable of learning from extremely limited training sets, using large input space dimensionalities from a rich variety of polysomnography inputs, and with rapid computational times. Validation was pursued to approach the numerous contentious dynamics that have led to the demise of previous solutions. Somnivore generalisation was evaluated at the level of canonical classifier evaluation metrics such as F-measure, as well as experimental end-measures more germane to traditional biological sleep research. Somnivore, generated superior generalisation, with high power, on both murine (n = 54) and human (n = 52) recordings. These included multiple rat strains (Sprague-Dawley, Wistar) and mouse phenotypes (wild type, orexin neuron-ablated transgenic), various pharmacological interventions (placebo, alcohol, muscimol, caffeine, zolpidem, almorexant), and in humans, both genders, younger and older subjects, and subjects with mild cognitive impairment (MCI). Somnivore’s generalisation was also evaluated in conditions of signal challenged data, and provided excellent performance in all conditions using only one EEG channel for learning. Remarkable results were also reported for learning undertaken using only one EMG channel or two EOG channels. Furthermore, validation studies highlighted that a substantial part of the disagreement between manual and automated hypnograms was located within transition epochs. As Somnivore has several features geared towards the management of transition epochs, further control over generalisation is also possible. Comprehensive inter-scorer agreement analysis was conducted on human data, showcasing how inter-scorer agreement between manual hypnograms and their automated counterparts provided by Somnivore is comparable to the gold-standard of the inter-scorer agreement between two experts trained in the same laboratory. Results also highlighted critical problems within the scoring of stage N1. However, inter-scorer agreement validation studies also confirmed what has already been reported in the literature, that N1 is a volatile stage that systematically produces inadequate agreement even between trained experts, both within or outside the same laboratory. Accordingly, Somnivore performed as well on N1 as reported in the literature for manually scored data. Due to the high-throughput nature of Somnivore’s analyses of experimental end-measures, several novel, cautionary findings were extracted from the recordings provided by external laboratories for this research evaluations. Additionally, as Somnivore is also capable of scoring real-time during polysomnography recordings, it will facilitate the development of more advanced protocols such as biofeedback sleep-deprivation protocols and integrated optogenetics. In conclusion, Somnivore, has been comprehensively validated as an accurate, reliable, high-throughput solution for scoring and analysis of polysomnography data, in a range of experimental situations including studies of normal physiology and tests related to drug discovery for the improved treatment of sleep disorders and psychiatric diseases.