http://hdl.handle.net/11343/159 2019-09-16T02:51:05Z http://hdl.handle.net/11343/227639 Transcriptomic diversification along the monocyte-macrophage continuum. Butcher, Suzanne Kathryn Current models of innate immune responses describe hard wired, gene-centric signalling networks, with limited capacity to define the molecular mechanisms underpinning transcriptomic diversity. It is well established that a transcriptional spectrum of responses accompanies acute macrophage activation, however it is unclear whether this spectrum originates in monocytes and to what extent it continues throughout reinfection. The contribution of molecular mechanisms such as enhancers and alternative transcription start sites is also undetermined. Given the critical roles that myeloid cells play in directing acute infection and priming adaptive immunity, it is important the regulation of their responses be understood. This thesis employed bioinformatic analysis of Cap Analysis Gene Expression (CAGE) and microarray data to describe transcriptional diversity along the monocyte-macrophage continuum. Using CAGE to map transcription start sites for capped RNAs, this thesis has shown that pathogen-specific transcriptional diversification commences early in monocyte infection (Chapters Three and Four) and continues throughout acute macrophage infection (Chapter Five). Transcriptomic diversity during acute infection was the product of kinetic and pathogen-specific engagement of distinct transcription start sites. Engagement of multiple transcription start sites drove responsiveness by regulating expression amplitude in functionally focused inflammatory gene sets and diversifying secondary response networks via expression of distinct protein isoforms. Chapter Six extended these studies of acute infection, demonstrating that transcriptional phenotypes continue to diversify during reinfection. These findings highlight the importance of studying innate immune responses at the isoform level and prompt the need to revise current models of innate immune signalling, such that monocyte-macrophage biology should no longer be modelled as a series of static states, but rather, as a continually evolving continuum. © 2019 Suzanne Kathryn Butcher 2019-01-01T00:00:00Z http://hdl.handle.net/11343/227638 Cardiorespiratory and Autonomic Function in Epilepsy: Implications for Clinical Practice Sivathamboo, Shobitha Epilepsy is one of the most common serious neurological disorders in the world, affecting over 70 million people worldwide. It is associated with an increased risk of premature mortality as a direct or indirect consequence of seizures, epilepsy, comorbidities, and treatments for epilepsy. Sudden unexpected death in epilepsy (SUDEP) is the leading cause of death in epilepsy, where proposed mechanisms are thought to be seizure-induced brainstem, arousal, and cardiorespiratory dysfunction. Dysfunction to cardiorespiratory and autonomic function has long been recognized, and growing evidence points to its implication in SUDEP. However, comprehensive studies using multimodal physiological monitoring are limited, and a better understanding may improve outcomes in patients with epilepsy. The overall aim of this thesis was to investigate cardiorespiratory and autonomic function in patients with epilepsy to determine the relevance that this may have for outcomes in patients with epilepsy. Firstly, I examined the prevalence and risk factors for sleep-disordered breathing in patients routinely screened using diagnostic polysomnography who were admitted for video-encephalogram (EEG) monitoring, using current guidelines and recommended polysomnography techniques. This study identified a high prevalence of clinically significant sleep-disordered breathing in this population and risk-factors to help guide selection of patients for referral to laboratory-based polysomnography where concurrent video-EEG monitoring and polysomnography is unavailable. Secondly, I examined mortality in patients who had a diagnosis of epilepsy and/or PNES who were found to have sleep-disordered breathing in the first study. Though a higher proportion of deceased patients with clinically significant sleep-disordered breathing compared to living patients was found, we did not find an association between sleep-disordered breathing and mortality. Larger studies with a greater follow-up duration are warranted. Thirdly, I examined cardiorespiratory and autonomic function in patients with epilepsy before, during, and after convulsive and non-convulsive seizures. This study demonstrates substantial cardiorespiratory and autonomic dysfunction among convulsive seizures which may explain why these seizures carry the greatest risk of SUDEP. Lastly, I examined the incidence and frequency of cardiac arrhythmias in a population with drug-resistant chronic epilepsy using implantable cardiac monitors. This study identified that patients with chronic drug resistant epilepsy have a high incidence of cardiac arrhythmias. Two patients had potentially fatal cardiac arrhythmias requiring further cardiology management including sinus arrest and ventricular asystole requiring permeant pacemaker insertion, and polymorphic wide complex tachycardia associated with febrile tonic-clonic seizures. Overall, the findings of this thesis demonstrate cardiorespiratory and autonomic dysfunction are common in drug-resistant epilepsy and warrants the more widespread application of multimodality physiological monitoring including incorporating continuous respiratory monitoring and polysomnography in epilepsy monitoring units, which is not currently standard practice. © 2019 Shobitha Sivathamboo 2019-01-01T00:00:00Z http://hdl.handle.net/11343/227637 Generalizability of A Neural Network Model for Circadian Phase Prediction in Real-World Conditions Stone, JE; Phillips, AJK; Ftouni, S; Magee, M; Howard, M; Lockley, SW; Sletten, TL; Anderson, C; Rajaratnam, SMW; Postnova, S A neural network model was previously developed to predict melatonin rhythms accurately from blue light and skin temperature recordings in individuals on a fixed sleep schedule. This study aimed to test the generalizability of the model to other sleep schedules, including rotating shift work. Ambulatory wrist blue light irradiance and skin temperature data were collected in 16 healthy individuals on fixed and habitual sleep schedules, and 28 rotating shift workers. Artificial neural network models were trained to predict the circadian rhythm of (i) salivary melatonin on a fixed sleep schedule; (ii) urinary aMT6s on both fixed and habitual sleep schedules, including shift workers on a diurnal schedule; and (iii) urinary aMT6s in rotating shift workers on a night shift schedule. To determine predicted circadian phase, center of gravity of the fitted bimodal skewed baseline cosine curve was used for melatonin, and acrophase of the cosine curve for aMT6s. On a fixed sleep schedule, the model predicted melatonin phase to within ± 1 hour in 67% and ± 1.5 hours in 100% of participants, with mean absolute error of 41 ± 32 minutes. On diurnal schedules, including shift workers, the model predicted aMT6s acrophase to within ± 1 hour in 66% and ± 2 hours in 87% of participants, with mean absolute error of 63 ± 67 minutes. On night shift schedules, the model predicted aMT6s acrophase to within ± 1 hour in 42% and ± 2 hours in 53% of participants, with mean absolute error of 143 ± 155 minutes. Prediction accuracy was similar when using either 1 (wrist) or 11 skin temperature sensor inputs. These findings demonstrate that the model can predict circadian timing to within ± 2 hours for the vast majority of individuals on diurnal schedules, using blue light and a single temperature sensor. However, this approach did not generalize to night shift conditions. 2019-07-29T00:00:00Z http://hdl.handle.net/11343/227636 Application of a Limit-Cycle Oscillator Model for Prediction of Circadian Phase in Rotating Night Shift Workers Stone, JE; Aubert, XL; Maass, H; Phillips, AJK; Magee, M; Howard, ME; Lockley, SW; Rajaratnam, SMW; Sletten, TL Practical alternatives to gold-standard measures of circadian timing in shift workers are needed. We assessed the feasibility of applying a limit-cycle oscillator model of the human circadian pacemaker to estimate circadian phase in 25 nursing and medical staff in a field setting during a transition from day/evening shifts (diurnal schedule) to 3-5 consecutive night shifts (night schedule). Ambulatory measurements of light and activity recorded with wrist actigraphs were used as inputs into the model. Model estimations were compared to urinary 6-sulphatoxymelatonin (aMT6s) acrophase measured on the diurnal schedule and last consecutive night shift. The model predicted aMT6s acrophase with an absolute mean error of 0.69 h on the diurnal schedule (SD = 0.94 h, 80% within ±1 hour), and 0.95 h on the night schedule (SD = 1.24 h, 68% within ±1 hour). The aMT6s phase shift from diurnal to night schedule was predicted to within ±1 hour in 56% of individuals. Our findings indicate the model can be generalized to a shift work setting, although prediction of inter-individual variability in circadian phase shift during night shifts was limited. This study provides the basis for further adaptation and validation of models for predicting circadian phase in rotating shift workers. 2019-07-30T00:00:00Z