Anatomy and Neuroscience - Doctoral Theses

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    Defining the potential of ZNHIT1, an SNCA co-expressed gene in the substantia nigra, as a therapeutic target for Parkinson’s disease
    (University College Cork, 2022) McCarthy, Erin; O'Keeffe, Gerard W.; Sullivan, Aideen M.; Collins, Louise; Irish Research Council
    Parkinson’s Disease (PD) is synucleinopathy that is characterised by the formation of toxic α-Synuclein (αSyn)-containing Lewy Bodies (LBs) in the midbrain leading to the progressing death of dopaminergic (DAergic) neurons in the substantia nigra (SN). Toxic aggregation of αSyn results in the dysfunction of important neuronal processes, leading to increased neurotoxicity and neurodegeneration. SNCA and its mutant variants have been linked to several cases of familial PD. Given the lack of effective disease-modifying therapies, there is an increasing focus on examining SNCA-induced changes in epigenetic regulation in the hopes of identifying novel targets for gene therapy in PD. In the first experimental chapter, we used gene co-expression analysis to identify Synuclein Alpha (SNCA) co-expressed genes in the SN, whose co-expression pattern was lost in PD. We identified nuclear zinc finger HIT-type containing 1 (ZNHIT1) as an important interacting partner of SNCA in the SN, and that this co-expression pattern is lost in PD indicating functional dysregulation.. We went on to investigate the functional role of ZNHIT1, which revealed that overexpression of ZNHIT1 promotes neurite growth and prevents αSyn-induced reductions in neurite growth and cell viability in SH-SY5Y cells. Analysis of ZNHIT1 co-expressed genes in the SN revealed a significant enrichment of genes associated with the regulation of mitochondrial function. Bioenergetic state analysis agreed with these findings and revealed that ZNHIT1 overexpression increases ATP synthesis, and rescues αSyn-induced impairments in oxygen consumption rate (OCR), basal respiration, maximal respiration, and spare respiratory capacity. These findings reveal that ZNHIT1 can protect against αSyn-induced neurotoxicity and mitochondrial dysfunction in ZNHIT1-overexpressing cells, this rationalising further investigation into ZNHIT1 as a potential therapeutic target for PD. In the second experimental chapter, we investigated the role of ZNHIT1 in αSyn-induced neurotoxicity and mitochondrial dysfunction in PD. PD is characterised by impairments in mitochondrial function and reductions in ATP levels. ZNHIT1 overexpression protects against αSyn-induced deficits in mitochondrial function through an upregulation of genes associated with mitochondrial function. Proteomic and bioinformatic analysis revealed that ZNHIT1 interacts with mitochondrial proteins that are significantly enriched in functional categories important for mitochondrial function such as mitochondrial transport, ATP synthesis, and ATP-dependent activity. We also found that ZNHIT1 upregulates and is co-expressed with hub protein HSP90B1, which is known to deter PD progression, thus indicating a neuroprotective role for ZNHIT1-HSP90BI in the SN. Indeed, we show that ZNHIT1 is also co-expressed with DAergic markers TH and ALDH1A1 in control samples, but that this correlation is lost in PD samples. These results indicate functional dysregulation of ZNHIT1 in PD that may result in the misregulation of its mitochondrial interacting proteins in the cytosol, leading to mitochondrial dysfunction and reductions in ATP synthesis that is characteristic of PD, and thus validates our previous findings that highlight ZNHIT1 as a potential target for PD therapy In the third experimental chapter, we investigated the role of ZNHIT1 in BMP-Smad-dependent transcriptional activation in SH-SY5Y cells overexpressing ZNHIT1. Our analysis revealed that ZNHIT1 activates the BMP-Smad pathway, which has been shown to promote DAergic neurite growth and survival and protects them against αSyn-induced neurotoxicity. However, SNCA overexpression was found to inhibit these ZNHIT1-induced increases in BMP-Smad activation. Further investigation revealed that the neuroprotective effects of ZNHIT1 against αSyn-induced cellular and mitochondrial dysfunction, were inhibited by the BMP receptor (BMPR) inhibitors, Dorsomorphin and K02288, indicating that the neuroprotective effects of ZNHIT1 may be dependent on BMP-Smad signalling. We also show that SMAD4 expression in SH-SY5Y cells overexpressing dominant negative SMAD4 was rescued by ZNHIT1 overexpressing. These results support the hypothesis that αSyn in PD inhibits BMP-Smad signalling, which could lead to the inhibition of the growth promoting effects of ZNHIT1, which appear to be mediated by BMP-Smad signalling. Together, these results further highlight the potential role of ZNHIT1 as a therapeutic target for PD. In the fourth and final experimental chapter, we sought to examine the gene expression changes associated with important signalling networks induced by αSyn overexpression in an in vivo AAV-αSyn rat model of PD, in order to further our understanding of the role of αSyn in PD pathology. Analysis of gene expression changes of 84 genes known to be associated with PD pathology revealed significant reductions in the expression of genes associated with DA synthesis. Further analysis of gene expression changes in the SN induced by αSyn revealed 2,305 differentially expressed genes. The top ranked gene to be overexpressed in this list was Skor1, a known inhibitor of BMP-Smad signalling. Further investigation revealed a significant reduction in constitutively active BMPR1B-stimulated luciferase activity in HEK293T and SH-SY5Y cells overexpressing αSyn. Gene set enrichment analysis (GSEA) revealed that the overexpression of αSyn causes disruptions to cytoskeletal organisation, DNA repair networks and ATP binding, while analysis of cellular bioenergetic states showed reduced ATP synthesis, oxygen consumption rates and basal rates of respiration. This study highlights the role of αSyn as a regulator of mitochondrial function, ATP synthesis and BMP-Smad signalling. Collectively, the data presented in this thesis rationalises the future development of strategies focused on ZNHIT1 overexpression as a potential neuroprotective strategy for PD. In this thesis, we show that overexpression of ZNHIT1 is neuroprotective against αSyn-induced neurotoxicity in PD, including reductions in cell viability and growth, as well as mitochondrial dysfunction, ATP synthesis and BMP-Smad signalling. We also show a functional dysregulation of ZNHIT1-SNCA in PD, suggesting that altered expression patterns of ZNHIT1 may play an important role in PD progression. We hypothesis that overexpression of ZNHIT1 in the SN of PD patients may result in neuroprotection against the progression of PD. Our results highlight a potential role for ZNHIT1 in cellular dysfunction that may underlie PD pathology. Collectively the data in this thesis rationalises the future development of strategies focused on ZNHIT1 overexpression as a potential neuroprotective strategy for PD.
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    Unravelling the role of tryptophan metabolism in the microbiota-gut-brain axis: focus on the effects of stress and exercise
    (University College Cork, 2023) Gheorghe, Cassandra Elise; Clarke, Gerard; Cryan, John; Dinan, Timothy G.; FEANS; Léandre Pourcelot
    The involvement of the microbiota-gut-brain axis communication in brain function and behaviour has enabled a paradigm shift in neuroscience, neurogastroenterology and psychiatric research. Two key routes of this bidirectional communication are impaired in stress-related disorders: the hypothalamic-pituitary-adrenal axis and tryptophan metabolism. This crosstalk has been widely characterised following chronic stress, in stress-related disorders and gastrointestinal disorders comorbid with psychological distress. The acute transitory reaction to an acute stressor and the associated adaptation over time, however, is not well understood. In this work, we investigated this neglected topic by evaluating the response to a psychological and a physical s stress exposure in terms host and microbial tryptophan metabolism and other pillars of the microbiota-gut-brain axis. For this purpose, we used well-recognised pre-clinical models (germ-free and antibiotic-treated mice) to decipher the role of gut microbes in the regulation of tryptophan metabolism and availability at baseline and following hypothalamic-pituitary-adrenal axis activation. We found that a 15-min acute stressor was sufficient to upregulate colonic 5-HT concentrations in conventional and re-colonised male mice while serotonin level was unaltered in GF male mice. We have identified novel region- and sex- dependent effects of the microbiota in modulating the expression of host rate-limiting enzyme of tryptophan metabolism following acute stress at both terminals of the gut-brain axis communication network (Chapter 2). We found that acute stress induced functional alterations visible as increased intestinal permeability in the ileum (ex vivo and in vitro) depending on time of day. In the colon, alterations in the rate-limiting enzyme of tryptophan breakdown towards serotonin synthesis, TPH-1, was reduced by stress in conventional mice but increased in antibiotic treated mice following stress (Chapter 3). Furthermore, both microbial and host tryptophan metabolism were altered in the caecum of conventional mice following an acute stressor, which was disturbed in germ-free mice and partially restored following colonisation of germ-free animals (Chapter 3). Then, we assessed in sedentary humans transient and adaptive changes to exercise along the microbiota-gut-brain axis at different exercise intensities. We discovered intensity-dependent effects on various pillars of the microbiota-gut-brain axis. While high-intensity training increased the cortisol awakening response chronically, it also led to an increase in peripheral serotonin level directly following a maximal exhaustion test. Light-intensity exercise lead to marked compositional alterations at the species level. Lastly, our work provides the first piece of evidence that the relative abundance of exercise-responsive bacteria with saccharolytic potential decreases with exercise intensity. Together, these results contribute to a deeper comprehension of the host-microbe dialogue driving the spatial and temporal dynamics of the physiological response and adaptation to various types of stress exposure. These observations open future research avenues and encourages a move towards the integrated physiological perspectives essential for the development of precision treatment options in stress-related disorders.
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    Machine learning techniques for identifying early-life biomarkers in perinatal & child health
    (University College Cork, 2022) O'Boyle, Daragh; English, Jane A.; Murray, Deirdre M.; Mooney, Catherine; Lopez, Lorna; Science Foundation Ireland; Health Research Board
    Artificial Intelligence (AI), and more specifically machine learning (ML), has been used in the investigation of biomarkers for many clinical conditions, reducing the need for specialist diagnosis, reducing waiting times and increasing access to reliable diagnostics. There are numerous areas yet to benefit from its application, particularly in the fields of perinatal and paediatric research. Two such brain related conditions, which will be the focus of this thesis are Hypoxic Ischemic Encephalopathy (HIE) and Autism Spectrum Disorder (ASD). HIE is a major cause of neurological disability globally and results from a lack of oxygen to the brain during and immediately after birth. The mainstay of treatment is therapeutic hypothermia, which, to be effective it must be applied within 6 hours of birth. This thesis aims to improve the early identification of infants eligible for therapeutic hypothermia using AI with clinical and metabolomic HIE biomarkers. Autism constitutes a group of neurodevelopmental disorders characterized by behavioural and cognitive symptoms. The underlying aetiology behind autism remains unclear and reliable predictive biomarkers are lacking. Intervention from an early age has been shown to reduce symptoms but diagnosis frequently occurs outside the window for effective treatment. Despite proven benefits in patient outcomes with intervention within the first two years of life, diagnosis often doesn’t occur until an individual is 3 or 4 years old, and in many cases much older. There is a pressing need for new methods to identify neonates most at risk to provide adequate treatment which improves long term outcomes. In the first experimental section, chapters 2-5, we have applied ML to first identity the optimum predictive clinical and metabolomic biomarkers for the identification of those with HIE. We initially assessed clinical variables using ML feature ranking and modelling. This study identified markers of a newborn’s condition at birth; Apgar scores, need for resuscitation, first measurements of pH and base deficit, as the most predictive. These models achieved an area under the receiver operator characteristic curve (AUROC) of 0.89 when distinguishing between those with perinatal asphyxia, who do not require treatment, and those with HIE. Furthermore, we then assessed a panel of promising metabolite markers for their predictive capabilities with and without clinical markers. ML identified metabolites alanine and lactate as the most predictive of HIE development and when combined with Apgar scores a measure of a newborn’s condition, at 1 and 5 minutes after birth, achieved a predictive AUROC of 0.96. These studies have successfully identified alanine as a candidate metabolite for cotside HIE risk assessment as well as displaying that ML models can improve on our current ability to identify those in need of therapeutic hypothermia. To validate these findings two studies were undertaken. The first independently compared alanine levels in cord blood of those with HIE and controls. This study has displayed elevated levels of alanine at birth and up to 6 hours after, successfully validating alanine as an early life marker for those with HIE in need of neuroprotective therapy. The second validation study successfully validated our algorithm for the prediction of HIE in a large diverse cohort comprised of infants with a range of differing conditions, compared to a set of HIE cases and controls previously assessed. Here 243 infants were assessed, using our model, to determine risk of HIE and an accuracy of 85% was maintained. This study has successfully validated our model's ability to retain performance when applied to a diverse, real-world cohort. In this section, we have successfully displayed the use of ML for improving HIE diagnostics and validated these findings. Further, larger validation studies are currently underway with the end goal of clinical use for determination of those in need of treatment for HIE. In the second experimental section, chapters 6-8, we aimed to apply ML methods to identify early life biomarkers for autism spectrum disorder. We first conducted a systematic review of all blood-based autism biomarkers. This study successfully catalogued all reported biomarkers and recorded the direction of change of theses markers in those with autism compared to neurotypical controls. This study also applied Genome Wide Association Studies (GWAS) and pathway analysis to test for biological processes which may be implicated at the level of the genome in autism. In chapter 7, ML analysis was applied to metabolomics data from cord blood samples from the Cork BASELINE birth cohort. Discovery and targeted metabolomics were completed on this data. In chapter 8, we applied ML to assessed clinical predictors for autism in the Danish National Birth Cohort, which included data from 500 autism cases and matched controls. We identified markers of maternal health and wellbeing as being important for autism prediction and achieved a prediction accuracy of 0.68 AUROC. Overall, this thesis successfully addressed its’ aims to apply ML methods for the identification of biomarkers and development of prediction models for HIE and autism. We have validated previously identified biomarkers and identified novel clinical and blood-based markers as well as created robust HIE predication models with the ability to improve clinical decision making. Possible future steps this research can follow to further add to the field are outlined within. Overall, this research has added to the growing body of evidence displaying the ability of ML to offer improvements to healthcare and specifically to perinatal and child health. Artificial Intelligence (AI), and more specifically machine learning (ML), has been used in the investigation of biomarkers for many clinical conditions, reducing the need for specialist diagnosis, reducing waiting times and increasing access to reliable diagnostics. There are numerous areas yet to benefit from its application, particularly in the fields of perinatal and paediatric research. Two such brain related conditions, which will be the focus of this thesis are Hypoxic Ischemic Encephalopathy (HIE) and Autism Spectrum Disorder (ASD). HIE is a major cause of neurological disability globally and results from a lack of oxygen to the brain during and immediately after birth. The mainstay of treatment is therapeutic hypothermia, which, to be effective it must be applied within 6 hours of birth. This thesis aims to improve the early identification of infants eligible for therapeutic hypothermia using AI with clinical and metabolomic HIE biomarkers. Autism constitutes a group of neurodevelopmental disorders characterized by behavioural and cognitive symptoms. The underlying aetiology behind autism remains unclear and reliable predictive biomarkers are lacking. Intervention from an early age has been shown to reduce symptoms but diagnosis frequently occurs outside the window for effective treatment. Despite proven benefits in patient outcomes with intervention within the first two years of life, diagnosis often doesn’t occur until an individual is 3 or 4 years old, and in many cases much older. There is a pressing need for new methods to identify neonates most at risk to provide adequate treatment which improves long term outcomes. In the first experimental section, chapters 2-5, we have applied ML to first identity the optimum predictive clinical and metabolomic biomarkers for the identification of those with HIE. We initially assessed clinical variables using ML feature ranking and modelling. This study identified markers of a newborn’s condition at birth; Apgar scores, need for resuscitation, first measurements of pH and base deficit, as the most predictive. These models achieved an area under the receiver operator characteristic curve (AUROC) of 0.89 when distinguishing between those with perinatal asphyxia, who do not require treatment, and those with HIE. Furthermore, we then assessed a panel of promising metabolite markers for their predictive capabilities with and without clinical markers. ML identified metabolites alanine and lactate as the most predictive of HIE development and when combined with Apgar scores a measure of a newborn’s condition, at 1 and 5 minutes after birth, achieved a predictive AUROC of 0.96. These studies have successfully identified alanine as a candidate metabolite for cotside HIE risk assessment as well as displaying that ML models can improve on our current ability to identify those in need of therapeutic hypothermia. To validate these findings two studies were undertaken. The first independently compared alanine levels in cord blood of those with HIE and controls. This study has displayed elevated levels of alanine at birth and up to 6 hours after, successfully validating alanine as an early life marker for those with HIE in need of neuroprotective therapy. The second validation study successfully validated our algorithm for the prediction of HIE in a large diverse cohort comprised of infants with a range of differing conditions, compared to a set of HIE cases and controls previously assessed. Here 243 infants were assessed, using our model, to determine risk of HIE and an accuracy of 85% was maintained. This study has successfully validated our model's ability to retain performance when applied to a diverse, real-world cohort. In this section, we have successfully displayed the use of ML for improving HIE diagnostics and validated these findings. Further, larger validation studies are currently underway with the end goal of clinical use for determination of those in need of treatment for HIE. In the second experimental section, chapters 6-8, we aimed to apply ML methods to identify early life biomarkers for autism spectrum disorder. We first conducted a systematic review of all blood-based autism biomarkers. This study successfully catalogued all reported biomarkers and recorded the direction of change of theses markers in those with autism compared to neurotypical controls. This study also applied Genome Wide Association Studies (GWAS) and pathway analysis to test for biological processes which may be implicated at the level of the genome in autism. In chapter 7, ML analysis was applied to metabolomics data from cord blood samples from the Cork BASELINE birth cohort. Discovery and targeted metabolomics were completed on this data. In chapter 8, we applied ML to assessed clinical predictors for autism in the Danish National Birth Cohort, which included data from 500 autism cases and matched controls. We identified markers of maternal health and wellbeing as being important for autism prediction and achieved a prediction accuracy of 0.68 AUROC. Overall, this thesis successfully addressed its’ aims to apply ML methods for the identification of biomarkers and development of prediction models for HIE and autism. We have validated previously identified biomarkers and identified novel clinical and blood-based markers as well as created robust HIE predication models with the ability to improve clinical decision making. Possible future steps this research can follow to further add to the field are outlined within. Overall, this research has added to the growing body of evidence displaying the ability of ML to offer improvements to healthcare and specifically to perinatal and child health.
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    Microbiota-gut-immune-brain communication across the lifespan
    (University College Cork, 2022-12-21) Cruz-Pereira, Joana S.; Cryan, John; Clarke, Gerard; Science Foundation Ireland
    While the exploration of the gut microbiome in health and disease evolves, the implications of the microorganisms that inhabit the gut for host brain health also multiply. As we grow up and grow old, the gut microbiota along with the host physiological systems, undergoes significant remodelling. The influences of the gut microbiome on host physiology are relevant across the lifespan, with continuous communication between the gut microbiome and the central nervous system (CNS) representing an important aspect of this host-microbe dialogue. A growing body of research into dissecting the involvement of the gut microbiota in the behaviour and functioning of the CNS raises the need to understand how these integrated systems communicate throughout the lifespan of the host, and how it is phenotypically reflected. Understanding the gut-brain axis across the lifespan is imperative, as these insights can be used to further support healthy brain aging, along with the development of better biomarkers towards the development of personalized therapeutic strategies. In this thesis, we aimed to investigate the influence of the gut microbiome in immune and behavioural features throughout the lifespan of the host: in early-life and aging. To this end, we assessed the effect of microbiota depletion in aged mice and demonstrated for the first time that the gut microbiome is associated with social behaviour and restricts the accumulation of T-helper cells in the choroid plexus in aged mice. This was accompanied by modulation of caecal metabolite levels, and in particular, some metabolites previously associated with age-dependent processes, namely argininosuccinic acid and N-formylmethionine. To further examine the involvement of the gut microbiome in aging, we explored whether supplementation with the prebiotic FOS-Inulin could alter behavioural and physiological aspects along the gut-brain axis in stressed aged mice. We demonstrated that FOS-Inulin supplementation can ameliorate the disrupted social behavioural responses that arise following a stress exposure, including the alterations in social interaction with a non-aggressive mouse and social novelty, while promoting the remodelling of caecal and prefrontal cortex metabolite levels. More specifically, dietary supplementation with FOS-Inulin promotes the amelioration of the levels of 4-Hydroxybenzaldehyde and spermine in the prefrontal cortex of stressed aged mice. Additionally, we evaluated if FOS-Inulin supplementation could alter adult social, depressive- and anxiety-like behavioural and immune markers in offspring exposed to early life microbial disruption. In our study, we observed altered intestinal immune markers and subtle behavioural changes following this intervention. Taken together, these results provide novel insights on time sensitive critical windows for the gut microbiome, and its impact in behaviour and immunity outcomes in the host. While further investigation into the mechanisms underlying these effects is crucial, these findings highlight the involvement of gut microbial signalling on host behaviour and immunity. This research paves the way for the future development of therapeutic options that target the gut microbiome to modify these age-dependent behavioural and metabolite alterations.
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    Universal design for learning and anatomy healthcare education
    (University College Cork, 2022-10-06) Dempsey, Audrey M. K.; Nolan, Yvonne M.; Hunt, Eithne; Lone, Mutahira
    Inclusive learning environments and educational experiences for all individuals have been identified as priorities in recent educational policies in the Republic of Ireland (ROI) and Northern Ireland (NI). Driven by these policy mandates, curricula across all disciplines, including anatomy education, are undergoing reform to ensure inclusive learning experiences are afforded to all individuals. Anatomy is a critical component of healthcare curricula. Robust knowledge of anatomy ensures the safe and effective practice of healthcare professionals. Motivation and engagement have been found to play an integral role in successful student learning. However, there are reports of a lack of both motivation and engagement among some healthcare students studying anatomy. Hence there is need to incorporate a specific pedagogical framework in anatomy curricula to enhance motivation and engagement among healthcare students and in turn promote an inclusive learning experience. The aim of this thesis was to determine whether Universal Design for Learning (UDL) would be an appropriate pedagogical framework in the design and delivery of anatomy curricula to enhance healthcare students’ motivation and engagement. Firstly, a scoping review, which is a method of mapping emerging literature, was carried out which established that UDL has not been utilised in anatomy curricula of third level healthcare programmes, specifically medicine, dentistry, occupational therapy or speech and language therapy (Chapter Two). While there are published studies incorporating teaching strategies which align with UDL, and have in turn reported benefits to student learning, none of these studies specifically mention the UDL framework. Motivation levels of first year undergraduate healthcare students in University College Cork (UCC) at the start and end of their anatomy modules were established (Chapter 3) using the Motivated Strategies for Learning Questionnaire (MSLQ), and a change in motivation over the duration of the module was identified. First year healthcare students in UCC and anatomy educators based in the ROI and United Kingdom (UK) were surveyed (Chapter Four and Chapter Five, respectively). The first year healthcare students were informed about UDL as they neared the end of an anatomy module. After informed consent was obtained a paper questionnaire was distributed to potential participants. An online questionnaire was distributed to anatomy educators using the online platform Microsoft Forms and was available for 12 weeks. Both studies highlighted that very few anatomy students or educators were aware of UDL. However, the majority of the participants in both studies acknowledged the potential of the UDL framework to enhance the design and delivery of anatomy curricula. The results of this thesis show that the incorporation of UDL into the design and delivery of third level anatomy curricula could potentially enhance student motivation, engagement and their overall educational experience. More specifically, the results from the scoping review (Chapter Two) indicated that teaching methods which align with UDL enhance anatomy students’ academic performance, motivation and confidence. The results described in Chapter Three highlight the range in motivation levels among anatomy students enrolled in various healthcare programmes both at the start of their respective anatomy modules and at the end. The majority of first year anatomy healthcare students thought that UDL benefits student learning (99%, n=186) and that the implementation of UDL increases student engagement (97%, n=183) (Chapter Four). Finally, the results described in Chapter Five revealed that anatomy educators have a mixed opinion of UDL as some participants were concerned about the time commitment required to implement UDL in anatomy curricula design. Nevertheless, the potential benefit of the utilisation of ULD was acknowledged by the majority of the participants. In summary, students vary in their levels of motivation to study anatomy and the manner in which they prefer to engage with learning material, activities and assessments. The utilisation of the UDL pedagogical framework in anatomy curricula can accommodate learner variability and in turn afford all students the opportunity to reach their individual potential while enhancing and promoting an inclusive third level educational experience.