Anatomy and Neuroscience - Doctoral Theses

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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.
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    Novel insights into early life stress-induced dysfunction of the gut-brain axis
    (University College Cork, 2022-05-27) Collins, James M.; O'Mahony, Siobhain M.; Cryan, John; Science Foundation Ireland; Mead Johnson Nutrition; GlaxoSmithKline
    Visceral pain, a debilitating hallmark of disorders of gut-brain axis interactions such as irritable bowel syndrome, has a major impact on quality of life. Given the increasing prevalence of irritable bowel syndrome over the past number of years, as well as a lack of effective treatments for disorders of visceral pain, new strategies need to be undertaken to develop successful interventions. The use of both dietary and pharmacological interventions to reduce visceral pain has yielded some promising results, however, these require further investigation. There is also a pressing need to unravel the mechanisms behind the aetiology of these disorders. In this thesis, we focused on prenatal and postnatal stress-induced dysfunction of the gut-brain axis and provide novel insights into the factors that modulate the visceral pain response. Firstly, the potential of CL-316243, a pharmacological intervention, and milk fat globule membrane (MFGM), a dietary intervention, as potential novel strategies to ameliorate visceral hypersensitivity resultant from exposure to stress in the early postnatal period were assessed. Specifically, using Sprague Dawley rats exposed to maternal separation (MS) for 3 hours per day from postnatal day 2-12, a well-established rodent model of early life stress, we administered either CL-316243 via the oral route or MFGM in the diet to assess their efficacy in ameliorating MS-induced visceral hypersensitivity. Here, we report that both interventions were successful in reducing MS-induced visceral hypersensitivity and this occurred independently of changes at the level of central serotonergic signalling and secretomotor activity (CL-316243), or the enteric nervous system and intestinal permeability (MFGM). xiii Next, we investigated the role of female sex hormones and the gut microbiota as modulators of visceral sensitivity using female germ-free mice. Here, we observed that the oestrous cycle modulated the visceral pain response in a microbiota-dependent manner and ovariectomy resulted in visceral hypersensitivity in conventional animals only. We then assessed alterations in the immune profiles of pre-adolescent rats and the consequent impact of MS. Here, we reported modest pre-adolescent changes in the plasma immune profile and spleen weight in male rats, with no changes seen in the gut immune profile at this same timepoint. Finally, we propose the use of several biological markers of systemic inflammation and gastrointestinal permeability as indicators of prenatal maternal stress during the second trimester of healthy pregnancies. The utilisation of these biomarkers could help to negate or prevent the deleterious impacts of early life stress both on foetal development and maternal health. Overall, the results of this thesis provide novel insights into early life stress-induced dysfunction of the gut-brain axis as well as potential therapeutic strategies.
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    Characterisation of the molecular mechanisms of dopaminergic axonal growth and their impairment in Parkinson's disease
    (University College Cork, 2021) Anantha, Jayanth; O'Keeffe, Gerard W.; Sullivan, Aideen M.; Science Foundation Ireland
    Parkinson’s disease (PD) is a neurodegenerative disease that affects 6.1 million people globally as of 2016. Axonal degeneration has been identified as the earliest event in the pathogenesis of the disease. For this reason, the application of neurotrophic growth factors for the protection and/or restoration of dopaminergic neurons and their axons has been the subject of intensive research efforts. Glial derived neurotrophic factor (GDNF) was the first of these growth factors to be tested. Despite an extensive body of pre-clinical evidence, clinical trials of GDNF did not meet their primary end points. Thus, the search for new neurotrophic growth factors that can promote the survival of dopaminergic neurons and restore axonal degeneration has remained an ongoing pursuit. Growth differentiation factor (GDF)5 is one such neurotrophic factor under consideration for PD therapy. Therefore, in this body of work, we evaluate the molecular mediators through which GDF5 promotes neurite growth in cellular models of PD. GDF5 is known to mediate its neurotrophic growth factor effects by regulating the BMP-Smad signalling pathway. Therefore, for GDF5 to be a suitable candidate therapy for PD, it is important that the cellular response to GDF5 is unaffected by the disease process. In Chapter 2, we demonstrate that GDF5 signalling is normal in cellular models of PD. Leucine rich repeat kinase (LRRK)2, both wild type and the PD associated G2019S mutant, had no impact on the BMP-Smad mediated transcriptional response thus, confirming the integrity of this pathway in the LRRK2 models of PD. In contrast, α-synuclein impaired the BMP-Smad transcriptional response in cells overexpressing α-synuclein. α-synuclein impaired both BMPR1B- and Smad1- mediated transcription, however, GDF5 and BMP2 treatment restored α-synuclein-mediated impairments in the BMP-Smad transcriptional response. Histone deacetylase (HDAC)5 also regulates the BMP-Smad pathway, and here we demonstrate that HDAC5 nuclear localization or sequestration is central to the regulation of the BMP-Smad pathway. Finally, we demonstrate that GDNF, unlike GDF5, cannot activate its downstream transcriptional response in the presence of α-synuclein. Taken together, these findings suggest that the GDF5-mediated transcriptional response is unimpaired in cellular models of PD. Despite various studies demonstrating the neurite growth promoting effects of GDF5, in vitro and in vivo, its downstream effectors have remained largely unknown. In Chapter 3, we use a proteomics-based approach to identify the global change in proteome and identify upregulated proteins which play a critical role in mediating the axonal and neurite growth effects of GDF5. We identify two proteins called serine threonine receptor associated protein (STRAP) and nucleoside diphosphate kinase (NME)1 as proteins that play a critical role in GDF5-mediated axonal growth in SH-SY5Y cells. Using siRNA-mediated gene silencing, we demonstrated that the expression of both STRAP and NME1 proteins is crucial for GDF5-mediated neurite growth. Furthermore, we report that the overexpression of STRAP or NME1 was sufficient to promote neurite growth in SH-SY5Y cells. Additionally, we also demonstrate that treatment with recombinant NME1 promoted neurite growth in dopaminergic neurons from the embryonic day (E) 14 rat ventral mesencephalon (VM). Finally, we show that the coexpression pattern of the STRAP and NME1, with midbrain dopaminergic neuronal markers was reduced in the PD brain. Since we establish the role of NME1 in promoting neurite growth in neuronal cells and dopaminergic neurons, we next examine the effects of NME1 in cellular models of PD in Chapter 4. We demonstrate that NME1 protects against 6 hydroxydopamine-induced neurite degeneration in SH-SY5Y cells and E14 rat VM dopaminergic neurons. Additionally, we also demonstrate that NME1 prevented α-synuclein-induced reductions in neurite growth in SH-SY5Y cells. This effect of NME1 was confirmed in cultured primary dopaminergic neurons of E14 VM transduced with AAV-α-synuclein viral vectors. NME1 also rescued neurite growth impairment in cells stably expressing the LRRK2 G2019S mutant. Cumulatively, these findings demonstrated that NME1 treatment can protect against neurite growth impairments in multiple models of PD. We also demonstrated that NME1 exerts its neurite growth-promoting effects through the ROR2 and RORα receptors, and the expression of these receptors is critical for the neurite growth promoting effects of NME1. In the human substantia nigra, we found that NME1 is co-expressed with a distinct set of genes that regulate mitochondrial respiration. Therefore, we finally explore the effects of NME1 on mitochondrial respiration in SH-SY5Y cells. We found that NME1 treatment increases maximal respiration and proton leak in SH-SY5Y cells which confirmed that NME1 regulates mitochondrial respiration and promotes neurite growth. In Chapter 5, we explore the immediate effectors of GDF5 on neuronal cells using a proteomics approach. Our proteomics screen revealed that GDF5 upregulated 14 proteins which had significant coexpression pattern with BMPR2 and multiple dopaminergic neuronal markers. The gene ontology analysis on the proteins upregulated by GDF5 revealed their involvement in oxidative phosphorylation and mitochondrial respiratory processes. We next performed a Seahorse mitochondrial respiration assay to study the effect of GDF5 on mitochondrial function in SH-SY5Y cells. This revealed that GDF5 lowered oxygen consumption rates in SH-SY5Y cells and reduced basal and maximal respiration. Additionally, we demonstrate that in cell lines stably expressing LRRK2 wild type or the G2019S mutant, the expression of BMP-Smad signalling proteins was unaltered. We demonstrated that GDF5 promotes neurite growth in these cells and induces a BMP-Smad dependent transcriptional response. In G2019S-LRRK2 cells, GDF5 lowered basal respiration, maximal respiration, and ATP production, thus lowering bioenergetic demand which is a characteristic of differentiated cells. In summary, the work presented in this thesis identifies new downstream effectors and effects of GDF5. We demonstrate that the GDF5-mediated regulation of BMP-Smad pathway is unimpaired in cellular models of PD. Additionally, the results emanating from this body of work also identify NME1 as a new growth factor with potential neuroprotective and restorative properties. Finally, we demonstrated that GDF5 regulates mitochondrial respiration by promoting differentiation in neuronal cells and that GDF5 rescues neurite growth impairments in cells stably expressing G2019S-LRRK2. Therefore, we conclude that GDF5 and NME1 are, and remain, candidate neurotrophic factors for neuroprotection in PD.