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Real-time edge processing of neural signals with memristive technologies
No full textIntracranial brain-computer interfaces, capable of real-time neural activity decoding, present a revolutionary opportunity to improve the quality of life of individuals with dysfunction or damage to the nervous system. Despite recent advancements in neural recording, neuroprosthetic technologies still face bottlenecks in data processing and transmission. Effective neuro-prosthetic devices must deliver enhanced performance metrics including high accuracy, low-power, small size, and minimal latency to enable continuous and real-time brain interfacing. Memristive technologies are promising candidates, acting as bioelectronic links that integrate biosensing with computation for brain-inspired architectures, and operating at low power levels.
Memristive devices are two-terminal electronic components that reversibly and gradually adjust conductance in response to electrical stimuli, with their memory state depending on the thresholded integral of the input voltage. Acting as integrating sensors, they suppress noise and encode signal amplitude and frequency within their resistive state when biased with suitable preamplified neuronal signals. This behaviour similar to biological synapses offers a novel solution for processing strategies in brain-computer interfaces. A memristor-based platform for detecting action potentials (APs) -- the fundamental units of communication between neurons and a well-established indicator of brain activity -- has already demonstrated promising results in the literature.
This doctoral research proposes a memristor-based processing platform for real-time decoding of neural signals, with a focus on population-level activity rather than single-neuron action potential (AP) detection. In many clinical or assistive applications, such as state monitoring or rehabilitation relying on fine-grained single-neuron activity is not necessary. Instead, larger scale population-level dynamics provide more robust and stable biomarkers. Moreover, relying on these signals offers energy efficiency benefits due to their reduced bandwidth requirements.
Specifically, local field potentials (LFPs) were used, as they provide greater spatial coverage and temporal stability by capturing collective synaptic activity. LFPs recorded in vivo from the ventral tegmental area of awake rats performing associative memory tasks were applied to TiOx-based non-volatile memristors, significantly reducing processing power. The system achieved real-time biomarker detection with over 98% accuracy and power consumption as low as 4.14 nW per channel—up to 100× lower than comparable state-of-the-art methods, at similar accuracy levels.
This memristor-based protocol was then extended to process the envelope of multi-unit activity (eMUA), a more recently explored neural signal that also reflects population dynamics but enables earlier biomarker detection and reduced inter-channel correlation—key for real-time prosthetic control. With over 95% detection accuracy and ~9 nW power consumption, the approach was validated across different metal-oxide memristor stacks, confirming the platform-agnostic applicability of the MIS method. The integration of MIS with ultra-low-power front-end analogue circuitry showed a 30× reduction in power demand compared to majority of state of the art front-end chips, achieving sub-μW consumption and projecting up to 10× improvement over the most advanced implementations.
LFPs emerged as the most power-efficient and reliable neural source in the presented experiments, while eMUA provided a lower-latency alternative better suited to multi-channel applications. As the number of recording channels increases and monolithic integration with CMOS is optimised, this memristor-based strategy is expected to further reduce power consumption per channel while enabling the detection of increasingly complex behavioural states.
As a final experiment, given the continued prevalence of action potentials in neural signal processing, a strategy was developed to detect not amplitude-based but frequency-encoded biomarkers from action potential activity. Temporal compression was applied to reduce spiked quantity, while preserving the information needed to distinguish between high- and low-activity brain states—patterns often linked to neurological conditions such as Alzheimer’s disease or stroke. This compression was implemented using volatile metal-oxide memristors, whose intrinsic temporal filtering proved beneficial in identifying regions of high-frequency spiking activity before passing the data to a spiking neural network (SNN) for classification. Once again, neural activity was processed more efficiently and benchmarked against detection accuracy in a clinically relevant in vivo application using anaesthetised rats. These biomarkers were reliably detected using only 10% of the original data, while maintaining an SNN detection accuracy of approximately 97.5%.
Overall, this research lays the groundwork for scalable, ultra-low-power systems for chronic neural monitoring and implantable neuro-prosthetic technologies
Regression models for extreme values with random function covariates
No full textA fundamental principle of statistics of extremes is that any realistic quantification of risk requires extrapolating into a distribution’s tail—often beyond the observed extremes in a dataset.
Yet, as modern technology advances, an increasing amount of data is recorded continuously or
intermittently, and hence the question arises: how to take advantage of such data in an extreme
value framework? Motivated by this question, this thesis develops a class of novel statistical
methods that can be used for marginal and joint distributions to learn how the extreme values
may change according to a functional covariate. The first contribution consists of a functional
regression model for the tail index that can be used for assessing how the magnitude of the
extremes can change according to a random function. Another contribution of this thesis is the
development of a nonparametric regression model that can be regarded as a functional covariate
regression method, designed for situations where there is a need to assess how the extremal
dependence of a random vector can change according to a functional explanatory variable. Such
development is based on modeling a family of angular measures indexed by a random function.
The performance of the proposed methodologies is assessed via numerical studies, and financial
data is used to illustrate their application
Vibrational spectroscopy with machine learning for accurate cancer detection
No full textCancer remains a global health crisis, significantly impacting individuals and societies
worldwide. In 2020, approximately 19.3 million new cancer cases and 10 million
cancer-related deaths were reported globally. Screening and triaging are crucial in the
early detection, diagnosis, and management of cancer, targeting different stages to
improve patient outcomes. Despite being one of the leading causes of mortality, many
cancers lack effective screening methods. While conventional screening techniques are
available for some cancers, they have varying accuracy and limitations. Identifying
cancer or precancerous conditions early can significantly reduce mortality and enhance
treatment outcomes. The analysis of biofluids to detect cancer-related signals—liquid
biopsy, has garnered considerable attention over the past decade. Although promising,
many current liquid biopsies lack the sensitivity needed for early-stage cancer detection.
Raman spectroscopy (RS) is a non-destructive, real-time technique for molecular
analysis. Our study investigated the impact of optimising selected parameters and
assessed various spectral processing methods on the reliability and accuracy of spectral
analyses, and demonstrated that manual extension of the sampled volume significantly
enhanced the detection of low-concentration cancer biomolecules, improving spectral
resolution in half the measurement time compared to conventional settings.
Additionally, we examined chemical changes associated with acquired radioresistance
in HR+ and HR− breast cancer cell lines. Combining RS with machine learning, we
achieved high accuracy in distinguishing between parental cell lines and their
radioresistant phenotypes, regardless of hormonal status. The radioresistant phenotypes
exhibited similar difference spectra and formed a single cluster, suggesting common
biochemical changes during the acquisition of radioresistance. We also integrated RS
with advanced machine learning techniques for accurate cancer detection in blood
plasma, using both liquid and dried samples. Our results showed high sensitivity and
specificity in classifying stage Ia breast cancer, with an Area Under the Curve (AUC)
of 1.00. Hierarchical clustering validated the reproducibility of our results. This
research highlights the potential of combining vibrational spectroscopy with AI for
cost-effective, non-invasive, and personalised early cancer detection, emphasising the
need for standardised protocols and robust data processing techniques to facilitate
clinical translation in liquid biopsy applications
Using pangenomic approaches to analyse and refine meiotic recombination breakpoints in African trypanosomes
Get PDFTrypanosoma brucei is an African protozoan parasite which causes severe health and economic burden across sub-Saharan Africa, causing both Human and Animal African trypanosomiasis. Trypanosomes have previously been shown to undergo meiotic recombination in the salivary gland of the tsetse fly, which has the potential to influence genetic inheritance in hybrid progeny. Previously, MacLeod et al (2005), published a study that characterised hybrid progeny from genetic crosses between two parental strains of T. b. brucei – STIB 247 x TREU 927, following sexual recombination. They identified meiotic recombination events in the hybrid progeny, to the level of resolution possible with mini- and microsatellites. The work presented in this thesis builds upon the work of MacLeod et al., with the aim of understanding how meiotic recombination between two parental strains of T. b. brucei – TREU 927 Cl1 x STIB 247 Cl2 influences genotypic inheritance in twelve hybrid progeny, and aims to improve the resolution of the meiotic recombination events which were previously identified. This was achieved by generating multiple sequencing datasets for both the parental trypanosomes (Oxford Nanopore Technology [ONT] ultra-long reads, Pacific Bioscience [PacBio] HiFi reads, and Hi-C reads) and the progeny trypanosomes (ONT long reads, and Illumina whole genome sequencing (WGS) short reads), details of which are described in Chapter 2. Chapter 3 focuses on the genome assembly of both the parental and progeny genomes. As all downstream analysis was referenced against the parental trypanosomes – these assemblies had to be highly accurate, complete, contiguous assemblies. Both parental genomes were successfully assembled and haplotype-resolved, with contig N50s of 2.83 Mb and 3.57 Mb, consistent with most chromosomes being largely captured by individual contigs. In the final chapter, Chapter 4, a pangenome was generated comprising the genomes of the reference and parental genomes. This pangenome was used to map Illumina WGS reads from the progeny, to discern which parental haplotypes had been inherited by each of the progeny. 93 inferred recombination events were congruent with those identified in the previous microsatellite study, but mapped to a higher resolution, with a further 18 novel events identified. Furthermore, the frequency of meiotic recombination events was investigated, with the most events per Mb occurring on chromosome 4 (0.69 per Mb), and the fewest on chromosome 1 (0.09 per Mb). In total, all except for two meiotic recombination events were able to be refined, which substantially improved the resolution of the previous genetic map. This PhD project elucidates how genetic inheritance is affected by meiotic recombination in T. b. brucei, and the frequency at which sexual recombination occurs in hybrid progeny – e.g., which chromosomes experience chromosomal recombination ‘hotspots’ and ‘coldspots’. The outcomes of this PhD project could aid understanding of how meiotic recombination influences haplotypic content in hybrid progeny, and the implications this has in the field where both human-infective and non-human-infective trypanosomes circulate in the same geographic area. Additionally, further work could build upon this project to ascertain how the very large gene family (~2,000 genes) that is responsible for antigenic variation (the variant surface glycoprotein) segregates within the hybrid progeny, which could lead to a better understanding of how virulence genes are propagated in trypanosome populations through sexual recombination
Regulation of human brown adipose tissue activity
Get PDFThe rising prevalence of obesity globally poses significant morbidity and mortality, as obesity
increases the risk of developing a number of diseases such as diabetes, hypertension and
cardiovascular disease. While some more recent interventions to treat obesity have been
efficacious, they can cause undesirable side effects, highlighting a need for novel treatment
options. Adipose tissue comprises brown (BAT) and white adipose tissue (WAT). WAT mainly
stores energy, while BAT increases energy expenditure primarily through cold-induced
thermogenesis, which is mediated through a specialised thermogenic protein, called
uncoupling protein 1 (UCP1). Due to its role in thermogenesis, BAT has gained significant
interest as a target to treat obesity and associated cardiometabolic disease. 2-deoxy-2-
[¹⁸F]fluoro-D-glucose (¹⁸F-FDG) positron emission tomography (PET) is the most commonly
used technique to quantify human BAT mass and activity, exploiting the substantial glucose
uptake by BAT as a surrogate marker for BAT thermogenesis. The prevalence of detectable
BAT at room temperature using ¹⁸F-FDG PET is reduced in individuals with increased
cardiovascular risk such as obesity, diabetes and hypertension. However, ¹⁸F-FDG uptake by
BAT may be confounded by obesity-induced insulin resistance so it remains unclear whether
BAT thermogenesis is decreased in obesity and cardiometabolic disease. Our understanding
of the regulation of human BAT activation also remains limited. Using transcriptomics, we
recently identified serotonin as a potential regulator of human BAT, by demonstrating that
the gene SLC6A4, which encodes the serotonin reuptake transporter (SERT), was one of the
most differentially expressed genes in human brown compared with white adipocytes. SERT
inhibition, which increases serotonin receptor activation, decreased human brown adipocyte
thermogenesis. However, it is unknown whether serotonin levels are altered in obesity or if
reduction in peripheral serotonin activity can lead to a therapeutic benefit.
We hypothesised that (1) UCP1 expression in human BAT, a marker of BAT thermogenic
capacity, is inversely associated with cardiovascular risk factors, (2) circulating and adipose
tissue serotonin levels are increased in obesity and (3) inhibition of peripheral serotonin
synthesis stimulates human BAT activity.
To assess hypothesis 1, we measured UCP1 mRNA expression in whole adipose tissue (n=53)
and in differentiated pre-adipocytes (n=85) from paired BAT and WAT biopsy samples
obtained from patients undergoing elective neck surgery. UCP1 expression in BAT (but not in
WAT or in differentiated brown or white pre-adipocytes) was inversely associated with
obesity, ageing, insulin resistance and hypertension. Age was the only independent predictor
of high UCP1 expression in BAT and obesity reduced the frequency of high UCP1 expression
only in individuals >40 years. To explore the effect of obesity in young adults in vivo, ¹⁸F-FDG
PET-MR scans were performed in young obese and age-matched normal weight individuals
(n=6 in each weight group, mean age ~22 years) following 2 hours of mild cold exposure.
Consistent with the UCP1 data, young individuals with obesity had preserved ¹⁸F-FDG uptake
by BAT, despite increased insulin resistance.
To determine if obesity alters peripheral serotonin levels, we measured circulating and
abdominal adipose tissue serotonin concentrations in healthy age-matched individuals with
normal body weight and obesity (n=10 in each weight group) during warm and cold exposure.
The majority of circulating serotonin is inactive due to being platelet-bound with only a small
proportion circulating freely. Platelet serotonin concentrations increased during cold
exposure in normal weight individuals. However, in obese volunteers, inactive platelet
serotonin levels increased during warm conditions, which dropped during cold exposure,
accompanied by an increase in adipose tissue serotonin levels. These variations in serotonin
concentrations suggest a potential dysregulation in cold-induced serotonin response in
obesity.
To assess whether inhibition of peripheral serotonin synthesis altered BAT activity, we
undertook a double-blind crossover study in 8 healthy normal weight subjects and 8
participants with obesity. These volunteers were given placebo and telotristat ethyl tablets
(an inhibitor of peripheral serotonin synthesis) for 2 weeks in random order. Telotristat ethyl
reduced ¹⁸F-FDG uptake by BAT, increased total cholesterol levels and suppressed the rise in
noradrenaline and insulin levels following cold exposure and oral glucose load respectively.
In conclusion, UCP1 expression in BAT is reduced with ageing, obesity and cardiometabolic
disease, in keeping with BAT dysfunctio
Understanding how tissue geometry and signalling dynamics control cell fate patterning using an in vitro model of human anterior primitive streak
No full textCell fate patterning remains difficult to study, particularly when focusing on gastrulation and the anterior primitive streak (APS). The cells present at and surrounding the APS will give rise to anterior and nascent mesoderm, definitive endoderm, neuromesodermal progenitors and axial mesendoderm. Despite the latter two being crucial for axial elongation and formation of the spinal column, how these cells spatiotemporally pattern and interact remains to be fully understood. This is mainly due to the inherent complexity of the emerging patterning and the difficulties to investigate this in vivo.
In recent years, the use of 2D micropatterning technologies has gained traction as means to partially reconstruct cellular environments and decouple biological variables in quantitative studies. Indeed, 2D micropatterning techniques have shed light into the effect of cellular confinement on signalling dynamics and cell fate decisions. In this thesis, micropatterned mediated cellular confinement has been used to investigate how spatiotemporal signalling regimes lead to APS cell fate decisions and organisations, and how spatial organisation organisation influences the signalling environment that dictates APS-associated cell fate emergence and patterning.
In the first chapter of this thesis, I address the issue of investigating cell fate decisions during gastrulation. To address this, I have developed an in vitro system that mimics the anterior primitive streak. Culturing 2D confined human embryonic stem cells stimulated with the WNT pathway activator CHIR9901 and FGF2, elicits the self-organisation of human embryonic stem cells into a multi-tissue architecture, composed of an internal region of pluripotent cells surrounded by posterior mesoderm and a ring-domain of definitive endoderm at the periphery of the colony. Importantly, I show that cell fate patterning only arises under strict confinement, with size of micropattern controlling different morphological changes. I further show that whilst initially there is an emergence of anterior primitive streak cell fates, such as neuromesodermal progenitors, these are then lost in favour of definitive endoderm. Thus, showing the potential of using this model to study anterior primitive streak cell fate decisions.
By using this model, in the second chapter, I then look at understanding cell fate patterning with a focus on the role of biochemical signalling pathways. Via small molecule inhibition of the WNT and NODAL pathways, I show that endogenous TGFβ signalling dynamics downstream of CHIR9901, controls cell fate decision. I show that whilst NODAL inhibition is required for NMP specification, sustained NODAL signalling drives endoderm emergence, in accordance with in vivo data on when these cell types emerge during gastrulation. I also show evidence of a crosstalk between TGFβ inhibition and increase in WNT activity which correlates with notochord progenitor emergence. Additionally, I show that both adequate NODAL suppression and colony size control neuromesodermal progenitor-like cell emergence and axial elongation. Altogether, this work provides insights into the specification of APS cell fates during gastrulation, highlighting the importance of spatiotemporal signalling dynamics in their emergence.
Finally in my last chapter I investigate the effects of geometrical confinement in cell fate specification, more specifically, the effects of boundary curvature. I show that epithelial organisation depends on the boundary curvature (convex vs concave). These changes induced by curvature ultimately defines the domain in which SOX17+ endodermal and SOX2+ pluripotent cells arise in our patterns. Additionally, I show that whilst SOX2+ epiblast-like cells coalesce to the centre of the colony via an actomyosin-mediated mechanisms, SOX17+ do not pattern in the same manner. These results further show the complexity of definitive endoderm patterning, the importance of tissue geometry, and highlight the further need to understand how
does definitive endoderm migrate in vivo within the developing embryo.
Overall, this work has lead to a first author publication in development and provides insights into the regulatory biochemical and physical mechanisms that ultimately control cell fate patterning and population balance of the progenitors emerging at the APS during gastrulation. Whilst more research is required, this work provides an initial step in understanding definitive endoderm patterning as well as a starting point for the investigation of neuromesodermal and notochordal progenitor interactions. Thus, providing further understanding of APS cell fate patterning, useful for the generation of APS cell fates in vitro for further clinical and therapeutic applications
Campus for higher education in China: what is the link between the outdoor spaces on campus with students’ health and wellbeing?
No full textWithin China's rapidly expanding higher education sector, students face mounting pressures that adversely impact their health and wellbeing. The outdoor environments of university campuses—key settings in students' daily lives—are increasingly recognised as potential resources for health promotion. Yet, empirical evidence on how Chinese students use, perceive, and benefit from these spaces remains limited, and design guidelines lack a robust
behavioural and evidence-based foundation. This study investigates the relationships between
the use and design of campus outdoor spaces (COS) and student wellbeing in the distinctive context of high-density Chinese campuses.
The research addressed four questions: (1) What is the relationship between frequency of COS
use and students' physical, mental, and social wellbeing, and how is it moderated by demographic and contextual factors? (2) What are students' preferences for different typologies of COS, and which environmental features are perceived as most important? (3)
What are the behavioural patterns of student activities across different COS, and how do they vary by time, gender, and period (lockdown vs. post-COVID)? (4) Which specific physical and design characteristics are most strongly associated with health-promoting activities?
A mixed-methods approach was employed across four case study campuses in Cangzhou, integrating a Public Participation GIS (PPGIS) survey (n=1285), systematic behavioural mapping (n=12,000+ observations), and environmental quality audits using the NEST tool. Spatial, statistical, and qualitative analyses were used to triangulate findings.
Key results demonstrated a consistent positive association between frequency of COS use and multiple dimensions of wellbeing. A composite Green-User Score (GUS) correlated significantly with better self-reported health (Rₛ ≈ 0.29–0.36), higher quality of life (Rₛ ≈ 0.31–0.45), reduced stress (Rₛ ≈ -0.29 to -0.32), and moderately with improved academic performance. Notably, active—not just passive—use was a critical predictor of benefits. Behavioural mapping revealed that social interaction—especially ‘chatting’—was the most prevalent activity across all space types, underscoring the role of COS as vital social infrastructure. Student preferences
strongly favoured functional, well-maintained, and socially conducive spaces (e.g., sports facilities, hard squares, accessible lakes) over under-managed green areas (e.g., woods, green corridors), challenging a simplistic ‘green vs. grey’ dichotomy. Key design characteristics supporting health-promoting activities included flat paving, varied seating, shading facilities, good lighting, high maintenance, and visual permeability.
The study concludes that well-designed COS can significantly contribute to student health and
wellbeing, particularly when they facilitate active use and social interaction. Findings advocate for a shift in Chinese campus planning policy from prescriptive green-space targets towards evidence-based, behaviour-centred design that prioritises functional affordances, social support, and perceptual quality. The research provides transferable insights for the development of healthier and more inclusive academic environments in China and similar high-density educational settings internationally
Tools for monitoring and managing sustainable improvement in honeybee populations
No full textThe Western honeybee is a species of economic importance globally, yet in recent decades it has been experiencing substantial colony losses that result in economic damage and possibly decreased genetic diversity.
This situation is highlighting the need for honeybee breeding and conservation programmes. Monitoring genetic variability is an essential component of breeding programmes to ensure genetic gain and managing both global and local genetic diversity.
One way to study breeding and conservation programmes is via stochastic simulation. Stochastic simulators are essential for rapid and low-cost testing of breeding decisions and methods, aiding in the optimization of current programmes or the establishment of new ones. These simulations provide valuable insights when they accurately model realistic populations and parameters, allowing for a deeper understanding of factors such as population genetic variability, responses to selection and levels of inbreeding.
There was, however, no existing genetics simulator that allows for a detailed simulation of individual honeybee. Therefore, the aim of this thesis was to develop a simulation tool and demonstrate concepts of honeybee relatedness to aid in the sustainable improvement of managed honeybee populations.
Chapter 1 introduces key concepts essential to the understanding of the thesis.
It’s first focus is on honeybees; describing some of their basic biology, their economic importance to humans, addressing the stressors affecting honeybee populations, and why maintaining genetic diversity within these population is so critical.
The chapter then delves into quantitative genetic methods for calculating relatedness, outlining methods such as relatedness coefficients derived from both pedigree and genome-wide data, as well as their pedigree decomposition of genetic values into parent average and Mendelian sampling deviations. The penultimate section describes stochastic simulators and their application in quantitative genetics and selective breeding.
Finally, the chapter concludes by outlining the thesis objectives, providing the necessary understanding and context as to why this thesis is relevant.
Chapter 2 of this thesis describes the implementation of SIMplyBee, a holistic simulator of honeybee populations and breeding programs that a small team and myself have developed as an R package. SIMplyBee builds upon the stochastic simulator AlphaSimR that simulates individuals with their corresponding genomes and quantitative genetic values. To enable honeybee-specific simulations, AlphaSimR was extended by developing classes for global simulation parameters, for a honeybee single colony, and multiple colonies. Functions to address major honeybee specificities were also developed: honeybee genome, haplodiploid inheritance, social organisation, complementary sex determination, polyandry, colony events, and quantitative genetics at the individual- and colony-levels. SIMplyBee provides a research platform for testing breeding and conservation strategies and their effect on future genetic gain and genetic variability.
Chapter 3 demonstrates principles of genetic relatedness in honeybees by analysing the genetic and pedigree information from a SIMplyBee simulation, comparing closed and hybrid populations. Coefficients of relatedness are regularly used to measure genetic similarity within and between populations and their individuals. Although the haplodiploid inheritance of honeybees is well understood, interpreting the various types of relatedness coefficients based on pedigree and genotype data is a challenge for researchers and practitioners in honeybee breeding. I evaluated the relatedness between individuals within a colony, between queens of the same population, and between queens of different populations.
The results demonstrated an alignment of mean relatedness using different sources of information when calculated using the same founder population. Identity-by-state (IBS) relatedness varied significantly when calculated relative to different founder populations. While this result is anticipated, it highlights the need for caution when comparing values across studies that use different founder populations with varying allele frequencies. Misestimation of relatedness if interpreted incorrectly can lead to inappropriate breeding and conservation decisions. These decisions may exacerbate inbreeding, reduce a population’s genetic diversity, or compromise genetic gain. This emphasises the need for better understanding and standardising methodologies for computing relatedness coefficients, to ensure accurate comparability in relatedness studies.
In Chapter 4, I evaluated pedigree reconstruction and patriline determination in honeybees using both real and simulated data. Comparison of simulated data and real data allows researchers to identify weaknesses in established models of genetic inheritance associated methods, or errors in real data, and analyse the accuracy of outputs. In this chapter, real genotype data collected from the mating experiment "BeeConSel" was replicated using SIMplyBee-generated genotypes. Four simulated SNP array sizes were examined to assess the limitations of each software and used the actual pedigree information of the simulation to measure the precision of sire assignments. While gametic information is an important aspect of genetic analysis, gap in the literature was identified for a method to assign parent-of-origin to haplotypes derived from phased genotypes. To address this gap, I developed an R function for this task. Additionally, the information gathered from these tasks was used to develop and evaluate a method for determining the number of patrilines in a honeybee colony. My analysis demonstrated that the effectiveness of different software tools and SNP array sizes in determining paternal assignments and reconstructing pedigrees varies significantly. Real-world data showed variations in software performance, revealing that simulated results often failed to capture the full complexity of actual genetic data. Moreover, the observed deviations in haplotype assignments highlight potential issues with phasing accuracy and the need for better methods and higher-quality data in genetic studies.
Overall, my thesis explores the complexities of honeybee biology and breeding, through the development and application of the honeybee-specific simulation tool, SIMplyBee. Through the analysis of the simulation outputs using quantitative genetic methods and comparison to real data, the thesis provides insight into optimizing breeding programmes and managing the genetic diversity of honeybee populations.
The thesis concludes with Chapter 5, which reiterates the objectives and findings of Chapters 2-4 before discussing the relevance of these findings in relation to current research. This chapter also expands upon the limitations of the work and the implications of the thesis’ findings on future work
Fairly quantifying insertion and deletion landscapes in mammalian genomes
Get PDFInsertion and deletion mutations (indels) are a major component of genome evolution, and their disruptive properties play an important role in genetic diseases, including cancer. Incidence and sequence context of indels are clinically important in characterising tumours and detecting underlying molecular defects. Despite the functional and clinical importance of indels, they have often been overlooked, discarded as difficult to confidently identify in panels and problematic to meaningfully categorise. A key component of difficulty with indels lies in their association with repetitive sequences. Tandemly repeated sequences (e.g. AAAAA or ACACAC) are often enriched for indels and may be shortened or expanded by indel generating processes. Indels often cannot be unambiguously positioned onto repetitive sequences, adding to their complexity. These features make indels challenging to use in approaches such as mutation signature analysis, which have been illuminating when applied to single nucleotide substitutions. However, indels have considerable potential for mutation signature analysis if their underpinning challenges can be overcome.
This thesis describes the development of a novel framework that quantifies the ambiguity inherent in indel alignment to systematically score the repetitiveness of the local sequence context. This framework provides means for sequence composition correction and to generate null expectations from any sequence. It allows composition corrected indel rates to be compared between genomic regions and species. This has previously been the limiting factor in current indel analyses, as this thesis outlines whilst characterising indel landscapes in four mouse species. Exploring indel rate variation in human colon cancer genomes revealed an indel mutational signature associated with DNA mismatch repair independently of replication timing. Further evaluation of 2-5-bp deletion rate variation at nucleosomes in other cancers did not indicate a local mutation enrichment.
Strand-specific DNA analyses in this thesis identify indel-substitution clusters in mouse liver cancer, showing that 1bp deletions are caused by base-skipping and downstream substitutions by collateral mutagenesis through translesion synthesis (TLS) polymerases bypassing DNA lesions. Conversely, 1bp insertions downstream of substitutions are caused by collateral mutagenesis rather than DNA damage. In human melanoma, similar deletion-substitution clusters with 1bp deletions in dipyrimidine context were detected. The mutational signature of substitutions downstream of deletions are reminiscent of the collateral mutations found in mouse tumours, suggesting that these clusters are the result of TLS polymerases bypassing UV-induced DNA lesions. Surprisingly, these clusters show that UV-induced damaged Ts in dipyrimidine context may mutate through translesion synthesis. This is typically undetected when considering single base substitutions but these results revealed T mutations as deletions.
Together, this work demonstrates that sequence alignment ambiguity scoring can account for variability in sequence composition in order to make accurate comparison of indel mutation rates across the genome and better resolve aetiology of the events and processes leading to cancer
Investigating the role of primary cilia loss in intrahepatic cholangiocarcinoma
No full textPrimary cilia (PC) are important sensory organelles which protrude from the epithelial cells lining bile ducts (cholangiocytes), where they serve as sensors of bile composition, flow and osmolality. PC malformation has been reported in cholangiocarcinoma (CCA), a neoplasia that originates from these biliary epithelial cells. Despite changes in cilia regulation within CCA, PC-loss alone is insufficient for CCA initiation. However, we show that when PC is deleted from biliary cells in an intrahepatic CCA (iCCA) mouse model, tumour burden significantly increases. Interestingly, these non-ciliated tumour cells do not show significant differences in cell proliferation when compared to ciliated cells. In this thesis, I explore the transcriptional differences between ciliated and non-ciliated iCCA tumour cells and ask how these changes influence the immune cells that surround emerging tumours.
Using an iCCA mouse model in which Trp53 and Pten are deleted alongside the essential cilia gene Wdr35, I found that loss of cilia accelerated tumour development. In the absence of chronic inflammation, cilia loss in the context of tumour suppressor loss accelerated the development of biliary neoplasia. Bulk RNA sequencing of these tumour cells demonstrated that PC-loss increased the expression of pro-inflammatory cytokines such as Cxcl9, Cxcl10 and Cxcl11. However, the expression of neutrophil-specific chemokines such as Cxcl1, Cxcl2 and Cxcl5 was significantly suppressed. In concordance, immunohistochemical analysis revealed that neutrophil infiltration was frequently decreased in the livers of mice with non-ciliated biliary cells when compared to mice with ciliated cholangiocytes.
To further explore tumour-neutrophil interactions in vitro, I derived organoid lines from biliary cells isolated from ciliated and non-ciliated mouse models of iCCA and showed that the expression and secretion of neutrophil-specific chemokines was significantly downregulated in non-ciliated organoids. I then isolated naïve WT bone marrow neutrophils and co-cultured these with organoid conditioned media (CM). In a Transwell migration assay, I showed that neutrophils exposed to non-ciliated CM migrated significantly less than when cultured in the presence of ciliated CM, suggesting that PC-loss on cancer cells directly impacts neutrophil migration.
The work presented here demonstrates how losing primary cilia can promote a more immunosuppressive microenvironment during iCCA development and highlights the importance of continuing to address the status and role of primary cilia in future iCCA studies
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