143174 research outputs found
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Mass spectrometric investigation into glycomes implicated in human reproductive biology
No full textGlycosylation is a complex biological process where enzymes attach glycans to proteins, altering their structure and function. In reproduction, glycoproteins support sperm immunoprotection, modulate sperm-oocyte interactions, and influence implantation. Glycan sequences on cell surfaces, such as uterine epithelial cells, facilitate cell-to-cell communication, playing roles in both normal physiology and pathological conditions. Understanding glycan structures and functions enhances our knowledge of glycosylation in reproduction and its potential therapeutic and diagnostic applications. This project examined the differential glycosylation of human chorionic gonadotrophin (hCG) in normal and abnormal pregnancies, as well as across pregnancy stages. Additionally, we analysed glycomic profiles of normal endometrial tissue and tissue affected by endometriosis. Structural characterisation of glycans was performed using Matrix-Assisted Laser Desorption/Ionisation Time-of-Flight Mass Spectrometry (MALDI-TOF MS), Tandem Mass Spectrometry (MS/MS), and enzymatic digestion. Enzymatic digestion allowed for the breakdown of glycans into smaller components, facilitating their structural identification. Our findings reveal that βhCG N-glycans exhibit high mannose, bi-, tri-, and tetraantennary structures. The novel identification of bisecting N-glycans in β-hCG, alongside their increased expression in late pregnancy, suggests a role in immune defence, possibly supporting immune tolerance. The data confirm that hCG undergoes differential glycosylation throughout pregnancy and that abnormal glycosylation is linked to pathological pregnancies. Analysis of endometrial tissue N-glycans showed that endometriosis is associated with altered glycan profiles, likely due to hyposialylation. The N-glycan profiles presented here allow for direct comparisons between normal and pathological states. Changes in glycan composition and structure, particularly those with immunomodulatory potential, may serve as targets for nonsurgical endometriosis management through diagnostic and therapeutic strategies.Open Acces
Artificial intelligence analysis of the single-lead ECG predicts long-term clinical outcomes
No full textAims
Artificial intelligence (AI) applied to a single-lead electrocardiogram (AI-ECG) can detect impaired left ventricular systolic dysfunction [LVSD: left ventricular ejection fraction (LVEF) ≤ 40%]. This study aimed to determine if AI-ECG can also predict the two-year risk of major adverse cardiovascular events (MACE) and all-cause mortality independent of LVSD.
Methods and results
Clinical outcomes after two-year follow-up were collected on patients who attended for routine echocardiography and received simultaneous single-lead-ECG recording for AI-ECG analysis. MACE and all-cause mortality were compared by Cox regression, measured against the classification of LVEF > or ≤40%. A subgroup analysis was performed on patients with echocardiographic LVEF ≥ 50%. With previously established thresholds, ‘positive’ AI-ECG was defined as an LVEF-predicted ≤40%, and negative AI-ECG signified an LVEF-predicted >40%; 1007 patients were included for analysis (mean age, 62.3 years; 52.4% male). 339 (33.7%) had an AI-ECG-predicted LVEF ≤ 40% and had a higher MACE rate (LVEF ≤ 40% vs. >40%: 34.2% vs.11.9%; adjusted hazard ratio (aHR) 1.93; 95% CI, 1.39–2.69; P < 0.001), primarily driven by increased mortality (23% vs. 9.6%; P < 0.001; aHR 1.56; 95% CI, 1.06–2.29; P = 0.0239). In patients with echocardiographic LVEF ≥ 50%, there was a higher incidence of MACE in those with an AI-ECG ‘false positive’ prediction of LVEF ≤ 40% (27.2% vs.11.9%; P < 0.001; aHR 1.71 and 95% CI, 1.11–2.47) and all-cause mortality (20.4% vs. 9.6%; P < 0.001; aHR 1.59, 95% CI, 1.09–2.42).
Conclusion
An AI-ECG algorithm designed to detect LVEF ≤ 40% can also identify patients at risk of MACE and all-cause mortality from single-lead ECG recording—independent of actual LVEF on echo. This requires further evaluation as a point-of-care risk stratification tool
Acquisition of azole resistant aspergillus fumigatus in individuals with chronic respiratory diseases.
No full textAzole resistance in Aspergillus fumigatus presents a significant challenge to effective treatment in chronic respiratory diseases, where prolonged azole antifungal therapy is routinely employed. Individuals with chronic respiratory diseases are disproportionately impacted by the emergence of drug resistance, thought to be driven by strong selective pressures associated with environmental fungicide use and oral azole therapy. The mechanisms driving antifungal resistance acquisition and evolution in individuals with chronic respiratory diseases remain poorly described. This thesis investigates the prevalence, genetic diversity, and transmission dynamics of azole resistant A. fumigatus across clinical and environmental settings in the UK, with a focus on the TR34/L98H resistance mechanism. Utilising whole-genome sequencing (WGS) and high-throughput phenotyping, 912 isolates were characterised to elucidate the genetic basis of resistance and adaptation. Analysis of 210 virulence-associated genes revealed increased frequencies of nonsynonymous SNPs in TR34/L98H isolates, indicating genetic diversification that may support adaptation under antifungal pressures. Phenotypic profiling under 384 growth conditions highlighted metabolic adaptations in TR34/L98H isolates, including shifts in carbon and nitrogen utilisation, osmotic stress tolerance, and reduced reliance on d-trehalose during germination, potentially contributing to persistence in clinical settings. Prospective longitudinal surveillance of 55 individuals with chronic respiratory diseases identified a 25% prevalence of azole resistance, predominantly among those with chronic pulmonary aspergillosis and cystic fibrosis. WGS of clinical and environmental isolates revealed the dominance of TR34/L98H and emerging TR46/Y121F/T289A resistance mechanisms. Integration of environmental and clinical WGS data from hospital settings revealed genetic similarities between isolates, suggesting bidirectional transmission pathways. Additionally, home environments were surveyed through a citizen science approach, identifying resistant isolates in 60% of homes, with soil as a key reservoir. These findings emphasise the role of environmental reservoirs in the persistence and transmission of azole-resistant A. fumigatus, underscoring the need for advanced surveillance strategies.Open Acces
On the identification of novel bragg phenomena in static and dynamic media
No full textThis dissertation extends scalar coupled-wave theory through the application of Möbius transformations, thereby reducing the coupled-wave equations to a first-order non-linear differential equation of a single real variable. This reformulation facilitates both analytical and numerical approaches to complex refractive index modulation scenarios, establishing an innovative connection between coupled-wave theory and coupled oscillators, and offering novel insights into photonic bandgaps. The theory is applied to optically active structurally chiral media, demonstrating that giant chirality may induce back-scattering of both polarisations under conditions intrinsically aligned with negative refraction. A novel Bragg-like phenomenon is subsequently introduced, observable in uniform media, where tuning is accomplished by adjusting the medium parameters, rather than by wavelength-matching, thereby enabling broadband, polarisation-selective reflection. Additionally, non-axial wave propagation in axially bi-anisotropic media reveals a similar circular Bragg-like effect with more flexible tuning requirements, with the inclination angle exercising control over the resonance location and corresponding bandwidth.
The second part of this dissertation initiates by examining photonic time-crystals characterised by a periodically time-varying permittivity. Employing Möbius transformations, it explores the dispersion and response characteristics of a finite ‘time-slab' of the considered dynamic medium, uncovering the temporal analogue of Bragg gratings and elucidating time modulation as a platform for parametric amplification. The analysis also considers the influence of almost-periodicity on the first-order momentum gap formation in photonic time-crystals, demonstrating how material imperfections can paradoxically broaden modal coupling to augment amplification. Further examination of light propagation in time-periodic chiral media, characterised by time-varying permittivity, permeability, and chirality parameter, reveals distinctive synchronisation phenomena for contra-handed modes. The findings illustrate that extreme optical rotation triggers a temporal analogue of the chirality-induced negative refraction and highlight the potential for designing an all-optical modulator capable of simultaneously amplifying the signal whilst controlling its polarisation.Open Acces
Peptide‐drug conjugates: a new hope for cancer
No full textPeptide-drug conjugates (PDCs) are advancing as targeted cancer therapies, leveraging lessons from antibody-drug conjugates (ADCs) to improve tumour specificity. These molecules combine a homing peptide with a cytotoxic payload via a linker, enabling precise drug delivery while sparing healthy tissue. Despite their potential, PDCs face challenges including metabolic instability, premature payload release and rapid clearance, limiting clinical success. Only Lutathera remains FDA-approved after Pepaxto's withdrawal, though Pepaxto retains EMA and MHRA approval—highlighting regulatory and technical complexities. Most PDCs target overexpressed receptors (e.g., somatostatin and GnRH), though novel designs like CBX-12 employ alternative strategies. Currently, six PDCs are in Phase III trials, with ~96 in development, signalling growing interest. This review explores how ADC research has guided PDC optimisation, particularly in linker chemistry and payload selection. We analyse key structural features governing PDC efficacy, including peptide-receptor binding and intracellular trafficking. Innovations in stable linkers and tumour-selective activation mechanisms are critical to overcoming pharmacokinetic hurdles. Promising candidates in late-stage trials are highlighted, emphasising their potential to address unmet needs in oncology. By refining targeting precision and payload delivery, next-generation PDCs may expand treatment options for resistant cancers, bridging the gap between biologics and small-molecule therapies
Structural and functional characterisation of the human vitamin c transporters
No full textVitamin C is an essential micronutrient that functions as an antioxidant and a cofactor for several enzymes. Vitamin C has also shown potential as a possible therapeutic for treating several diseases such as cancers and neurodegenerative diseases. Humans and other primates are not able to synthesise vitamin C, so they must obtain it exclusively from their diet. The sodium-dependent vitamin C transporters (SVCT1 and SVCT2) are responsible for the cellular uptake of the reduced form of vitamin C, ascorbic acid. A greater understanding of the molecular mechanisms of the SVCTs would provide insights into their roles and potential with respect to health and disease.
The recent mouse SVCT1 structure provided insights into the molecular determinants of substrate binding and revealed that SVCT1 exists as a homodimer. Based on this structure and that of a related protein, I introduced several individual mutations, such as L65A, F112A, R206A, I439A and L458G, to human SVCT1 (hSVCT1) and explored their effect on expression, membrane trafficking and protein function. Results showed that these residues are important in substrate transport with no influence on expression and membrane trafficking. In addition, truncations at the N- and C-terminal ends of the protein confirmed the importance of the C-terminus in membrane trafficking. Correct dimerisation was also found to be essential in both membrane trafficking and function of hSVCT1.
hSVCT2 differs from hSVCT1 in that, in addition to transporting ascorbic acid, it activates the JAK/STAT pathway. However, this transceptor activity is not well understood as the structure of hSVCT2 has not yet been solved. Here, I performed extensive optimisation of hSVCT2 expression and purification, including changes to the expression construct, buffer conditions and purification resin, enabling the production of stable protein for structural studies. This will provide the basis for the future structural characterisation of hSVCT2 using cryo-EM.Open Acces
The contribution of miRNAs to airway inflammation
No full textThis thesis explores the role of microRNAs (miRNAs) in asthma and eosinophilic lung disease (ELD) through a series of next generation sequencing (NGS) studies. The research aimed to identify differentially expressed (DE) miRNAs and their potential biological implications in these respiratory conditions.
Initially, a nasosorption sampling method was optimized for miRNA sequencing from nasal mucosal samples, demonstrating its feasibility as a non-invasive approach for planned subsequent respiratory studies. In addition to optimization of the sampling, extraction and sequencing library generation an analytical pipeline for downstream processing of generated sequencing data was also developed. Due to the impacts of the COVID pandemic the focus of the thesis had to alter. Consequently, research became focused on the analysis of circulating miRNA expression in whole blood samples from severe asthmatics, non-severe asthmatics, and healthy controls using NGS. These investigations identified distinct miRNA signatures associated with asthma severity, including miR-1304-3p, miR-32-5p, and members of the let-7 family.
Gene ontology analyses of the DE-miRNAs implicated their involvement in processes such as apoptosis regulation, cellular metabolism, and enzyme binding, providing insights into potential underlying molecular mechanisms of disease. The research also explored miRNA expression in both whole blood and lung biopsy samples from individuals with ELD and healthy controls, revealing potential associations between miRNAs and clinical features, as seen with miR-202-5p and Eosinophil counts.
While sample size limitations and technical challenges were encountered, particularly in relation to the ELD study, the research highlighted the complexity of miRNA regulatory networks in respiratory diseases. The findings lay the groundwork for future research aimed at developing novel diagnostic tools and personalized treatment approaches for respiratory diseases.Open Acces
Microbes-on-a-chip: deciphering the responsiveness of microbes using microfluidic chemostats
No full textSoil is essential for all forms of life. Highly complex and heterogeneous, it contains immense biodiversity: plants, fungi, bacteria, and more. Interestingly, the vast majority of bacteria in soil lie dormant. Specific germinants can reactivate some of these metabolically inactive spores. Similarly, some fungi also form diverse spores. Understanding this germination process is crucial for sustainable agriculture, as these spores interact with plants and other microbes and could be used as biocontrol agents. The main objective of this thesis was to develop a tool to observe and measure the germination of microbial spores in the presence of different germinants.
Microfluidics technologies refer to devices that handle fluids at the micrometre scale. They allow for continuous monitoring of individual cells over long periods, revealing behaviours that would be masked when looking only at the population level or only at snapshots.
The main microfluidic device developed in this thesis, called the 4-Conditions Microfluidic Chemostat (4CMC), consists of an array of microchemostats exposed to four different environmental conditions. It was validated through a series of experiments with the model organism Bacillus subtilis, which revealed new insights into the effect of spore density on spore germination. The device was also used to elucidate the responsiveness of spores of two non-model soil dwellers, the bacterium Ammoniphilus oxalaticus and the fungus Trichoderma rossicum, both of which are potential biocontrol agents. This research led to new findings on A. oxalaticus spores' response to oxalate, an abundant component of soil, and on the effect of nutrient availability on T. rossicum spores.
The 4CMC was also employed to study the responsiveness of engineered bacteria and yeast communities, revealing behaviours that standard population-level assays had masked, as well as choanoflagellates.Open Acces
Virtual failure assessment diagrams for hydrogen transmission pipelines
No full textWe combine state-of-the-art thermo-metallurgical welding process modeling with coupled diffusion-elastic–plastic phase field fracture simulations to predict the failure states of hydrogen transport pipelines. This enables quantitatively resolving residual stress states and the role of brittle, hard regions of the weld such as the heat affected zone (HAZ). Failure pressures can be efficiently quantified as a function of asset state (existing defects), materials and weld procedures adopted, and hydrogen purity. Importantly, simulations spanning numerous relevant conditions (defect size and orientations) are used to build Virtual Failure Assessment Diagrams (FADs), enabling a straightforward uptake of this mechanistic approach in fitness-for-service assessment. Model predictions are in very good agreement with FAD approaches from the standards but show that the latter are not conservative when resolving the heterogeneous nature of the weld microstructure. Appropriate, mechanistic FAD safety factors are established that account for the role of residual stresses and hard, brittle weld regions
Developing computational and machine learning techniques to robustly detect the genome’s cell type-specific, protein coding and non-coding effects in alzheimer's disease
No full textFurthering our understanding into the cell’s transcriptional and regulatory mechanisms and how changes in these relate to Alzheimer’s disease (AD), promises to unravel the aetiology of this complex disease, potentially yielding improved therapeutics. However, doing so requires both AD-specific and broader advancements in experimental assays and computational approaches in the field. Here, we attempt to address such shortcomings in computational analyses, covering both the protein coding and non-coding genome. These approaches include the standardisation of processing and quality control of genetic information, the standardisation of analysis for cell type-specific transcriptional changes—both broadly and in the study of AD—prioritising functional and disease-relevant genomic loci in silico using deep learning to link epigenetics to transcription, and predicting cell type-specific effects of genetic variants while accounting for distal regulation with genomic deep learning models. Although the focus of this work is AD, the developed, open-source, computational and deep learning techniques are all broadly applicable to our comprehension of the cis-regulatory code.Open Acces