8,898 research outputs found

    Bioactive Self-Assembled Protein Nanosheets for Stem Cell-Based Biotechnologies

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    Tissue and stem cell culture methods have been dominated by glass and plastic substrates such as Tissue culture plastic. These solid substrates, although widely used, are associated with poor scalability for adherent stem cell expansion in systems such as 3D bioreactors and the design of parallel culture systems. Therefore, investigating strategies to bypass these obstacles in stem cell expansion is essential to enable the wider translation of stem cell technologies. An alternative strategy recently proposed consists in using a liquid surface instead, such as an oil, and associated oil droplets. Indeed, emulsions can be formed using protein nanosheets to stabilise oil/water interfaces to promote the adhesion of stem cells and enable their proliferation. These nanosheets exhibit enhanced interfacial mechanics and allow the introduction of bioactive components via recombinant protein expression to promote bioactivity. Beyond the application of resulting bioemulsions for the expansion of Mesenchymal stem cells, the impact of these bioactive interfaces on the differentiation of iPSCs and the development of cerebral organoids will be presented. The Bovine serum albumin protein was recombinantly modified to attach an N-terminal Avi-Tag, this was expressed and purified from the yeast P. pastoris expression system. The Avi-tag was then biotinylated in vitro by recombinantly expressed BirA. Emulsions of a specific size were formed using the newly biotinylated Bt-BSA protein and functionalized with a cascade of components to mimic cell-cell ligands, this resulted in bioemulsions with a bioactive surface that can interact with surrounding cells. These functionalised droplets were integrated into developing cerebral organoids and their impact on phenotype was studied. The droplets were found not to deform sufficiently to allow mechanical forces to be measured, yet the many of these droplets were retained within the organoids which led to an interesting phenotype within the organoids. The developing rosettes were found to develop enlarged lumens shown by an increase in area, this phenotype did not impact the differentiation into the cerebral lineage depicted by immunohistochemistry of hallmark marker of neuronal differentiation within organoids retaining droplets. The interfacial mechanics of fibrinogen nanosheets treated with varying concentrations of thrombin was studied using interfacial shear rheology. The effect of thrombin significantly altered the interfacial mechanics with the lower concentration of thrombin significantly increasing the toughness multiple folds and decreasing the elasticity of the nanosheets. Additionally, the nanostructure of nanosheets was studied using SEM and TEM and traditional fibrin fibres were found to not form at these interfaces, but local rearrangements and retractions in the thrombin treated nanosheets were observed. Finally, these enhanced mechanical properties promoted the proliferation and expansion of Mesenchymal stem cells on quasi-2D and 3D interfaces

    Investigating the impact of lung cancer cell-of-origin on tumour metabolic phenotype and heterogeneity

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    Non-small-cell lung cancer has been described as highly heterogenous which results in different metabolic phenotypes. There are multiple factors which contribute to this heterogeneity, one of which is the tumour cell-of-origin. In the lung, there are five cell types reported to be cells-of-origin: alveolar epithelial type 2, club, basal, neuroendocrine and bronchioalveolar stem cells. This project focuses on the interaction between the cell-of-origin and the metabolic phenotype of lung cancer, and we aim to assess the contribution of the cell-of-origin to lung cancer metabolic resultant phenotype and heterogeneity. To accomplish this, we have established two complementary model systems, one in vitro and one in vivo. In our in vitro model, we isolated specific lung cell types, including AT2 cells, basal cells, and club cells, utilising their unique cell surface markers. By introducing oncogenic KRAS mutations and deleting the P53 gene, we are creating lineage-restricted organoids. These organoids will serve as valuable tools for characterizing the metabolic aspects of tumours arising from different cell-of-origin backgrounds within an in vitro setting. In our in vivo model, we induced NSCLC tumours in mice with genetic modifications using viral vectors, namely Ad5-mSPC-Cre, Ad5-CC10-Cre, and Ad5- bk5-Cre. These vectors are selectively expressed in AT2, club, and basal cells, respectively. To ensure the validity of our comparisons, we have carefully monitored tumour growth dynamics and burden in these mouse models. Our comprehensive analysis has revealed three distinct transcriptomic subtypes (S1, S2, and Acetate) within these NSCLC tumours. Notably, S1 and Acetate subtypes are enriched in tumours originating from specific cell types. Positron emission tomography (PET) imaging has unveiled metabolic variations, with S1 tumours displaying heightened [18F]FDG uptake and the Acetate subtype exhibiting increased [11C]acetate uptake. Furthermore, our multi-omics approach, encompassing transcriptomics, proteomics, and metabolomics, has exposed disparities in critical metabolic pathways, such as glycolysis, hypoxia response, and apoptosis. In summary, our research provides a comprehensive examination of the metabolic heterogeneity of NSCLC based on the cell-of-origin independently of genomic alterations

    How to Turn Your Knowledge Graph Embeddings into Generative Models

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    Some of the most successful knowledge graph embedding (KGE) models for link prediction -- CP, RESCAL, TuckER, ComplEx -- can be interpreted as energy-based models. Under this perspective they are not amenable for exact maximum-likelihood estimation (MLE), sampling and struggle to integrate logical constraints. This work re-interprets the score functions of these KGEs as circuits -- constrained computational graphs allowing efficient marginalisation. Then, we design two recipes to obtain efficient generative circuit models by either restricting their activations to be non-negative or squaring their outputs. Our interpretation comes with little or no loss of performance for link prediction, while the circuits framework unlocks exact learning by MLE, efficient sampling of new triples, and guarantee that logical constraints are satisfied by design. Furthermore, our models scale more gracefully than the original KGEs on graphs with millions of entities

    The role of the oral microbiome in the immunobullous diseases pemphigus vulgaris and mucous membrane pemphigoid and oral lichen planus

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    Saliva is formed from contributions of salivary glands and the serum exudates principally from gingival margins or damaged mucosa combined with components derived from the environment, including a community of microorganisms - the microbiome. I postulate that changes in microbial diversity and population structure play key roles in the modulation of host- microbial interactions which influence both the hypersensitive autoimmune responses and inflammation seen in these inflammatory mucocutaneous disorders. For my research, a total of 186 participants were recruited: 48 mucous membrane pemphigoid (MMP), 48 pemphigus vulgaris (PV), 50 oral lichen planus (OLP) patients, and 40 healthy controls. Unstimulated whole saliva, subgingival plaque, serum, and plasma samples were collected from 186 participants. In addition, metadata were collected on the following covariates: age, gender, ethnicity, type of the diet, disease history and therapeutic intervention in the preceding six months. Oral disease severity scores (ODSS) were assessed, and periodontal status was examined using a periodontal six pocket chart. To characterise microbiome profiles, saliva and subgingival plaque were processed for sequencing genomic DNA using the NGS Shotgun metagenomics sequencing technique. Inflammatory cytokines and proteases were investigated in saliva and serum using Human Magnetic Luminex Screening Assay (R&D Systems). Selected cytokines were analysed by enzyme-linked immunosorbent assay (ELISA) technique (R&D Systems) to determine host inflammatory responses in saliva and serum samples. Additionally, saliva and plasma samples were analysed for metabolites by nuclear magnetic resonance (NMR). Significant increases in periodontal score (PISA) in all three groups of disease were identified compared to healthy control group with significant positive correlation between oral disease severity (ODSS) and PISA in OLP and PV groups. All three groups of diseases had significantly higher levels of inflammatory Th2/Th17 cytokines (IL-6, IL-13 and IL-17 in saliva samples), as well as higher levels of MMP-3 matrixins in saliva. In addition, there were positive correlations between ODSS and salivary IL-6, IL-13 and MMP-3 in saliva of OLP, salivary and serum levels of IL-6 and MMP-3 in MMP group, and significant association of salivary IL-6, IL-1β and MMP-3 in PV group. Metabolomic data showed that saliva is a better biofluid for correlation of the metabolomic profile with oral disease severity than plasma. Salivary ethanol was corelated with disease severity in the OLP group, whereas in PV was a strong correlation of ODSS with choline. Finally, a unique microbial community was found in each group of diseases. In the MMP group, ODSS was significantly correlated with L. hofstadii, C. sputigena, N. meningitidis, N. cinerea and P. sacchar0lytica. In PV, a positive correlation was found with F. nucleatum, G. morbillorum, and E. corrodens, G. elegans, H. sapiens and T. vincentii. In OLP, the disease tends to worsen when there was reduced abundance of X. cellulosilytica, Actinomyces ICM 47, S. parasanguinis, S. salivarius, L. mirabilis and O. sinus. Lower microbial diversity was correlated with ODSS in saliva and plaque of the OLP group. In conclusion, this study provides strong evidence of the complex interplay between the oral microbiome, immunological factors, and metabolites in the context of immunobullous diseases and OLP. The findings highlight the integral role of oral bacteria in disease progression, the significance of immune dysregulation, and the potential impact of specific microbial species and metabolic pathways. These insights give the way for further research and clinical applications, offering the promise of personalized approaches for diagnosis, and management of OLP, MMP and PV. Future investigations should focus on discovering the mechanistic details underlying these associations and validating the identified biomarkers in larger patient cohorts, ultimately contributing to a deeper understanding of the pathogenesis of these conditions

    Long-Molecule Assessment of Ribosomal DNA and RNA

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    The genes encoding ribosomal RNA and their transcriptional products are essential for life, however, remain poorly understood. Even with the advent of long-range sequencing methodologies, rDNA loci are difficult to study and remain obscure, prompting the consideration of alternative methods to probing this critical region of the genome. The research outlined in this thesis utilises molecular combing, a fibre stretching technique, to isolate DNA molecules measuring more than 5 Mbp in length. The capture of DNA molecules of this size should assist in exploring the architecture of entire rDNA clusters at the single-molecule level. Combining molecular combing with SNP targeting probes, this study aims to distinguish and assess the arrangement of rDNA promoter variants which have been shown to exhibit dramatically different environmental sensitivity. Additionally, through the application of Oxford Nanopore Technologies direct RNA sequencing, the work here has demonstrated the capture of near full-length rRNA primary transcripts, which will allow for assessing post-transcriptional modification across the length of multiple coding subunits within a single molecule, for the first time. Furthermore, an exploration of RNA modification profiles across sample types representative of different developmental stages has been conducted. This study predicts many sites to be differentially modified across these different developmental conditions, several of which are known to be important for, if not crucial in ribosome biogenesis and function. The work outlined in this thesis provides a framework for future studies to conduct long-molecule, genetic, and epitranscriptome profiling of this vital region of the genome, and its dynamic response to a changing environment

    Computational and experimental studies on the reaction mechanism of bio-oil components with additives for increased stability and fuel quality

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    As one of the world’s largest palm oil producers, Malaysia encountered a major disposal problem as vast amount of oil palm biomass wastes are produced. To overcome this problem, these biomass wastes can be liquefied into biofuel with fast pyrolysis technology. However, further upgradation of fast pyrolysis bio-oil via direct solvent addition was required to overcome it’s undesirable attributes. In addition, the high production cost of biofuels often hinders its commercialisation. Thus, the designed solvent-oil blend needs to achieve both fuel functionality and economic targets to be competitive with the conventional diesel fuel. In this thesis, a multi-stage computer-aided molecular design (CAMD) framework was employed for bio-oil solvent design. In the design problem, molecular signature descriptors were applied to accommodate different classes of property prediction models. However, the complexity of the CAMD problem increases as the height of signature increases due to the combinatorial nature of higher order signature. Thus, a consistency rule was developed reduce the size of the CAMD problem. The CAMD problem was then further extended to address the economic aspects via fuzzy multi-objective optimisation approach. Next, a rough-set based machine learning (RSML) model has been proposed to correlate the feedstock characterisation and pyrolysis condition with the pyrolysis bio-oil properties by generating decision rules. The generated decision rules were analysed from a scientific standpoint to identify the underlying patterns, while ensuring the rules were logical. The decision rules generated can be used to select optimal feedstock composition and pyrolysis condition to produce pyrolysis bio-oil of targeted fuel properties. Next, the results obtained from the computational approaches were verified through experimental study. The generated pyrolysis bio-oils were blended with the identified solvents at various mixing ratio. In addition, emulsification of the solvent-oil blend in diesel was also conducted with the help of surfactants. Lastly, potential extensions and prospective work for this study have been discuss in the later part of this thesis. To conclude, this thesis presented the combination of computational and experimental approaches in upgrading the fuel properties of pyrolysis bio-oil. As a result, high quality biofuel can be generated as a cleaner burning replacement for conventional diesel fuel
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