324139 research outputs found
Sort by
July 13 in Kashmir: on federalised historiography and the silent challenge to federalism
Get PDFConstitutional debates on federalism in India have for long treated it as a matter of power-sharing between the states and the centre when in fact it also bears on questions such as self-conception of federal units, constitutional identity, and history
Biochemical investigations using mass spectrometry to monitor JMJD6-catalysed hydroxylation of multi-lysine containing bromodomain-derived substrates †
Get PDFJumonji-C domain-containing protein 6 (JMJD6) is a human 2-oxoglutarate (2OG)/Fe(ii)-dependent oxygenase catalysing post-translational C5 hydroxylation of multiple lysine residues, including in the bromodomain-containing proteins BRD2, BRD3 and BRD4. The role(s) of JMJD6-catalysed substrate hydroxylation are unclear. JMJD6 is important in development and JMJD6 catalysis may promote cancer. We report solid-phase extraction coupled to mass spectrometry assays monitoring JMJD6-catalysed hydroxylation of BRD2–4 derived oligopeptides containing multiple lysyl residues. The assays enabled determination of apparent steady-state kinetic parameters for 2OG, Fe(ii), l-ascorbate, O2 and BRD substrates. The JMJD6 Kappm for O2 was comparable to that reported for the structurally related 2OG oxygenase factor inhibiting hypoxia-inducible factor-α (FIH), suggesting potential for limitation of JMJD6 activity by O2 availability in cells, as proposed for FIH and some other 2OG oxygenases. The new assays will help development of small-molecule JMJD6 inhibitors for functional assignment studies and as potential cancer therapeutics
DriverOmicsNet: an integrated graph convolutional network for multi-omics exploration of cancer driver genes
Get PDFCancer is a complex and heterogeneous group of diseases driven by genetic mutations and molecular changes. Identifying and characterizing cancer driver gene is crucial for understanding cancer biology and guiding precision oncology. Integrating multi-omics data can reveal the intricate molecular interactions underlying cancer progression and treatment responses. We developed a graph convolutional network (GCN) framework, DriverOmicsNet, that integrates multi-omics data using STRING protein–protein interaction networks and correlation-based weighted gene correlation network analysis (WGCNA). We applied this framework to 15 cancer types, analyzing 5555 tumor samples to predict cancer-related features such as homologous recombination deficiency, cancer stemness, immune clusters, tumor stage, and survival outcomes. DriverOmicsNet demonstrated superior predictive accuracy and model performance metrics across all target labels when compared with GCN models based on STRING network alone. Gene expression emerged as the most significant feature, reflecting the dynamic and functional state of cancer cells. The combined use of STRING PPI and WGCNA networks enhanced the identification of key driver genes and their interactions. Our study highlights the effectiveness of using GCNs to integrate multi-omics data for precision oncology. The integration of STRING PPI and WGCNA networks provides a comprehensive framework that improves predictive power and facilitates the understanding of cancer biology, paving the way for more tailored treatments
Measuring growth, resistance, and recovery after artemisinin treatment of Plasmodium falciparum in a single semi-high-throughput assay
Get PDFBackground: Artemisinin partial resistance (ART-R) has spread throughout Southeast Asia and mutations in Pfkelch13, the molecular marker of resistance, are widely reported in East Africa. Effective in vitro assays and robust phenotypes are crucial for monitoring populations for the emergence and spread of resistance. The recently developed extended Recovery Ring-stage Survival Assay used a qPCR-based readout to reduce the labour intensiveness for in vitro phenotyping of ART-R and improved correlation with the clinical phenotype of ART-R. Here, the assay is extended and refined to include measurements of parasite growth and recovery after drug exposure. Clinical isolates and progeny from two genetic crosses were used to optimize and validate the reliability of a straight-from-blood, SYBR Green-based qPCR protocol in a 96-well plate format to accurately measure phenotypes with this new Growth, Resistance, and Recovery assay (GRRA). Results: The assay determined growth between 6 and 96 h, resistance at 120 h, and recovery from 120 to 192 h. Growth can be accurately captured by qPCR and is shown by reproduction of previous growth phenotypes from HB3 × Dd2. Resistance measured at 120 h continually shows the most consistent phenotype for ring stage susceptibility. Recovery identifies an additional response to drug in parasites that are determined sensitive by replicative viability at 120 h. Comparison of progeny phenotypes for Growth versus Resistance showed a minor but significant correlation, whereas Growth versus Recovery and Resistance versus Recovery showed no significant correlation. Additionally, dried blood spot (DBS) samples matched replicative viability measured from liquid samples demonstrating Resistance can be easily quantified using either storage method. Conclusions: The direct-from-blood qPCR-based methodology provides the throughput needed to quickly measure large numbers of parasites for multiple relevant phenotypes. Growth can reveal fitness defects and illuminate relationships between proliferation rates and drug response. Recovery serves as a complementary phenotype to resistance that quantifies the ability of sensitive parasites to tolerate drug exposure. All three phenotypes offer a comprehensive assessment of parasite-drug interaction each with potential independent genetic determinants of main effect and overlapping secondary effects. By adapting the method to include DBS, readouts can be easily extended to ex vivo surveillance applications
The Global Neurodegeneration Proteomics Consortium: biomarker and drug target discovery for common neurodegenerative diseases and aging
Get PDFMore than 57 million people globally suffer from neurodegenerative diseases, a figure expected to double every 20 years. Despite this growing burden, there are currently no cures, and treatment options remain limited due to disease heterogeneity, prolonged preclinical and prodromal phases, poor understanding of disease mechanisms, and diagnostic challenges. Identifying novel biomarkers is crucial for improving early detection, prognosis, staging and subtyping of these conditions. High-dimensional molecular studies in biofluids (‘omics’) offer promise for scalable biomarker discovery, but challenges in assembling large, diverse datasets hinder progress. To address this, the Global Neurodegeneration Proteomics Consortium (GNPC)—a public–private partnership—established one of the world’s largest harmonized proteomic datasets. It includes approximately 250 million unique protein measurements from multiple platforms from more than 35,000 biofluid samples (plasma, serum and cerebrospinal fluid) contributed by 23 partners, alongside associated clinical data spanning Alzheimer’s disease (AD), Parkinson’s disease (PD), frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS). This dataset is accessible to GNPC members via the Alzheimer’s Disease Data Initiative’s AD Workbench, a secure cloud-based environment, and will be available to the wider research community on 15 July 2025. Here we present summary analyses of the plasma proteome revealing disease-specific differential protein abundance and transdiagnostic proteomic signatures of clinical severity. Furthermore, we describe a robust plasma proteomic signature of APOE ε4 carriership, reproducible across AD, PD, FTD and ALS, as well as distinct patterns of organ aging across these conditions. This work demonstrates the power of international collaboration, data sharing and open science to accelerate discovery in neurodegeneration research
Design and nonviral delivery of live attenuated vaccine to prevent chronic hepatitis C virus-like infection
Get PDFAn effective vaccine for the hepatitis C virus (HCV) remains an unmet medical need. There is no animal model for assessing HCV vaccines; however, rodent hepacivirus (RHV) infection in laboratory rats recapitulates the lifelong chronic hepatotropic infection and immune evasion of HCV. Here, we designed a live-attenuated vaccine (LAV) for RHV and determined its immunogenicity and efficacy for preventing chronic infection. The LAV strains are generated by synonymous mutagenesis to increase the frequencies of naturally suppressed dinucleotides, UpA or CpG, in genomic regions that lack extensive RNA secondary structures. Rats vaccinated using LAV containing infectious virions (LAV-IV), or lipid nanoparticle-encapsulated viral RNA (LNP-vRNA) developed short-term viremia and robust T cell responses. After challenge with RHV-rn1, while all unvaccinated rats developed chronic infection, 75% and 85% of rats vaccinated with LAV-IV and LAV-vRNA cleared the infection. Clearance of RHV-rn1 was associated with expansion of memory T cells, transient rise in serum ALT, and, more importantly, enhanced protection against reinfection. In conclusion, we identified a genomic region of hepacivirus that can be synonymously mutated to attenuate its persistence, and vaccines based on these modified genomes protect against chronic hepacivirus infection, a strategy with an apparent translational path toward HCV immunization
Success of EMI in higher education and its key components: a meta-analytic structural equation modelling approach
Get PDFThe increasing global demand for English Medium Instruction (EMI) in higher education (HE) highlights the need for empirical research to contribute to its success and suggest ways of mitigating the diverse challenges faced by students and institutions. Identifying the key factors that contribute to successful EMI is critical for improving both content and language learning outcomes for students. In the wake of the recent surge in EMI research, more research synthesis is needed to understand how these factors interact to influence EMI success rather than focusing solely on their isolated, bivariate relationships. To address this gap, we conducted meta-analytic structural equation modelling (MASEM) to examine the structural relationships among the key factors affecting the success of EMI. Synthesising data from 50 studies (N = 15,032), our analysis demonstrates that learners’ engagement and English proficiency are pivotal for both content and language learning, with English proficiency being more important for language learning outcomes, although itself considerably also influenced by learners’ anxiety and motivation. Additionally, the moderator analysis shows that learner engagement plays a more significant role in partial EMI contexts, leading to our recommendation for differentiated institutional support in full and partial EMI settings. However, owing to the limited availability of correlation coefficients, some key factors and moderators were excluded, prompting the need for further empirical research to explore these relationships in greater depth
Mechanical behaviour of cohesionless soils: Implications for offshore foundation design
No full textThe design of offshore wind turbine (OWT) foundations necessitates a thorough understanding of the mechanical behaviour of its founding soil. The soil’s stiffness, G0 and stiffness degradation, G/G0 are key factors influencing the foundation’s resistance to cyclic loading. This thesis undertakes a parametric study of G0, and the G/G0 complemented by an experimental study using the Resonant Column Apparatus (RCA). Many empirical models predicting G0 and G/G0 for cohesionless soils rely on the coefficient of uniformity, Cu. However, Cu can be altered by changing the average diameter, d50, fines content F.C and gravel content G.C which have opposing impacts on G0 and G/G0 making sole reliance on Cu unreliable. Experimentally, it is shown that within the elastic region, G/G0 varies minimally across all investigated parameters.
The cyclic behaviour and underlying physical mechanisms are explored through extensive tests in the Variable Dynamic Direct Simple Shear (VDDCSS) apparatus. Specifically key features such as densification, generation of porewater pressure, accumulated ratcheting and changes to stiffness, energy dissipation and post-cyclic strength are explored. Drainage condition notably influences these behaviours, with cyclic amplitude, loading symmetry, and relative density also playing significant roles.
The experimental findings offer theoretical insights that inform OWT foundation design. A numerical study on a reference turbine reveals that realistic variations in G0 have minimal impact on the system’s natural frequency, however, can significantly influence OWT fatigue performance depending on pile diameter. This underscores the complexity of fatigue analysis, where stiffness and fatigue are not directly linked, necessitating a careful design approach. Finally, comparing experimental, model, and field tests on piles highlights potential links between soil behaviour at the elemental and field scales, with similar mechanisms governing both
Assembly and intracellular delivery methods for nucleic acid nanotechnology
Get PDFThe field of nucleic acid nanotechnology has mainly taken DNA and RNA out of their biologicalvcontext and uses their properties to revolutionise the control of molecular self-assembly; but can we bring this technology back into the cell? Since their inception, nucleic acid nanostructures have developed into almost any shape imaginable, but one application area for these constructs that has not been explored as much — despite the advantages in size, biocompatibility and the programmability of these constructs— is at the interface with biology. Challenges preventing the breakthrough of this field into more intracellular applications involve improving the cellular uptake of the assembled constructs and, once inside the cell, protecting these nanostructures from nuclease degradation or unfolding. Overcoming these issues with new intracellular delivery methods or self-assembly designs that allow for de novo construction within the cell would open up a plethora of applications in diagnostics, therapeutics, drug delivery, as well as providing programmable tools for studying molecular biology within the cell itself.
This thesis investigates three approaches for designing intracellular nucleic acid nanostructures. The first strategy presented is a new isothermal single-stranded assembly method, inspired by knitting, for designing nucleic acid nanostructures inside the cell. I show the characterisation of both a single-knit and multi-knit structure, through both computational modelling as well as experimental analysis. The resulting knitted constructs were primarily focused on in vitro replicable DNA systems to allow for ease in characterisation, although I also delve into the concept as a co-transcriptionally folded RNA method. Using atomic force microscopy, I was able to visualise the rolling circle amplified multi-knitted constructs. Taking inspiration from knitting, this assembly technique has the potential to be used for further complex designs and patterns. I then explore alternative approaches towards bringing nucleic acid nanostructures into the cell. This includes the use of confinement as a new method for delivering asymmetrical DNA origami cryoelectron tomography tags into mammalian cells through the creation of transient pores in the membrane to allow for the cellular uptake and targeting of the DNA nanostructures. Finally, I demonstrate three variations of bistable DNA hairpins to show reconfiguration kinetics that could be used in the design of future intracellular nucleic acid nanostructures. With these three projects, I was able to demonstrate a variety of in vivo implementation of DNA nanostructures, which can inspire future exploration of this promising field of intracellular nucleic acid nanostructures