887 research outputs found
Discovery of selective saccharide receptors via Dynamic Combinatorial Chemistry
The diagnosis of various diseases and pathological conditions can be accomplished by screening and detecting glycans in cells. Certain glycans serve as excellent biomarkers, being related to cell malfunctioning, while other structurally similar glycans perform completely different functions and are naturally present in healthy cells. Despite the theoretical feasibility of using glycans as biomarkers for early disease detection, our current inability to discriminate between them limits their use.
One promising approach to distinguishing between glycans is targeting their dissimilarities in saccharide chains. However, designing selective receptors for saccharides is challenging due to the complexity of these molecules. Their vast diversity, the fact that they exist in many interconvertible forms, their lack of recognisable functional groups, or the fact that they are normally heavily solvated in aqueous environments have made the design of receptors for saccharides a challenge that has kept the scientific community busy for the last 35 years. Although there have been ground-breaking discoveries in the field, improvements are needed to enhance our disease detection and risk stratification tools.
To address this challenge, we employed a technique known as Dynamic Combinatorial Chemistry (DCC). DCC enables the self-formation and self-selection of the best possible receptor for a given target from a pool or library of potentially good ligands. DCC has been effective for creating receptors for biomolecules such as DNA, RNA, and proteins, but its use for discovering sugar receptors is less explored. In this work, we filled this gap by implementing DCC for screening common saccharides (glucose, galactose, mannose, and fructose) using small, simple, and inexpensive building blocks. Our results indicated that molecule 2DD, which consists of a benzene ring with 2 units of amino acid aspartic acid derivatives connected in positions 1 and 3, is the best receptor in a library of very similar structures for the saccharides glucose, galactose, and mannose. For fructose, molecule 1P, a benzene ring linked to just one unit of the amino acid phenylaldehyde, was appointed as the best receptor. The differential behaviour of fructose can provide insight into the relatively unknown processes behind molecular recognition of sugars.
Molecules 2DD and 1P, as well as some other library members as negative controls, were then synthesised for further testing and DCC results were then validated by Isothermal Titration Calorimetry (ITC) and NMR techniques, proving the effectiveness of DCC as a molecular recognition tool for the creation of receptors for saccharides. Moreover, molecule 1P was found to have a high binding constant (K = 1762 M) and selectivity (50-100 times over other sugars) for fructose, which is surprisingly good considering the simplicity of the receptor.
A much more challenging approach was attempted by employing short peptides as scaffolds in DCC experiments. The benefits of using peptides are numerous but can be summarised in three bullet points: customisability, flexibility, and easiness in their synthesis. Unfortunately, we encountered many difficulties for the complete functionalisation of the peptides within the Dynamic Combinatorial Library (DCL) and this approach did not yield the desired results before the research project came to an end. However, we believe in its potential and the knowledge that we gained on the topic helped to stablish the foundations on which new research will be carried out in the near future within the research group.
In summary, this thesis reports the development of a rapid methodology for the discovery of selective receptors for monosaccharides, employing a library of simple and inexpensive starting building blocks. While this was a proof-of-concept study, it can be scalable to larger library sizes and to target more complex biomolecules, becoming a useful tool that could accelerate the discovery of new molecules with biomedical applications
Combined Nutrition and Exercise Interventions in Community Groups
Diet and physical activity are two key modifiable lifestyle factors that influence health across the lifespan (prevention and management of chronic diseases and reduction of the risk of premature death through several biological mechanisms). Community-based interventions contribute to public health, as they have the potential to reach high population-level impact, through the focus on groups that share a common culture or identity in their natural living environment. While the health benefits of a balanced diet and regular physical activity are commonly studied separately, interventions that combine these two lifestyle factors have the potential to induce greater benefits in community groups rather than strategies focusing only on one or the other. Thus, this Special Issue entitled âCombined Nutrition and Exercise Interventions in Community Groupsâ is comprised of manuscripts that highlight this combined approach (balanced diet and regular physical activity) in community settings. The contributors to this Special Issue are well-recognized professionals in complementary fields such as education, public health, nutrition, and exercise. This Special Issue highlights the latest research regarding combined nutrition and exercise interventions among different community groups and includes research articles developed through five continents (Africa, Asia, America, Europe and Oceania), as well as reviews and systematic reviews
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Compositions and methods related to recombinant antibodies to histone posttranslational modifications
Embodiments concern compositions and methods involving recombinant antibodies to histone post-translational modifications. The invention provides compositions and methods for histone methyltransferase assays. In certain embodiments, the compositions and methods involve a recombinant antibody that binds histone H3 fragment harboring biomarkers such as H3K9me3 mark, H3K4me3 mark, H3K36me3 mark, H3K27me3, H3K9me3 and H3S10phos or a recombinant antibody that binds histone H4 fragment harboring H4K20me3 mark
The circadian rhythm as a temporal frame to detect phase differences in physiological and psychological functions
Human physiological and psychological functions are under control of the circadian clock. The purpose of this study was to analyze the potential effects of circadian modulation on cognitive performances, and to quantify the phase relationships between physiological and psychological functions. For the experiment, 18 Chinese female participants were recruited who were studying in Munich. In an isolated room with constant light conditions the subjects participated in the experiment from 7:00 to 23:00. They gave their subjective evaluations on sleepiness, satiety, and mood; their body temperature, grip strength, hear rate, blood pressure, and salivary cortisol were measured. A cognitive battery was used for objective testing, including a two-flash fusion task, psychomotor vigilance task, a Go/No-go task, a finger tapping task, and a temporal reproduction task. Within the concept of temporal perception, the tested domains were classified into a tens-millisecond to a thousands-millisecond time window. This study showed the significant influence of circadian rhythm on temporal perceptions for all tasks. Clustering different functions suggested that temporal perception is different in the morning and in the afternoon. The diurnal rhythms of biomarkers and subjective and objective functions indicated the regulatory role of the endogenous circadian clock. The different phase relationships of functions provide hints for underlying neural processes.Die physiologischen und psychologischen Funktionen des Menschen stehen unter der Kontrolle der zirkadianen Uhr. Ziel dieser Studie war es, die potenziellen Auswirkungen der zirkadianen Modulation auf kognitive Leistungen zu analysieren, und die Phasenbeziehungen zwischen physiologischen und psychologischen Funktionen zu quantifizieren. FĂŒr das Experiment wurden 18 chinesische Teilnehmerinnen mittleren Alters rekrutiert, die in MĂŒnchen studieren, um in einem isolierten Raum mit konstanter Beleuchtung den tageszeitlichen Verlauf von Funktionen wischen 7:00 und 23:00 Uhr zu messen. Die Teilnehmerinnen gaben unter anderem subjektive Bewertungen zu SchlĂ€frigkeit, SĂ€ttigung und Stimmung ab. AuĂerdem wurden Körpertemperatur, GriffstĂ€rke, Herzfrequenz, Blutdruck und das Speichelcortisol gemessen. Des Weiteren wurde eine kognitive Batterie eingesetzt mit Aufgaben zur PrĂŒfung der zeitlichen Fusion, der psychomotorischen Vigilanz, der zentralen Hemmung mit einer Go/No-Go-Aufgabe, des persönlichen Tempos und der zeitlichen Reproduktion. Im Hinblick auf die zeitliche Wahrnehmung wurden Zeitfenster von zehn bis tausend Millisekunden eingeteilt; dieser Bereich der Studie zeigte den signifikanten Einfluss des zirkadianen Rhythmus auf die zeitliche Wahrnehmung in allen diesen Zeitbereichen. Diese unterschiedlichen Muster deuten darauf hin, dass die zeitliche Wahrnehmung am Morgen und am Nachmittag unterschiedlich ist. Die Tagesrhythmen der Biomarker sowie der subjektiven und objektiven Funktionen zeigen die regulierende Rolle der endogenen circadianen Uhr. Die verschiedenen Phasenbeziehungen der Funktionen erlauben Hinweise auf zu Grunde liegende neuronale Prozesse
The immunobiology of B Lymphocytes in non-small cell lung cancer
Lung cancer is the second most diagnosed cancer, after breast cancer, worldwide. However, it is still the leading cause of cancer-specific mortality globally, contributing to 18% of all cancer-related deaths. Non-small cell lung cancer (NSCLC) makes up 85% of lung cancers and dependent on the stage, is amenable to a wide of treatments from surgery to systemic therapy. Immune responses within the tumour microenvironment have increasingly been implicated as determining factors in tumour progression and aggressiveness, and the focus has predominated on T-cell biology. The immune response is a complex interplay between the primary tumour and microenvironment, T and B cells. The role of the B cell in tumour survival is unclear but clearly has a function as tumour infiltration is commonly reported.
Through deep phenotyping and multispectral tissue imaging techniques, we identified key differences in the effector and suppressive B cell composition between the tumour and peripheral blood compartments. IL10 positive suppressive B regulatory phenotypes were significantly more abundant in the circulation of patients who recurred post-operatively. Using a broad spectrum immunome array, we employed machine learning techniques and identified an auto-antibody signature in the serum of NSCLC patients that was highly predictive for post-operative recurrence in two independent cohorts.
In addition to the techniques described above, we utilised functional ex vivo B cell assays to interrogate the response to checkpoint blockade in advanced disease patients and how this relates to B cell dynamics. Our findings demonstrated that lack of a suppressive B cell âbrakeâ predisposed patients to high grade immune related adverse events post-treatment. Moreover, the B cells from toxicity patients were not only functionally defective in their ability to produce IL10 but also displayed a pan cytokine failure affecting pro-inflammatory cytokines thus suggesting B cell exhaustion in these patients. These findings significantly enhanced our understanding of the aetiopathogenesis of auto-immune toxicity secondary to checkpoint blockade with anti PD-1/PDL-1.
In summary, this study aimed to explore the role of B cell biology in NSCLC by employing deep phenotyping and functional assay techniques at the blood and tissue level in both early and advanced stage disease. Our findings are likely to be informative in biomarker development for predicting response to treatment, post-operative relapse and for therapeutic adjuvant polyepitopic vaccine strategies in high-risk patients
Genetic architecture of glycomic and lipidomic phenotypes in isolated populations
Understanding how genetics contributes to the variation of complex traits and diseases is one of the key objectives of current medical studies. To date, a large portion of this genetic variation still needs to be identified, especially considering the contribution of low-frequency and rare variants. Omics data, such as proteomics and metabolomics, are extensively employed in genetic association studies as âproxiesâ for traits or diseases of interest. They are regarded as âintermediateâ traits: measurable manifestations of more complex phenotypes (e.g., cholesterol levels for cardiovascular diseases), often more strongly associated with genetic variation and having a clearer functional link than the endpoint or disease of interest. Accordingly, the genetics of omics have the potential to offer insights into relevant biological mechanisms and pathways and point to new drug targets or diagnostic biomarkers. The main goal of this thesis is to expand the current knowledge about the genetic architecture of protein glycomics and bile acid lipidomics, two under-studied omic traits, but which are involved in several common diseases.
First, in Chapter 2 I compared genetic regulation of glycosylation of two different proteins, transferrin and immunoglobulin G (IgG). By performing a genome-wide association study (GWAS) of ~2000 European samples, I identified 10 loci significantly associated with transferrin glycosylation, 9 of which were previously not reported as being related with the glycosylation of this protein. Comparing these with IgG glycosylation-associated genes, I noted both protein-specific and shared associations. These shared associations are likely regulated by different causal variants, suggesting that glycosylation of transferrin and IgG is genetically regulated by both shared and protein-specific mechanisms. Next, in Chapter 3 I investigated the effect of rare (MAF<5%) predicted loss-of-function (pLOF) and missense variants on the glycome of transferrin and IgG in ~3000 samples of European ancestry. Using multiple gene-based aggregation tests, I identified 16 significant gene-based associations for transferrin and 32 for IgG glycan traits,located in 6 genes already known to have a biological link to protein glycosylation but also in 2 genes which have not been previously reported.
Finally, in Chapter 4 I applied a similar approach to bile acid lipidomics, exploring the genetic contribution of both common and rare variants. Despite more than double the sample size (N = ~5000) compared to protein glycomics analysis, I identified only 2 loci, near the SLCO1B1 and PRKG1 genes, significantly associated with bile acid traits., for which I noted a sex-specific effect. Further, I found 3 rare variant gene-based associations, in genes not previously reported as associated with bile acid levels. While the biological mechanisms linking these genes to levels of bile acid is not immediately clear, there is evidence in the literature of their involvement in bile acid synthesis and secretion and in liver diseases. In summary, in my thesis I describe the genetic architecture of the protein glycome and the bile acid lipidome: the former has a higher genetic component, while the latter is largely influenced by environmental factors (e.g., sex, diet, gut flora). Despite the limited sample size, we were able to describe rare variant associations, demonstrating that isolated populations represent a useful strategy to increase statistical power. However, additional statistical power is needed to identify the possible effect of protein glycome and bile acid lipidome on complex disease. A clearer understanding of the genetic architecture of omics traits is crucial to develop informed disease screening tests, to improve disease diagnosis and prognosis, and finally to design innovative and more customised treatment strategies to enhance human health
A scalable formulation of joint modelling for longitudinal and time to event data and its application on large electronic health record data of diabetes complications
INTRODUCTION:
Clinical decision-making in the management of diabetes and other chronic diseases depends upon individualised risk predictions of progression of the disease or complica- tions of disease. With sequential measurements of biomarkers, it should be possible to make dynamic predictions that are updated as new data arrive. Since the 1990s, methods have been developed to jointly model longitudinal measurements of biomarkers and time-to-event data, aiming to facilitate predictions in various fields.
These methods offer a comprehensive approach to analyse both the longitudinal changes in biomarkers, and the occurrence of events, allowing for a more integrated understanding of the underlying processes and improved predictive capabilities. The aim of this thesis is to investigate whether established methods for joint modelling are able to scale to large-scale electronic health record datasets with multiple biomarkers measured asynchronously, and evaluates the performance of a novel approach that overcomes the limitations of existing methods.
METHODS:
The epidemiological study design utilised in this research is a retrospective observa- tional study. The data used for these analyses were obtained from a registry encompassing all individuals with type 1 diabetes in Scotland, which is delivered by the Scottish Care Information - Diabetes Collaboration platform. The two outcomes studied were time to cardiovascular disease (CVD) and time to end-stage renal disease (ESRD) from T1D diag- nosis. The longitudinal biomarkers examined in the study were glycosylated haemoglobin (HbA1c) and estimated glomerular filtration rate (eGFR). These biomarkers and endpoints were selected based on their prevalence in the T1D population and the established association between these biomarkers and the outcomes.
As a state-of-the-art method for joint modelling, Brillemanâs stan_jm() function was evaluated. This is an implementation of a shared parameter joint model for longitudinal and time-to- event data in Stan contributed to the rstanarm package. This was compared with a novel approach based on sequential Bayesian updating of a continuous-time state-space model for the biomarkers, with predictions generated by a Kalman filter algorithm using the ctsem package fed into a Poisson time-splitting regression model for the events. In contrast to the standard joint modelling approach that can only fit a linear mixed model to the biomarkers, the ctsem package is able to fit a broader family of models that include terms for autoregressive drift and diffusion. As a baseline for comparison, a last-observation-carried-forward model was evaluated to predict time-to-event.
RESULTS:
The analyses were conducted using renal replacement therapy outcome data regarding 29764 individuals and cardiovascular disease outcome data on 29479 individuals in Scotland (as per the 2019 national registry extract). The CVD dataset was reduced to 24779 individuals with both HbA1c and eGFR data measured on the same date; a limitation of the modelling function itself. The datasets include 799 events of renal replacement therapy (RRT) or death due to renal failure (6.71 years average follow-up) and 2274 CVD events (7.54 years average follow-up) respectively. The standard approach to joint modelling using quadrature to integrate over the trajectories of the latent biomarker states, implemented in rstanarm, was found to be too slow to use even with moderate-sized datasets, e.g. 17.5 hours for a subset
of 2633 subjects, 35.9 hours for 5265 subjects, and more than 68 hours for 10532 subjects. The sequential Bayesian updating approach was much faster, as it was able to analyse a dataset of 29121 individuals over 225598.3 person-years in 19 hours. Comparison of the fit of different longitudinal biomarker submodels showed that the fit of models that also included a drift and diffusion term was much better (AIC 51139 deviance units lower) than models that included only a linear mixed model slope term. Despite this, the improvement in predictive performance was slight for CVD (C-statistic 0.680 to 0.696 for 2112 individuals) and only moderate for end-stage renal disease (C-statistic 0.88 to 0.91 for 2000 individuals) by adding terms for diffusion and drift. The predictive performance of joint modelling in these datasets was only slightly better than using last-observation-carried-forward in the Poisson regression model (C-statistic 0.819 over 8625 person-years).
CONCLUSIONS:
I have demonstrated that unlike the standard approach to joint modelling, implemented in rstanarm, the time-splitting joint modelling approach based on sequential Bayesian updating can scale to a large dataset and allows biomarker trajectories to be modelled with a wider family of models that have better fit than simple linear mixed models. However, in this application, where the only biomarkers were HbA1c and eGFR, and the outcomes were time-to-CVD and end-stage renal disease, the increment in the predictive performance of joint modelling compared with last-observation-carried forward was slight. For other outcomes, where the ability to predict time-to-event depends upon modelling latent biomarker trajectories rather than just using the last-observation-carried-forward, the advantages of joint modelling may be greater.
This thesis proceeds as follows. The first two chapters serve as an introduction to the joint modelling of longitudinal and time-to-event data and its relation to other methods for clinical risk prediction. Briefly, this part explores the rationale for utilising such an approach to manage chronic diseases, such as T1D, better. The methodological chapters of this thesis describe the mathematical formulation of a multivariate shared-parameter joint model and introduce its application and performance on a subset of individuals with T1D and data pertaining to CVD and ESRD outcomes.
Additionally, the mathematical formulation of an alternative time-splitting approach is demonstrated and compared to a conventional method for estimating longitudinal trajectories of clinical biomarkers used in risk prediction. Also, the key features of the pipeline required to implement this approach are outlined. The final chapters of the thesis present an applied example that demonstrates the estimation and evaluation of the alternative modelling approach and explores the types of inferences that can be obtained for a subset of individuals with T1D that might progress to ESRD. Finally, this thesis highlights the strengths and weaknesses of applying and scaling up more complex modelling approaches to facilitate dynamic risk prediction for precision medicine
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