226 results found
Hoyles L, Pontifex MG, Rodriguez-Ramiro I, et al., 2021, Regulation of blood brain barrier integrity by microbiome-associated methylamines and cognition by trimethylamine N-oxide, Microbiome, Vol: 9, Pages: 1-21, ISSN: 2049-2618
BackgroundCommunication between the gut microbiota and the brain is primarily mediated via soluble microbe-derived metabolites, but the details of this pathway remain poorly defined. Methylamines produced by microbial metabolism of dietary choline and L-carnitine have received attention due to their proposed association with vascular disease, but their effects upon the cerebrovascular circulation have hitherto not been studied.ResultsHere, we use an integrated in vitro/in vivo approach to show that physiologically relevant concentrations of the dietary methylamine trimethylamine N-oxide (TMAO) enhanced blood-brain barrier (BBB) integrity and protected it from inflammatory insult, acting through the tight junction regulator annexin A1. In contrast, the TMAO precursor trimethylamine (TMA) impaired BBB function and disrupted tight junction integrity. Moreover, we show that long-term exposure to TMAO protects murine cognitive function from inflammatory challenge, acting to limit astrocyte and microglial reactivity in a brain region-specific manner.ConclusionOur findings demonstrate the mechanisms through which microbiome-associated methylamines directly interact with the mammalian BBB, with consequences for cerebrovascular and cognitive function.
Iacovacci J, Lin W, Griffin JL, et al., 2021, IonFlow: a galaxy tool for the analysis of ionomics data sets, Metabolomics, Vol: 17, Pages: 1-12, ISSN: 1573-3882
IntroductionInductively coupled plasma mass spectrometry (ICP-MS) experiments generate complex multi-dimensional data sets that require specialist data analysis tools.ObjectiveHere we describe tools to facilitate analysis of the ionome composed of high-throughput elemental profiling data.MethodsIonFlow is a Galaxy tool written in R for ionomics data analysis and is freely accessible at https://github.com/wanchanglin/ionflow. It is designed as a pipeline that can process raw data to enable exploration and interpretation using multivariate statistical techniques and network-based algorithms, including principal components analysis, hierarchical clustering, relevance network extraction and analysis, and gene set enrichment analysis.Results and ConclusionThe pipeline is described and tested on two benchmark data sets of the haploid S. Cerevisiae ionome and of the human HeLa cell ionome.
Ingwersen T, Linnenberg C, D'Acunto E, et al., 2021, G392E neuroserpin causing the dementia FENIB is secreted from cells but is not synaptotoxic, Scientific Reports, Vol: 11, Pages: 1-13, ISSN: 2045-2322
Familial encephalopathy with neuroserpin inclusion bodies (FENIB) is a progressive neurodegenerative disease caused by point mutations in the gene for neuroserpin, a serine protease inhibitor of the nervous system. Different mutations are known that are responsible for mutant neuroserpin polymerization and accumulation as inclusion bodies in many cortical and subcortical neurons, thereby leading to cell death, dementia and epilepsy. Many efforts have been undertaken to elucidate the molecular pathways responsible for neuronal death. Most investigations have concentrated on analysis of intracellular mechanisms such as endoplasmic reticulum (ER) stress, ER-associated protein degradation (ERAD) and oxidative stress. We have generated a HEK-293 cell model of FENIB by overexpressing G392E-mutant neuroserpin and in this study we examine trafficking and toxicity of this polymerogenic variant. We observed that a small fraction of mutant neuroserpin is secreted via the ER-to-Golgi pathway, and that this release can be pharmacologically regulated. Overexpression of the mutant form of neuroserpin did not stimulate cell death in the HEK-293 cell model. Finally, when treating primary hippocampal neurons with G392E neuroserpin polymers, we did not detect cytotoxicity or synaptotoxicity. Altogether, we report here that a polymerogenic mutant form of neuroserpin is secreted from cells but is not toxic in the extracellular milieu.
Kalash L, Winfield I, Safitri D, et al., 2021, Structure-based identification of dual ligands at the A(2A)R and PDE10A with anti-proliferative effects in lung cancer cell-lines, Journal of Cheminformatics, Vol: 13, ISSN: 1758-2946
Enhanced/prolonged cAMP signalling has been suggested as a suppressor of cancer proliferation. Interestingly, two key modulators that elevate cAMP, the A2A receptor (A2AR) and phosphodiesterase 10A (PDE10A), are differentially co-expressed in various types of non-small lung cancer (NSCLC) cell-lines. Thus, finding dual-target compounds, which are simultaneously agonists at the A2AR whilst also inhibiting PDE10A, could be a novel anti-proliferative approach. Using ligand- and structure-based modelling combined with MD simulations (which identified Val84 displacement as a novel conformational descriptor of A2AR activation), a series of known PDE10A inhibitors were shown to dock to the orthosteric site of the A2AR. Subsequent in-vitro analysis confirmed that these compounds bind to the A2AR and exhibit dual-activity at both the A2AR and PDE10A. Furthermore, many of the compounds exhibited promising anti-proliferative effects upon NSCLC cell-lines, which directly correlated with the expression of both PDE10A and the A2AR. Thus, we propose a structure-based methodology, which has been validated in in-vitro binding and functional assays, and demonstrated a promising therapeutic value.
Peluso A, Glen R, Ebbels T, 2021, Multiple-testing correction in metabolome-wide association studies, BMC Bioinformatics, Vol: 22, ISSN: 1471-2105
Background:The search for statistically significant relationships between molecular markers and outcomes is challenging when dealing with high-dimensional, noisy and collinear multivariate omics data, such as metabolomic profiles. Permutation procedures allow for the estimation of adjusted significance levels without assuming independence among metabolomic variables. Nevertheless, the complex non-normal structure of metabolic profiles and outcomes may bias the permutation results leading to overly conservative threshold estimates i.e. lower than those from a Bonferroni or Sidak correction.Methods:Within a univariate permutation procedure we employ parametric simulation methods based on the multivariate (log-)Normal distribution to obtain adjusted significance levels which are consistent across different outcomes while effectively controlling the type I error rate. Next, we derive an alternative closed-form expression for the estimation of the number of non-redundant metabolic variates based on the spectral decomposition of their correlation matrix. The performance of the method is tested for different model parametrizations and across a wide range of correlation levels of the variates using synthetic and real data sets.Results:Both the permutation-based formulation and the more practical closed form expression are found to give an effective indication of the number of independent metabolic effects exhibited by the system, while guaranteeing that the derived adjusted threshold is stable across outcome measures with diverse properties.
Read C, Nyimanu D, Yang P, et al., 2021, The G protein biased small molecule apelin agonist CMF-019 is disease modifying in endothelial cell apoptosis In vitro and induces vasodilatation without desensitisation in vivo, Frontiers in Pharmacology, Vol: 11, Pages: 1-11, ISSN: 1663-9812
Signaling through the apelin receptor is beneficial for a number of diseases including pulmonary arterial hypertension. The endogenous small peptides, apelin and elabela/toddler, are downregulated in pulmonary arterial hypertension but are not suitable for exogenous administration owing to a lack of bioavailability, proteolytic instability and susceptibility to renal clearance. CMF-019, a small molecule apelin agonist that displays strong bias towards G protein signaling over β-arrestin (∼400 fold), may be more suitable. This study demonstrates that in addition to being a positive inotrope, CMF-019 caused dose-dependent vasodilatation in vivo (50 nmol 4.16 ± 1.18 mmHg, **p < 0.01; 500 nmol 6.62 ± 1.85 mmHg, **p < 0.01), without receptor desensitization. Furthermore, CMF-019 rescues human pulmonary artery endothelial cells from apoptosis induced by tumor necrosis factor α and cycloheximide (5.66 ± 0.97%, **p < 0.01) by approximately 50% of that observable with rhVEGF (11.59 ± 1.85%, **p < 0.01), suggesting it has disease-modifying potential in vitro. CMF-019 displays remarkable bias at the apelin receptor for a small molecule and importantly recapitulates all aspects of the cardiovascular responses to the endogenous ligand, [Pyr1]apelin-13, in vivo. Additionally, it is able to protect human pulmonary artery endothelial cells from apoptosis, suggesting that the beneficial effects observed with apelin agonists extend beyond hemodynamic alleviation and address disease etiology itself. These findings support CMF-019 as a G protein biased small molecule apelin agonist in vitro and in vivo that could form the basis for the design of novel therapeutic agents in chronic diseases, such as, pulmonary arterial hypertension.
Davenport AP, Nyimanu D, Williams TL, et al., 2020, G Protein Biased Peptide Apelin Receptor Agonist Reverses Sugen/hypoxia-induced Pulmonary Hypertension as Effectively as the Endothelin Antagonist, Macitentan, Scientific Sessions of the American-Heart-Association (AHA) on Epidemiology and Prevention/Lifestyle and Cardiometabolic Health, Publisher: LIPPINCOTT WILLIAMS & WILKINS, Pages: E278-E278, ISSN: 0009-7330
Ashrafian H, Sounderajah V, Glen R, et al., 2020, Metabolomics - the stethoscope for the 21st century, Medical Principles and Practice, Vol: 30, Pages: 301-310, ISSN: 1011-7571
Metabolomics offers systematic identification and quantification of all metabolic products from the human body. This field could provide clinicians with new sets of diagnostic biomarkers for disease states in addition to quantifying treatment response to medications at an individualised level. This literature review aims to highlight the technology underpinning metabolic profiling, identify potential applications of metabolomics in clinical practice and discuss the translational challenges that the field faces. We searched PubMed, Medline and Embase for primary and secondary research articles regarding clinical applications of metabolomics. Metabolic profiling can be performed using mass spectrometry and NMR based techniques using a variety of biological samples. This is carried out in vivo or in vitro following careful sample collection, preparation and analysis. The potential clinical applications constitute disruptive innovations in their respective specialities, particularly oncology and metabolic medicine. Outstanding issues currently preventing widespread clinical use centre around scalability of data interpretation, standardisation of sample handling practice and e-infrastructure. Routine utilisation of metabolomics at a patient and population level will constitute an integral part of future healthcare provision.
Iacovacci J, Peluso A, Ebbels T, et al., 2020, Extraction and integration of genetic networks from short-profile omic data sets, Metabolites, Vol: 10, ISSN: 2218-1989
Mass spectrometry technologies are widely used in the fields of ionomics and metabolomics to simultaneously profile the intracellular concentrations of, e.g., amino acids or elements in genome-wide mutant libraries. These molecular or sub-molecular features are generally non-Gaussian and their covariance reveals patterns of correlations that reflect the system nature of the cell biochemistry and biology. Here, we introduce two similarity measures, the Mahalanobis cosine and the hybrid Mahalanobis cosine, that enforce information from the empirical covariance matrix of omics data from high-throughput screening and that can be used to quantify similarities between the profiled features of different mutants. We evaluate the performance of these similarity measures in the task of inferring and integrating genetic networks from short-profile ionomics/metabolomics data through an analysis of experimental data sets related to the ionome and the metabolome of the model organism S. cerevisiae. The study of the resulting ionome–metabolome Saccharomyces cerevisiae multilayer genetic network, which encodes multiple omic-specific levels of correlations between genes, shows that the proposed measures can provide an alternative description of relations between biological processes when compared to the commonly used Pearson’s correlation coefficient and have the potential to guide the construction of novel hypotheses on the function of uncharacterised genes
Liu K-D, Acharjee A, Hinz C, et al., 2020, Consequences of lipid remodeling of adipocyte membranes being functionally distinct from lipid storage in obesity., Journal of Proteome Research, Vol: 19, Pages: 3919-3935, ISSN: 1535-3893
Obesity is a complex disorder where the genome interacts with diet and environmental factors to ultimately influence body mass, composition, and shape. Numerous studies have investigated how bulk lipid metabolism of adipose tissue changes with obesity and, in particular, how the composition of triglycerides (TGs) changes with increased adipocyte expansion. However, reflecting the analytical challenge posed by examining non-TG lipids in extracts dominated by TGs, the glycerophospholipid composition of cell membranes has been seldom investigated. Phospholipids (PLs) contribute to a variety of cellular processes including maintaining organelle functionality, providing an optimized environment for membrane-associated proteins, and acting as pools for metabolites (e.g. choline for one-carbon metabolism and for methylation of DNA). We have conducted a comprehensive lipidomic study of white adipose tissue in mice which become obese either through genetic modification (ob/ob), diet (high fat diet), or a combination of the two, using both solid phase extraction and ion mobility to increase coverage of the lipidome. Composition changes in seven classes of lipids (free fatty acids, diglycerides, TGs, phosphatidylcholines, lyso-phosphatidylcholines, phosphatidylethanolamines, and phosphatidylserines) correlated with perturbations in one-carbon metabolism and transcriptional changes in adipose tissue. We demonstrate that changes in TGs that dominate the overall lipid composition of white adipose tissue are distinct from diet-induced alterations of PLs, the predominant components of the cell membranes. PLs correlate better with transcriptional and one-carbon metabolism changes within the cell, suggesting that the compositional changes that occur in cell membranes during adipocyte expansion have far-reaching functional consequences. Data are available at MetaboLights under the submission number: MTBLS1775.
Robinson MC, Glen RC, Lee AA, 2020, Validating the validation: reanalyzing a large-scale comparison of deep learning and machine learning models for bioactivity prediction, Journal of Computer-Aided Molecular Design, Vol: 34, Pages: 717-730, ISSN: 0920-654X
Machine learning methods may have the potential to significantly accelerate drug discovery. However, the increasing rate of new methodological approaches being published in the literature raises the fundamental question of how models should be benchmarked and validated. We reanalyze the data generated by a recently published large-scale comparison of machine learning models for bioactivity prediction and arrive at a somewhat different conclusion. We show that the performance of support vector machines is competitive with that of deep learning methods. Additionally, using a series of numerical experiments, we question the relevance of area under the receiver operating characteristic curve as a metric in virtual screening. We further suggest that area under the precision-recall curve should be used in conjunction with the receiver operating characteristic curve. Our numerical experiments also highlight challenges in estimating the uncertainty in model performance via scaffold-split nested cross validation.
Koundouros N, Karali E, Tripp A, et al., 2020, Metabolic fingerprinting links oncogenic PIK3CA with enhanced arachidonic acid-derived eicosanoids, Cell, Vol: 181, Pages: 1596-1611.e27, ISSN: 0092-8674
Oncogenic transformation is associated with profound changes in cellular metabolism, but whether tracking these can improve disease stratification or influence therapy decision-making is largely unknown. Using the iKnife to sample the aerosol of cauterized specimens, we demonstrate a new mode of real-time diagnosis, coupling metabolic phenotype to mutant PIK3CA genotype. Oncogenic PIK3CA results in an increase in arachidonic acid and a concomitant overproduction of eicosanoids, acting to promote cell proliferation beyond a cell-autonomous manner. Mechanistically, mutant PIK3CA drives a multimodal signaling network involving mTORC2-PKCζ-mediated activation of the calcium-dependent phospholipase A2 (cPLA2). Notably, inhibiting cPLA2 synergizes with fatty acid-free diet to restore immunogenicity and selectively reduce mutant PIK3CA-induced tumorigenicity. Besides highlighting the potential for metabolic phenotyping in stratified medicine, this study reveals an important role for activated PI3K signaling in regulating arachidonic acid metabolism, uncovering a targetable metabolic vulnerability that largely depends on dietary fat restriction.
Read C, Yang P, Kuc RE, et al., 2020, Apelin peptides linked to anti-serum albumin domain antibodies retain affinity in vitro and are efficacious receptor agonists in vivo, Basic and Clinical Pharmacology and Toxicology, Vol: 126, Pages: 96-103, ISSN: 1742-7843
The apelin receptor is a potential target in the treatment of heart failure and pulmonary arterial hypertension where levels of endogenous apelin peptides are reduced but significant receptor levels remain. Our aim was to characterise the pharmacology of a modified peptide agonist, MM202, designed to have high affinity for the apelin receptor and resistance to peptidase degradation and linked to an anti-serum albumin domain antibody (AlbudAb) to extend half-life in the blood. In competition binding experiments in human heart MM202-AlbudAb (pKi =9.39±0.09) bound with similar high affinity as the endogenous peptides [Pyr1 ]apelin-13 (pKi =8.83±0.06) and apelin-17 (pKi =9.57±0.08). [Pyr1 ]apelin-13 was 10-fold more potent in the cAMP (pD2 =9.52±0.05) compared to the β-arrestin (pD2 =8.53±0.03) assay, whereas apelin-17 (pD2 =10.31±0.28; pD2 =10.15±0.13, respectively) and MM202-AlbudAb (pD2 =9.15±0.12; pD2 =9.26±0.03, respectively) were equipotent in both assays, with MM202-AlbudAb 10-fold less potent than apelin-17. MM202-AlbudAb bound to immobilised human serum albumin with high affinity (pKD =9.02). In anaesthetised, male Sprague-Dawley rats, MM202-AlbudAb (5nmol, n=15) significantly reduced left ventricular systolic pressure by 6.61±1.46mmHg and systolic arterial pressure by 14.12±3.35mmHg and significantly increased cardiac contractility by 533±170mmHg/s, cardiac output by 1277±190RVU/min, stroke volume by 3.09±0.47RVU and heart rate by 4.64±2.24BPM. This study demonstrates that conjugating an apelin mimetic peptide to the AlbudAb structure retains receptor and in vivo activity and may be a new strategy for development of apelin peptides as therapeutic agents.
Sands C, Wolfer A, DS Correia G, et al., 2019, The nPYc-Toolbox, a Python module for the pre-processing, quality-control, and analysis of metabolic profiling datasets, Bioinformatics, Vol: 35, Pages: 5359-5360, ISSN: 1367-4803
Summary: As large-scale metabolic phenotyping studies become increasingly common, the need forsystemic methods for pre-processing and quality control (QC) of analytical data prior to statistical analysishas become increasingly important, both within a study, and to allow meaningful inter-study comparisons.The nPYc-Toolbox provides software for the import, pre-processing, QC, and visualisation of metabolicphenotyping datasets, either interactively, or in automated pipelines.Availability and Implementation: The nPYc-Toolbox is implemented in Python, and is freelyavailable from the Python package index https://pypi.org/project/nPYc/, source isavailable at https://github.com/phenomecentre/nPYc-Toolbox. Full documentation canbe found at http://npyc-toolbox.readthedocs.io/ and exemplar datasets and tutorials athttps://github.com/phenomecentre/nPYc-toolbox-tutorials
Read C, Nyimanu D, Williams TL, et al., 2019, International union of basic and clinical pharmacology. CVII. structure and pharmacology of the apelin receptor with a recommendation that elabela/toddler is a second endogenous peptide ligand, Pharmacological Reviews, Vol: 71, Pages: 467-502, ISSN: 0031-6997
The predicted protein encoded by the APJ gene discovered in 1993 was originally classified as a class A G protein-coupled orphan receptor but was subsequently paired with a novel peptide ligand, apelin-36 in 1998. Substantial research identified a family of shorter peptides activating the apelin receptor, including apelin-17, apelin-13, and [Pyr1]apelin-13, with the latter peptide predominating in human plasma and cardiovascular system. A range of pharmacological tools have been developed, including radiolabeled ligands, analogs with improved plasma stability, peptides, and small molecules including biased agonists and antagonists, leading to the recommendation that the APJ gene be renamed APLNR and encode the apelin receptor protein. Recently, a second endogenous ligand has been identified and called Elabela/Toddler, a 54-amino acid peptide originally identified in the genomes of fish and humans but misclassified as noncoding. This precursor is also able to be cleaved to shorter sequences (32, 21, and 11 amino acids), and all are able to activate the apelin receptor and are blocked by apelin receptor antagonists. This review summarizes the pharmacology of these ligands and the apelin receptor, highlights the emerging physiologic and pathophysiological roles in a number of diseases, and recommends that Elabela/Toddler is a second endogenous peptide ligand of the apelin receptor protein.
Tong Z, Guo J, Glen RC, et al., 2019, A Bone Morphogenetic Protein (BMP)-derived peptide based on the Type I receptor-binding site modifies cell-type dependent BMP signalling, Scientific Reports, Vol: 9, Pages: 1-9, ISSN: 2045-2322
Bone morphogenetic proteins (BMPs) are multifunctional cytokines of the transforming growth factor β (TGFβ) superfamily with potential therapeutic applications due to their broad biological functionality. Designing BMP mimetics with specific activity will contribute to the translational potential of BMP-based therapies. Here, we report a BMP9 peptide mimetic, P3, designed from the type I receptor binding site, which showed millimolar binding affinities for the type I receptor activin receptor like kinase 1 (ALK1), ALK2 and ALK3. Although showing no baseline activity, P3 significantly enhanced BMP9-induced Smad1/5 phosphorylation as well as ID1, BMPR2, HEY1 and HEY2 gene expression in pulmonary artery endothelial cells (hPAECs), and this activity is dependent on its alpha helix propensity. However, in human dermal microvascular endothelial cells, P3 did not affect BMP9-induced Smad1/5 phosphorylation, but potently inhibited ALK3-dependent BMP4-induced Smad1/5 phosphorylation and gene expression. In C2C12 mouse myoblast cells, P3 had no effect on BMP9-induced osteogenic signalling, which is primarily mediated by ALK2. Interestingly, a previously published peptide from the knuckle region of BMP9 was found to inhibit BMP4-induced Smad1/5 phosphorylation. Together, our data identify a BMP9-derived peptide that can selectively enhance ALK1-mediated BMP9 signalling in hPAECs and modulate BMP9 and BMP4 signalling in a cell type-specific manner.
Dube N, Marzinek JK, Glen RC, et al., 2019, The structural basis for membrane assembly of immunoreceptor signalling complexes, Journal of Molecular Modeling, Vol: 25, Pages: 1-14, ISSN: 0948-5023
Immunoreceptors are TM complexes that consist of separate ligand-binding and signal-transducing modules. Mounting evidence suggests that interactions with the local environment may influence the architecture of these TM domains, which assemble via crucial sets of conserved ionisable residues, and also control the peripheral association of immunoreceptor tyrosine-based activation motifs (ITAMs) whose phosphorylation triggers cytoplasmic signalling cascades. We now report a molecular dynamics (MD) simulation study of the archetypal T cell receptor (TCR) and its cluster of differentiation 3 (CD3) signalling partners, along with the analogous DNAX-activation protein of 12 kDa (DAP12)/natural killer group 2C (NKG2C) complex. Based on > 15 μs of explicitly solvated, atomic-resolution sampling, we explore molecular aspects of immunoreceptor complex stability in different functionally relevant states. A novel alchemical approach is used to simulate the cytoplasmic CD3ε tail at different depths within lipid bilayer models, revealing that the conformation and cytoplasmic exposure of ITAMs are highly sensitive to local enrichment by different lipid species and to phosphorylation. Furthermore, simulations of the TCR and DAP12 TM domains in various states of oligomerisation suggest that, during the early stages of assembly, stable membrane insertion is facilitated by the interfacial lipid/solvent environment and/or partial ionisation of charged residues. Collectively, our results indicate that the architecture and mechanisms of signal transduction in immunoreceptor complexes are tightly regulated by interactions with the microenvironment.
Inglese P, Correia G, Pruski P, et al., 2019, Colocalization features for classification of tumors using desorption electrospray ionization mass spectrometry imaging, Analytical Chemistry, Vol: 91, Pages: 6530-6540, ISSN: 0003-2700
Supervised modeling of mass spectrometry imaging (MSI) data is a crucial component for the detection of the distinct molecular characteristics of cancerous tissues. Currently, two types of supervised analyses are mainly used on MSI data: pixel-wise segmentation of sample images and whole-sample-based classification. A large number of mass spectra associated with each MSI sample can represent a challenge for designing models that simultaneously preserve the overall molecular content while capturing valuable information contained in the MSI data. Furthermore, intensity-related batch effects can introduce biases in the statistical models. Here we introduce a method based on ion colocalization features that allows the classification of whole tissue specimens using MSI data, which naturally preserves the spatial information associated the with the mass spectra and is less sensitive to possible batch effects. Finally, we propose data visualization strategies for the inspection of the derived networks, which can be used to assess whether the correlation differences are related to coexpression/suppression or disjoint spatial localization patterns and can suggest hypotheses based on the underlying mechanisms associated with the different classes of analyzed samples.
Yang P, Read C, Kuc RE, et al., 2019, A novel cyclic biased agonist of the apelin receptor, MM07, is disease modifying in the rat monocrotaline model of pulmonary arterial hypertension, British Journal of Pharmacology, Vol: 176, Pages: 1206-1221, ISSN: 0007-1188
Background and PurposeApelin is an endogenous vasodilatory and inotropic peptide that is down‐regulated in human pulmonary arterial hypertension, although the density of the apelin receptor is not significantly attenuated. We hypothesised that a G protein‐biased apelin analogue MM07, which is more stable than the endogenous apelin peptide, may be beneficial in this condition with the advantage of reduced β‐arrestin‐mediated receptor internalisation with chronic use.Experimental ApproachMale Sprague–Dawley rats received either monocrotaline to induce pulmonary arterial hypertension or saline and then daily i.p. injections of either MM07 or saline for 21 days. The extent of disease was assessed by right ventricular catheterisation, cardiac MRI, and histological analysis of the pulmonary vasculature. The effect of MM07 on signalling, proliferation, and apoptosis of human pulmonary artery endothelial cells was investigated.Key ResultsMM07 significantly reduced the elevation of right ventricular systolic pressure and hypertrophy induced by monocrotaline. Monocrotaline‐induced changes in cardiac structure and function, including right ventricular end‐systolic and end‐diastolic volumes, ejection fraction, and left ventricular end‐diastolic volume, were attenuated by MM07. MM07 also significantly reduced monocrotaline‐induced muscularisation of small pulmonary blood vessels. MM07 stimulated endothelial NOS phosphorylation and expression, promoted proliferation, and attenuated apoptosis of human pulmonary arterial endothelial cells in vitro.Conclusion and ImplicationsOur findings suggest that chronic treatment with MM07 is beneficial in this animal model of pulmonary arterial hypertension by addressing disease aetiology. These data support the development of G protein‐biased apelin receptor agonists with improved pharmacokinetic profiles for use in human disease.
Koundouros N, Tripp A, Karali E, et al., 2019, Near real-time stratification of PIK3CA mutant breast cancers using the iKnife, 211th Meeting of the Pathological-Society-of-Great-Britain-and-Ireland, Publisher: Wiley, Pages: S8-S8, ISSN: 0022-3417
Peters K, Bradbury J, Bergmann S, et al., 2019, PhenoMeNal: Processing and analysis of metabolomics data in the cloud, GigaScience, Vol: 8, ISSN: 2047-217X
Background: Metabolomics is the comprehensive study of a multitude of small molecules to gain insight into an organism's metabolism. The research field is dynamic and expanding with applications across biomedical, biotechnological and many other applied biological domains. Its computationally-intensive nature has driven requirements for open data formats, data repositories and data analysis tools. However, the rapid progress has resulted in a mosaic of independent-and sometimes incompatible-analysis methods that are difficult to connect into a useful and complete data analysis solution. Findings: PhenoMeNal (Phenome and Metabolome aNalysis) is an advanced and complete solution to set up Infrastructure-as-a-Service (IaaS) that brings workflow-oriented, interoperable metabolomics data analysis platforms into the cloud. PhenoMeNal seamlessly integrates a wide array of existing open source tools which are tested and packaged as Docker containers through the project's continuous integration process and deployed based on a kubernetes orchestration framework. It also provides a number of standardized, automated and published analysis workflows in the user interfaces Galaxy, Jupyter, Luigi and Pachyderm. Conclusions: PhenoMeNal constitutes a keystone solution in cloud e-infrastructures available for metabolomics. PhenoMeNal is a unique and complete solution for setting up cloud e-infrastructures through easy-to-use web interfaces that can be scaled to any custom public and private cloud environment. By harmonizing and automating software installation and configuration and through ready-to-use scientific workflow user interfaces, PhenoMeNal has succeeded in providing scientists with workflow-driven, reproducible and shareable metabolomics data analysis platforms which are interfaced through standard data formats, representative datasets, versioned, and have been tested for reproducibility and interoperability. The elastic implementation of PhenoMeNal further allows easy adap
Inglese P, Correia G, Takats Z, et al., 2019, SPUTNIK: an R package for filtering of spatially related peaks in mass spectrometry imaging data, Bioinformatics, Vol: 35, Pages: 178-180, ISSN: 1367-4803
Summary: SPUTNIK is an R package consisting of a series of tools to filter mass spectrometry imaging peaks characterized by a noisy or unlikely spatial distribution. SPUTNIK can produce mass spectrometry imaging datasets characterized by a smaller but more informative set of peaks, reduce the complexity of subsequent multi-variate analysis and increase the interpretability of the statistical results. Availability: SPUTNIK is freely available online from CRAN repository and at https://github.com/paoloinglese/SPUTNIK. The package is distributed under the GNU General Public License version 3 and is accompanied by example files and data. Supplementary information: Supplementary data are available at Bioinformatics online.
Ebbels TMD, Pearce JTM, Sadawi N, et al., 2019, Big Data and Databases for Metabolic Phenotyping, HANDBOOK OF METABOLIC PHENOTYPING, Editors: Lindon, Nicholson, Holmes, Publisher: ELSEVIER SCIENCE BV, Pages: 329-367, ISBN: 978-0-12-812293-8
Davenport AP, Brame AL, Kuc RE, et al., 2018, First In human study of a novel biased apelin receptor ligand, MM54, a G-alpha(i) agonist/beta-arrestin antagonist, Scientific Sessions of the American-Heart-Association (AHA), Publisher: American Heart Association, Pages: E75-E76, ISSN: 0009-7330
Introduction: The peptide apelin acts via G proteins to cause beneficial vasodilation and potent positive inotropy to ameliorate pulmonary arterial hypertension in humans and animal models. Apelin is internalised via β-arrestin. In contrast, with loss of endogenous apelin, its receptor acts as a mechanosensor, stimulating β-arrestin to induce detrimental cardiac hypertrophy. Our aim was to characterise the action of our apelin ligand, MM54 that in cell based assays blocks β-arrestin but activates the Gαi protein pathway, in this first in human study. Method: Competition binding in human heart (n=3) used [I125] [Pyr1]apelin-13 (0.1nmol/L). β-arrestin recruitment, receptor internalization and forskolin-induced cAMP inhibition were measured in CHO-K1 cells expressing human apelin receptor. Forearm blood flow was measured in 9 volunteers using venous occlusion plethysmography at baseline and at 4 incremental doses (1, 10, 30, 100 nmol/min) of MM54, each for eight minutes. The Aellig hand vein technique was used to measure the effect of 3 incremental doses (3, 30, 300 nmol/min) of MM54 for 15 min on veins pre-constricted with noradrenaline in 6 individuals compared with 8 controls. Data are mean+SEM, n≥3. Results: MM54 had an affinity of pKi = 6.50±0.03. In β-arrestin (pKB 6.93±0.15) and receptor internalization assays (pKB 5.89±0.06) MM54 was an antagonist, but activated the G protein pathway (pD2±SEM 5.86+0.23). At the highest concentration (100 nmol/min), MM54 caused a significant absolute increase in forearm blood flow compared to control arm, representing a 76 % change from baseline (P<0.01, ANOVA with repeated measures with Dunnett’s post hoc analysis on untransformed data). In the hand vein, MM54 caused a significant concentration dependent dilatation in veins over the concentration range tested, with the highest dose causing 57% reversal (P<0.01). Conclusion: At the cellular level, the resu
Peluso A, Ebbels T, Glen R, 2018, Empirical estimation of permutation-based metabolome-wide significance thresholds, Publisher: bioRxiv
A key issue in the omics literature is the search of statistically significant relationships between molecular markers and phenotype. The aim is to detect disease-related discriminatory features while controlling for false positive associations at adequate power. Metabolome-wide association studies have revealed significant relationships of metabolic phenotypes with disease risk by analysing hundreds to tens of thousands of molecular variables leading to multivariate data which are highly noisy and collinear. In this context, Bonferroni or Sidak correction are rather useful as these are valid for independent tests, while permutation procedures allow for the estimation of p-values from the null distribution without assuming independence among features. Nevertheless, under the permutation approach the distribution of p-values may presents systematic deviations from the theoretical null distribution which leads to biased adjusted threshold estimate, e.g. smaller than a Bonferroni or Sidak correction. We make use of parametric approximation methods based on a multivariate Normal distribution to derive stable estimates of the metabolome-wide significance level within a univariate approach based on a permutation procedure which effectively controls the maximum overall type I error rate at the α level. We illustrate the results for different model parametrizations and distributional features of the outcome measure, as well as for diverse correlation levels within the features and between the features and the phenotype in real data and simulated studies. MWSL is the open-source R software package for the empirical estimation of the metabolomic-wide significance level available at https://github.com/AlinaPeluso/MWSL.
Nyimanu D, Read C, Williams TL, et al., 2018, The Apelin-36 Mutant Peptide N-58 (apelin 36-[l28A]) and Its Pegylated Analogue N-140 (apelin 36-[L2c-30kDa-PEG) That Mediate Beneficial Metabolic Actions Are G-Protein Biased Ligands at the Apelin Receptor, Publisher: LIPPINCOTT WILLIAMS & WILKINS, ISSN: 0009-7322
Inglese P, Dos Santos Correia G, Pruski P, et al., 2018, Co-localization features for classification of tumors using mass spectrometry imaging
Statistical modeling of mass spectrometry imaging (MSI) data is a crucial component for the understanding of the molecular characteristics of cancerous tissues. Quantification of the abundances of metabolites or batch effect between multiple spectral acquisitions represents only a few of the challenges associated with this type of data analysis. Here we introduce a method based on ion co-localization features that allows the classification of whole tissue specimens using MSI data, which overcomes the possible batch effect issues and generates data-driven hypotheses on the underlying mechanisms associated with the different classes of analyzed samples.
Kalash L, Cresser-Brown J, Habchi J, et al., 2018, Structure-based design of allosteric calpain-1 inhibitors populating a novel bioactivity space, European Journal of Medicinal Chemistry, Vol: 157, Pages: 1264-1275, ISSN: 0223-5234
Dimeric calpains constitute a promising therapeutic target for many diseases such as cardiovascular, neurodegenerative and ischaemic disease. The discovery of selective calpain inhibitors, however, has been extremely challenging. Previously, allosteric inhibitors of calpains, such as PD150606, which included a specific α-mercaptoacrylic acid sub-structure, were reported to bind to the penta-EF hand calcium binding domain, PEF(S) of calpain. Although these are selective to calpains over other cysteine proteases, their mode of action has remained elusive due to their ability to inhibit the active site domain with and without the presence of PEF(S), with similar potency. These findings have led to the question of whether the inhibitory response can be attributed to an allosteric mode of action or alternatively to inhibition at the active site. In order to address this problem, we report a structure-based virtual screening protocol as a novel approach for the discovery of PEF(S) binders that populate a novel chemical space. We have identified compound 1, Vidupiprant, which is shown to bind to the PEF(S) domain by the TNS displacement method, and it exhibited specificity in its allosteric mode of inhibition. Compound 1 inhibited the full-length calpain-1 complex with a higher potency (IC50 = 7.5 μM) than the selective, cell-permeable non-peptide calpain inhibitor, PD150606 (IC50 = 19.3 μM), where the latter also inhibited the active site domain in the absence of PEF(S) (IC50 = 17.8 μM). Hence the method presented here has identified known compounds with a novel allosteric mechanism for the inhibition of calpain-1. We show for the first time that the inhibition of enzyme activity can be attributed to an allosteric mode of action, which may offer improved selectivity and a reduced side-effects profile.
Kalash L, Winfield I, Safitri D, et al., 2018, MD-assisted approach for designing multi-target ligands at A2AR and PDE10A that elevate cyclic AMP, 256th National Meeting and Exposition of the American-Chemical-Society (ACS) - Nanoscience, Nanotechnology and Beyond, Publisher: AMER CHEMICAL SOC, ISSN: 0065-7727
Inglese P, Strittmatter N, Doria L, et al., 2018, Mass spectrometry: from imaging to metabolic networks
A deeper understanding of inter-tumorand intra-tumorheterogeneity is a critical factor for the advancement of next generation strategies against cancer. Under the hypothesis that heterogeneous progression of tumorsis mirrored by their metabolic heterogeneity, detection of biochemical mechanisms responsible of the local metabolism becomes crucial.We show that network analysis of co-localized ions from mass spectrometry imaging data provides a detailed chemo-spatial insightinto the metabolic heterogeneity of tumor. Furthermore, module preservation analysis between colorectal cancer patients with and without metastatic recurrence suggests hypotheses on the nature of the different local metabolic pathways.
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