214 results found
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, 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.
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
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, ISSN: 1610-2940
Sands C, Wolfer A, DS Correia G, et al., The nPYc-Toolbox, a Python module for the pre-processing, quality-control, and analysis of metabolic profiling datasets, Bioinformatics, 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
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
Read C, Yang P, Kuc RE, et al., 2019, 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, 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.
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.
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: LIPPINCOTT WILLIAMS & WILKINS, Pages: E75-E76, ISSN: 0009-7330
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.
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.
Dumas M-E, Chilloux J, Myridakis A, et al., 2018, Microbiome inhibition of IRAK-4 by trimethylamine mediates metabolic and immune benefits in high fat diet-induced insulin resistance, 54th Annual Meeting of the European-Association-for-the-Study-of-Diabetes (EASD), Publisher: SPRINGER, Pages: S267-S268, ISSN: 0012-186X
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.
Hoyles L, Snelling T, Umlai UK, et al., 2018, Microbiome–host systems interactions: protective effects of propionate upon the blood–brain barrier, Microbiome, Vol: 6, ISSN: 2049-2618
Background: Gut microbiota composition and function are symbiotically linked with host health, and altered in metabolic, inflammatory and neurodegenerative disorders. Three recognized mechanisms exist by which the microbiome influences the gut--brain axis: modification of autonomic/sensorimotor connections, immune activation, and neuroendocrine pathway regulation. We hypothesized interactions between circulating gut-derived microbial metabolites and the blood--brain barrier (BBB) also contribute to the gut--brain axis. Propionate, produced from dietary substrates by colonic bacteria, stimulates intestinal gluconeogenesis and is associated with reduced stress behaviours, but its potential endocrine role has not been addressed. Results: After demonstrating expression of the propionate receptor FFAR3 on human brain endothelium, we examined the impact of a physiologically relevant propionate concentration (1 μM) on BBB properties in vitro. Propionate inhibited pathways associated with non-specific microbial infections via a CD14-dependent mechanism, suppressed expression of LRP-1 and protected the BBB from oxidative stress via NRF2 (NFE2L2) signaling. Conclusions: Together, these results suggest gut-derived microbial metabolites interact with the BBB, representing a fourth facet of the gut--brain axis that warrants further attention.
Hoyles L, Snelling T, Umlai U-K, et al., 2018, Propionate has protective and anti-inflammatory effects on the blood–brain barrier, Alzheimer's Research UK Research Conference 2018
Propionate is a short-chain fatty acid (SCFA) produced by the human gut microbiota from dietary substrates, and is biologically active via the G protein coupled receptors FFAR2 and FFAR3. It is taken up from the gut and reaches systemic circulation in micromolar quantities. The blood–brain barrier (BBB) is the major interface between the circulation and central nervous system. FFAR3 is expressed on the vascular endothelium and a likely target for propionate in the BBB. We hypothesized exposure of the BBB to propionate influences barrier integrity and function.Methods and materialsWe investigated the in vitro effects of a physiologically relevant concentration (1 μM) of propionate upon the human immortalised cerebromicrovascular endothelial cell line hCMEC/D3. FFAR3 was present on these cells. We, therefore, performed an unbiased transcriptomic analysis of confluent hCMEC/D3 monolayers treated or not for 24 h with 1 μM propionate, supported by in vitro validation of key findings and assessment of functional endothelial permeability barrier properties.ResultsPropionate treatment had a significant (PFDR < 0.1) effect on the expression of 1136 genes. It inhibited several inflammation-associated pathways: TLR-specific signalling, NFkappaB signalling, and cytosolic DNA-sensing. Functional validation of these findings confirmed the down-regulation of TLR signalling by propionate, achieved primarily through down-regulation of endothelial CD14 expression. Accordingly, propionate prevented LPS-induced increases in paracellular permeability to 70 kDa FITC-dextran and loss of transendothelial electrical resistance. Propionate activated the NFE2L2 (NRF2)-driven protective response against oxidative stress. Confirming these data, propionate limited free reactive oxygen species induction by the mitochondrial respiratory inhibitor rotenone. ConclusionsOur data strongly suggest the SCFA propionate contributes to maintaining BBB integrity and protecting against inflamm
McArthur S, Carvalho A, Fonseca S, et al., 2018, Effects of gut-derived trimethylamines on the blood–brain barrier, Alzheimer's Research UK Research Conference 2018
Introduction: The gut microbiota and its metabolites exert significant effects on host health, with disturbances to composition and function associated with conditions including obesity, type II diabetes and, more recently, Alzheimer’s disease (AD). Communication between microbes and the host can take a number of forms, but central to all of them is a role for gut-derived microbial metabolites, with trimethylamine N-oxide (TMAO) and its precursor trimethylamine (TMA) being important examples. TMA produced by gut bacteria is converted to TMAO in the liver by flavin monooxygenases whereupon it enters the circulation. TMAO was recently identified as potentially important in genetic pathways associated with AD, and has been shown to influence peripheral vascular function. Given these links, the key position of the cerebral vasculature as the major interface between circulating molecules and the brain, and evidence that deficits in blood–brain barrier (BBB) function occur early in AD, we investigated the effects of TMAO and TMA on key BBB properties in vitro and in vivo.Materials and Methods: Male C57Bl/6 mice (n=4-5) were used to examine the effect of TMAO treatment (1.8 mg/kg, 2 h, dose equivalent to circulating human concentrations) upon BBB permeability in vivo, assessed by Evans’ blue dye extravasation. TMA was not investigated as the average mouse plasma concentration of this methylamine is substantially greater than that seen in humans (TMAO-to-TMA ratio 1:10 in mice, 10:1 in humans).Human hCMEC/D3 cerebromicrovascular cells were used as an in vitro model of the BBB to investigate the effects of 24 h treatment with human physiologically relevant doses of TMAO (40 μM) and TMA (0.4 μM), studying (i) functional barrier properties of cell monolayers and (ii) gene expression. Results: Administration of TMAO to mice enhanced BBB integrity above baseline after 2 h treatment (p<0.05). Similarly, in vitro exposure of hCMEC/D3 cells to TMAO enhanc
Hoyles L, Snelling T, Umlai U-K, et al., 2018, Microbiome–host interactions: protective effects of propionate upon the blood–brain barrier, Publisher: biorixiv
Breakdown of foodstuffs by the gut microbiota results in the production of the short-chain fatty acids (SCFAs) acetate, propionate and butyrate. SFCAs are potent bioactive molecules, providing energy for intestinal cells, enhancing satiety and positively influencing metabolic health. They also influence the gut–brain axis. The gut microbiota and/or its bioactive molecules contribute to maintaining the integrity of the blood–brain barrier (BBB), the primary defensive structure of the brain. Propionate is produced by the gut microbiota from the breakdown of glucans found in whole grains, mushrooms and yeast products. It is found in the blood at ≤1 μM. At this physiologically relevant concentration, propionate enhances BBB integrity, mitigating against deleterious inflammatory and oxidative stimuli known to contribute to neurological and psychological diseases. Therefore, there is the potential that dietary supplementation with glucan-containing products may offer protection of the brain against detrimental stimuli.
Kalash L, Val C, Azuaje J, et al., 2017, Computer-aided design of multi-target ligands at A(1)R, A(2A)R and PDE10A, key proteins in neurodegenerative diseases, Journal of Cheminformatics, Vol: 9, ISSN: 1758-2946
Compounds designed to display polypharmacology may have utility in treating complex diseases, where activity at multiple targets is required to produce a clinical effect. In particular, suitable compounds may be useful in treating neurodegenerative diseases by promoting neuronal survival in a synergistic manner via their multi-target activity at the adenosine A1 and A2A receptors (A1R and A2AR) and phosphodiesterase 10A (PDE10A), which modulate intracellular cAMP levels. Hence, in this work we describe a computational method for the design of synthetically feasible ligands that bind to A1 and A2A receptors and inhibit phosphodiesterase 10A (PDE10A), involving a retrosynthetic approach employing in silico target prediction and docking, which may be generally applicable to multi-target compound design at several target classes. This approach has identified 2-aminopyridine-3-carbonitriles as the first multi-target ligands at A1R, A2AR and PDE10A, by showing agreement between the ligand and structure based predictions at these targets. The series were synthesized via an efficient one-pot scheme and validated pharmacologically as A1R/A2AR–PDE10A ligands, with IC50 values of 2.4–10.0 μM at PDE10A and Ki values of 34–294 nM at A1R and/or A2AR. Furthermore, selectivity profiling of the synthesized 2-amino-pyridin-3-carbonitriles against other subtypes of both protein families showed that the multi-target ligand 8 exhibited a minimum of twofold selectivity over all tested off-targets. In addition, both compounds 8 and 16 exhibited the desired multi-target profile, which could be considered for further functional efficacy assessment, analog modification for the improvement of selectivity towards A1R, A2AR and PDE10A collectively, and evaluation of their potential synergy in modulating cAMP levels.
Inglese P, Strittmatter N, Doria L, et al., 2017, Network analysis of mass spectrometry imaging data from colorectal cancer identifies key metabolites common to metastatic development, Publisher: Cold Spring Harbor Laboratory
<jats:title>Abstract</jats:title><jats:p>A deeper understanding of inter-tumor and intra-tumor heterogeneity is a critical factor for the advancement of next generation strategies against cancer. The heterogeneous morphology exhibited by solid tumors is mirrored by their metabolic heterogeneity. Defining the basic biological mechanisms that underlie tumor cell variability will be fundamental to the development of personalized cancer treatments. Variability in the molecular signatures found in local regions of cancer tissues can be captured through an untargeted analysis of their metabolic constituents. Here we demonstrate that DESI mass spectrometry imaging (MSI) combined with network analysis can provide detailed insight into the metabolic heterogeneity of colorectal cancer (CRC). We show that network modules capture signatures which differentiate tumor metabolism in the core and in the surrounding region. Moreover, module preservation analysis of network modules between patients with and without metastatic recurrence explains the inter-subject metabolic differences associated with diverse clinical outcomes such as metastatic recurrence.</jats:p><jats:sec><jats:title>Significance</jats:title><jats:p>Network analysis of DESI-MSI data from CRC human tissue reveals clinically relevant co-expression ion patterns associated with metastatic susceptibility. This delineates a more complex picture of tumor heterogeneity than conventional hard segmentation algorithms. Using tissue sections from central regions and at a distance from the tumor center, ion co-expression patterns reveal common features among patients who developed metastases (up of > 5 years) not preserved in patients who did not develop metastases. This offers insight into the nature of the complex molecular interactions associated with cancer recurrence. Presently, predicting CRC relapse is challenging, and histopathologically like-for-like cancers freque
Cooper S, Barr AR, Glen R, et al., 2017, NucliTrack: an integrated nuclei tracking application, Bioinformatics, Vol: 33, Pages: 3320-3322, ISSN: 1367-4803
Live imaging studies give unparalleled insight into dynamic single cell behaviours and fate decisions. However, the challenge of reliably tracking single cells over long periods of time limits both the throughput and ease with which such studies can be performed. Here, we present NucliTrack, a cross platform solution for automatically segmenting, tracking and extracting features from fluorescently labelled nuclei. NucliTrack performs similarly to other state-of-the-art cell tracking algorithms, but NucliTrack’s interactive, graphical interface makes it significantly more user friendly.
Harford-Wright E, Andre-Gregoire G, Jacobs KA, et al., 2017, Pharmacological targeting of apelin impairs glioblastoma growth., Brain, Vol: 140, Pages: 2939-2954, ISSN: 1460-2156
Glioblastoma are highly aggressive brain tumours that are associated with an extremely poor prognosis. Within these tumours exists a subpopulation of highly plastic self-renewing cancer cells that retain the ability to expand ex vivo as tumourspheres, induce tumour growth in mice, and have been implicated in radio- and chemo-resistance. Although their identity and fate are regulated by external cues emanating from endothelial cells, the nature of such signals remains unknown. Here, we used a mass spectrometry proteomic approach to characterize the factors released by brain endothelial cells. We report the identification of the vasoactive peptide apelin as a central regulator for endothelial-mediated maintenance of glioblastoma patient-derived cells with stem-like properties. Genetic and pharmacological targeting of apelin cognate receptor abrogates apelin- and endothelial-mediated expansion of glioblastoma patient-derived cells with stem-like properties in vitro and suppresses tumour growth in vivo. Functionally, selective competitive antagonists of apelin receptor were shown to be safe and effective in reducing tumour expansion and lengthening the survival of intracranially xenografted mice. Therefore, the apelin/apelin receptor signalling nexus may operate as a paracrine signal that sustains tumour cell expansion and progression, suggesting that apelin is a druggable factor in glioblastoma.
Hoyles L, Snelling T, Umlai UK, et al., 2017, Propionate has protective and anti-inflammatory effects on the blood–brain barrier, Exploring Human Host-Microbiome Interactions in Health and Disease
Production of short-chain fatty acids (SCFAs) from dietary substrates by the gut microbiota is associated with health, with these metabolites influencing the host via the ‘gut–brain axis’. Micromolar quantities of microbially derived SCFAs are taken up from the gut and reach systemic circulation, where they can influence host gene expression through a variety of largely unknown mechanisms. The blood–brain barrier (BBB) is the major interface between the circulation and central nervous system, and is critically involved in the pathogenesis of neuroinflammatory disorders such as stroke and vascular dementia. We hypothesized exposure of the BBB to SCFAs influences barrier integrity and function.To test our hypothesis, we investigated the in vitro effects of a physiologically relevant concentration (1 μM) of propionate upon the human immortalised cerebromicrovascular endothelial cell line hCMEC/D3. Propionate is produced by the microbiota from dietary glucans, and is biologically active via the G protein coupled receptors FFAR2 and FFAR3. It is a highly potent FFAR2 agonist (agonist activity 3.99) and has close to optimal ligand efficiency (-ΔG=1.19 kcal mol-1 atom-1) for this receptor. Notably, FFAR3 is expressed on the vascular endothelium and a likely target for propionate in the BBB.After confirming the presence of FFAR3 on hCMEC/D3 cells, we undertook an unbiased transcriptomic analysis of confluent hCMEC/D3 monolayers treated or not for 24 h with 1 μM propionate, supported by in vitro validation of key findings and assessment of functional endothelial permeability barrier properties.Propionate treatment had a significant (PFDR < 0.1) effect on the expression of 1136 genes: 553 upregulated, 583 downregulated. Propionate inhibited several inflammation-associated pathways: namely, TLR-specific signalling, NFkappaB signalling, and cytosolic DNA-sensing. Functional validation of these findings confirmed the down-regulation of TLR
McArthur S, Umlai UK, Snelling T, et al., 2017, Effects of gut-derived methylamines on the blood–brain barrier, 2017 Alzheimer's Research UK Conference
Introduction: Composition and functions of the gut microbiota are inextricably linked with host health, and altered in conditions such as obesity and type II diabetes. Central to microbe–host crosstalk are gut-derived microbial metabolites, of which trimethylamine N-oxide (TMAO) and its precursor trimethylamine (TMA) are of particular importance. TMA produced by intestinal microbes is converted to TMAO in the liver by flavin monooxygenases with circulating TMAO being associated with cardiovascular disease and insulin resistance. TMAO was also recently identified as potentially important in genetic pathways associated with Alzheimer’s disease (AD). In considering that deficits in blood–brain barrier (BBB) function occur early in AD, and its position as the major interface between circulating metabolites and the brain, we investigated the effects of TMAO and TMA on key BBB properties in vitro.Materials and Methods: Human hCMEC/D3 cerebromicrovascular cells were used as an in vitro model of the BBB to investigate the effects of 24 h treatment with physiologically relevant doses of TMAO and TMA, studying (i) functional barrier properties of cell monolayers, (ii) Aβ efflux transporters and (iii) gene expression.Results: Exposure of hCMEC/D3 cells to TMAO (40 μM) reinforced barrier integrity by enhancing transendothelial electrical resistance (P <0.001) and reducing paracellular permeability to a 70 kDa dextran tracer (P <0.001). In contrast, while TMA (0.4 μM) enhanced electrical resistance (P <0.001), it significantly increased tracer paracellular permeability (P <0.05), consistent with compromised barrier function. Transporter activity analysis showed TMAO inhibited p-glycoprotein function (P <0.001), which was not seen with TMA; neither metabolite affected BCRP function. Human-genome transcriptomic data are currently being analysed.Conclusions: TMAO and TMA affect BBB function in a metabolite-specific manner, regulating barr
Yang P, Kuc RE, Brame AL, et al., 2017, [Pyr(1)]Apelin-13((1-12)) Is a Biologically Active ACE2 Metabolite of the Endogenous Cardiovascular Peptide [Pyr(1)]Apelin-13, FRONTIERS IN NEUROSCIENCE, Vol: 11, ISSN: 1662-453X
Aims: Apelin is a predicted substrate for ACE2, a novel therapeutic target. Our aim was to demonstrate the endogenous presence of the putative ACE2 product [Pyr1]apelin-13(1–12) in human cardiovascular tissues and to confirm it retains significant biological activity for the apelin receptor in vitro and in vivo. The minimum active apelin fragment was also investigated.Methods and Results: [Pyr1]apelin-13 incubated with recombinant human ACE2 resulted in de novo generation of [Pyr1]apelin-13(1–12) identified by mass spectrometry. Endogenous [Pyr1]apelin-13(1–12) was detected by immunostaining in human heart and lung localized to the endothelium. Expression was undetectable in lung from patients with pulmonary arterial hypertension. In human heart [Pyr1]apelin-13(1–12) (pKi = 8.04 ± 0.06) and apelin-13(F13A) (pKi = 8.07 ± 0.24) competed with [125I]apelin-13 binding with nanomolar affinity, 4-fold lower than for [Pyr1]apelin-13 (pKi = 8.83 ± 0.06) whereas apelin-17 exhibited highest affinity (pKi = 9.63 ± 0.17). The rank order of potency of peptides to inhibit forskolin-stimulated cAMP was apelin-17 (pD2 = 10.31 ± 0.28) > [Pyr1]apelin-13 (pD2 = 9.67 ± 0.04) ≥ apelin-13(F13A) (pD2 = 9.54 ± 0.05) > [Pyr1]apelin-13(1–12) (pD2 = 9.30 ± 0.06). The truncated peptide apelin-13(R10M) retained nanomolar potency (pD2 = 8.70 ± 0.04) but shorter fragments exhibited low micromolar potency. In a β-arrestin recruitment assay the rank order of potency was apelin-17 (pD2 = 10.26 ± 0.09) >> [Pyr1]apelin-13 (pD2 = 8.43 ± 0.08) > apelin-13(R10M) (pD2 = 8.26 ± 0.17) > apelin-13(F13A) (pD2 = 7.98 ± 0.04) ≥ [Pyr1]apelin-13(1–12) (pD2 = 7.84 ± 0.06) >> shorter fragments (pD2 < 6). [Pyr1]apelin-13(1–12) and apelin-13(F13A) contracted human saphenous vein with similar sub-nanomolar potencies and [Pyr1]apelin-13(1–
Inglese P, McKenzie JS, Mroz A, et al., 2017, Deep learning and 3D-DESI imaging reveal the hidden metabolic heterogeneity of cancer, Chemical Science, Vol: 8, Pages: 3500-3511, ISSN: 2041-6539
Visual inspection of tumour tissues does not reveal the complex metabolic changes that differentiate cancer and its sub-types from healthy tissues. Mass spectrometry imaging, which quantifies the underlying chemistry, represents a powerful tool for the molecular exploration of tumour tissues. A 3-dimensional topological description of the chemical properties of the tumour permits the formulation of hypotheses about the biological composition and interactions and the possible causes of its heterogeneous structure. The large amount of information contained in such datasets requires powerful tools for its analysis, visualisation and interpretation. Linear methods for unsupervised dimensionality reduction, such as PCA, are inadequate to capture the complex non-linear relationships present in these data. For this reason, a deep unsupervised neural network based technique, parametric t-SNE, is adopted to map a 3D-DESI-MS dataset from a human colorectal adenocarcinoma biopsy onto a 2-dimensional manifold. This technique allows the identification of clusters not visible with linear methods. The unsupervised clustering of the tumour tissue results in the identification of sub-regions characterised by the abundance of identified metabolites, making possible the formulation of hypotheses to account for their significance and the underlying biological heterogeneity in the tumour.
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