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1.
PLoS One ; 15(12): e0242543, 2020.
Article in English | MEDLINE | ID: mdl-33326419

ABSTRACT

Clinical studies using a range of omega-3 supplements have yielded conflicting results on their efficacy to control inflammation. Omega-3 fatty acids are substrate for the formation of potent immune-protective mediators, termed as specialized pro-resolving mediators (SPM). Herein, we investigated whether observed differences in the potencies of distinct omega-3 supplements were linked with their ability to upregulate SPM formation. Using lipid mediator profiling we found that four commercially available supplements conferred a unique SPM signature profile to human macrophages, with the overall increases in SPM concentrations being different between the four supplements. These increases in SPM concentrations were linked with an upregulation of macrophage phagocytosis and a decreased uptake of oxidized low-density lipoproteins. Pharmacological inhibition of two key SPM biosynthetic enzymes 5-Lipoxygenase or 15-Lipoxygenase reversed the macrophage-directed actions of each of the omega-3 supplements. Furthermore, administration of the two supplements that most potently upregulated macrophage SPM formation and reprogrammed their responses in vitro, to APOE-/- mice fed a western diet, increased plasma SPM concentrations and reduced vascular inflammation. Together these findings support the utility of SPM as potential prognostic markers in determining the utility of a given supplement to regulate macrophage responses and inflammation.


Subject(s)
Atherosclerosis/prevention & control , Dietary Supplements , Fatty Acids, Omega-3/administration & dosage , Leukotrienes/biosynthesis , Lipoxins/biosynthesis , Macrophages/drug effects , Prostaglandins/biosynthesis , Animals , Apolipoproteins E/deficiency , Apolipoproteins E/genetics , Apolipoproteins E/immunology , Arachidonate 15-Lipoxygenase/genetics , Arachidonate 15-Lipoxygenase/immunology , Arachidonate 5-Lipoxygenase/genetics , Arachidonate 5-Lipoxygenase/immunology , Atherosclerosis/etiology , Atherosclerosis/immunology , Atherosclerosis/metabolism , Diet, Western/adverse effects , Fatty Acids, Omega-3/metabolism , Female , Gene Expression , Humans , Leukotrienes/immunology , Lipoproteins, LDL/antagonists & inhibitors , Lipoproteins, LDL/pharmacology , Lipoxins/immunology , Lipoxygenase Inhibitors/pharmacology , Macrophages/cytology , Macrophages/immunology , Male , Mice , Mice, Knockout, ApoE , Phagocytosis/drug effects , Primary Cell Culture , Principal Component Analysis , Prostaglandins/immunology
2.
Lab Chip ; 17(3): 511-520, 2017 01 31.
Article in English | MEDLINE | ID: mdl-28092382

ABSTRACT

There is a growing awareness that complex 3-dimensional (3D) organs are not well represented by monolayers of a single cell type - the standard format for many drug screens. To address this deficiency, and with the goal of improving screens so that drugs with good efficacy and low toxicity can be identified, microphysiological systems (MPS) are being developed that better capture the complexity of in vivo physiology. We have previously described an organ-on-a-chip platform that incorporates perfused microvessels, such that survival of the surrounding tissue is entirely dependent on delivery of nutrients through the vessels. Here we describe an arrayed version of the platform that incorporates multiple vascularized micro-organs (VMOs) on a 96-well plate. Each VMO is independently-addressable and flow through the micro-organ is driven by hydrostatic pressure. The platform is easy to use, requires no external pumps or valves, and is highly reproducible. As a proof-of-concept we have created arrayed vascularized micro tumors (VMTs) and used these in a blinded screen to assay a small library of compounds, including FDA-approved anti-cancer drugs, and successfully identified both anti-angiogenic and anti-tumor drugs. This 3D platform is suitable for efficacy/toxicity screening against multiple tissues in a more physiological environment than previously possible.


Subject(s)
Cell Culture Techniques/instrumentation , Drug Evaluation, Preclinical/instrumentation , Microfluidic Analytical Techniques/instrumentation , Tissue Array Analysis/instrumentation , Antineoplastic Agents/pharmacology , Drug Evaluation, Preclinical/methods , Equipment Design , Gene Expression Profiling , Gene Expression Regulation, Neoplastic/drug effects , Humans , Neoplasms, Experimental/metabolism , Neovascularization, Pathologic/metabolism , Tissue Array Analysis/methods
3.
PLoS One ; 10(12): e0144572, 2015.
Article in English | MEDLINE | ID: mdl-26695765

ABSTRACT

Supervised machine learning can be used to predict which drugs human cardiomyocytes have been exposed to. Using electrophysiological data collected from human cardiomyocytes with known exposure to different drugs, a supervised machine learning algorithm can be trained to recognize and classify cells that have been exposed to an unknown drug. Furthermore, the learning algorithm provides information on the relative contribution of each data parameter to the overall classification. Probabilities and confidence in the accuracy of each classification may also be determined by the algorithm. In this study, the electrophysiological effects of ß-adrenergic drugs, propranolol and isoproterenol, on cardiomyocytes derived from human induced pluripotent stem cells (hiPS-CM) were assessed. The electrophysiological data were collected using high temporal resolution 2-photon microscopy of voltage sensitive dyes as a reporter of membrane voltage. The results demonstrate the ability of our algorithm to accurately assess, classify, and predict hiPS-CM membrane depolarization following exposure to chronotropic drugs.


Subject(s)
Adrenergic beta-Agonists/pharmacology , Heart Rate/drug effects , Induced Pluripotent Stem Cells/drug effects , Myocytes, Cardiac/drug effects , Action Potentials/drug effects , Algorithms , Cell Differentiation/drug effects , Cell Line , Electrophysiologic Techniques, Cardiac , Humans , Induced Pluripotent Stem Cells/cytology , Isoproterenol/pharmacology , Propranolol/pharmacology , Supervised Machine Learning
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