15 research outputs found
S295 THE EFFECT OF COMT-GENOTYPE ON EXPERIMENTAL AND CLINICAL POSTOPERATIVE PAIN IN HUMAN BREAST CANCER PATIENTS
A Novel Small Molecule GDNF Receptor RET Agonist, BT13, Promotes Neurite Growth from Sensory Neurons in Vitro and Attenuates Experimental Neuropathy in the Rat
Neuropathic pain caused by nerve damage is a common and severe class of chronic pain. Disease-modifying clinical therapies are needed as current treatments typically provide only symptomatic relief; show varying clinical efficacy; and most have significant adverse effects. One approach is targeting either neurotrophic factors or their receptors that normalize sensory neuron function and stimulate regeneration after nerve damage. Two candidate targets are glial cell line-derived neurotrophic factor (GDNF) and artemin (ARTN), as these GDNF family ligands (GFLs) show efficacy in animal models of neuropathic pain (Boucher et al., 2000; Gardell et al., 2003: Wang et al., 2008, 2014). As these protein ligands have poor drug-like properties and are expensive to produce for clinical use, we screened 18,400 drug-like compounds to develop small molecules that act similarly to GFLs (GDNF mimetics). This screening identified BT13 as a compound that selectively targeted GFL receptor RET to activate downstream signaling cascades. BT13 was similar to NGF and ARTN in selectively promoting neurite outgrowth from the peptidergic class of adult sensory neurons in culture, but was opposite to ARTN in causing neurite elongation without affecting initiation. When administered after spinal nerve ligation in a rat model of neuropathic pain, 20 and 25 mg/kg of BT13 decreased mechanical hypersensitivity and normalized expression of sensory neuron markers in dorsal root ganglia. In control rats, BT13 had no effect on baseline mechanical or thermal sensitivity, motor coordination, or weight gain. Thus, small molecule BT13 selectively activates RET and offers opportunities for developing novel disease-modifying medications to treat neuropathic pain.Peer reviewe
A Novel Small Molecule GDNF Receptor RET Agonist, BT13, Promotes Neurite Growth from Sensory Neurons in Vitro and Attenuates Experimental Neuropathy in the Rat
Neuropathic pain caused by nerve damage is a common and severe class of chronic pain. Disease-modifying clinical therapies are needed as current treatments typically provide only symptomatic relief; show varying clinical efficacy; and most have significant adverse effects. One approach is targeting either neurotrophic factors or their receptors that normalize sensory neuron function and stimulate regeneration after nerve damage. Two candidate targets are glial cell line-derived neurotrophic factor (GDNF) and artemin (ARTN), as these GDNF family ligands (GFLs) show efficacy in animal models of neuropathic pain (Boucher et al., 2000; Gardell et al., 2003; Wang et al., 2008, 2014). As these protein ligands have poor drug-like properties and are expensive to produce for clinical use, we screened 18,400 drug-like compounds to develop small molecules that act similarly to GFLs (GDNF mimetics). This screening identified BT13 as a compound that selectively targeted GFL receptor RET to activate downstream signaling cascades. BT13 was similar to NGF and ARTN in selectively promoting neurite outgrowth from the peptidergic class of adult sensory neurons in culture, but was opposite to ARTN in causing neurite elongation without affecting initiation. When administered after spinal nerve ligation in a rat model of neuropathic pain, 20 and 25 mg/kg of BT13 decreased mechanical hypersensitivity and normalized expression of sensory neuron markers in dorsal root ganglia. In control rats, BT13 had no effect on baseline mechanical or thermal sensitivity, motor coordination, or weight gain. Thus, small molecule BT13 selectively activates RET and offers opportunities for developing novel disease-modifying medications to treat neuropathic pain
Role of Gut Microbiota in Statin-Associated New-Onset Diabetes : A Cross-Sectional and Prospective Analysis of the FINRISK 2002 Cohort
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Integration of polygenic and gut metagenomic risk prediction for common diseases.
Acknowledgements: Y.L. was supported by funding from the Cambridge Baker Centre for Systems Genomics. S.C.R. was supported by a British Heart Foundation program grant (no. RG/18/13/33946). M.O.R. was funded by the Research Council of Finland (grant no. 338818). L.L. was supported by the European Union’s Horizon 2020 research and innovation program (grant no. 952914). T.N. was supported by the Finnish Foundation for Cardiovascular Research, the Sigrid Jusélius Foundation, the Southwestern Finland Hospital District and the Research Council of Finland (grant nos. 321351 and 354447). V.S. was supported by the Finnish Foundation for Cardiovascular Research and the Juho Vainio Foundation. A.S.H. was supported by the Research Council of Finland (grant no. 321356). M.I. was supported by the Munz Chair of Cardiovascular Prediction and Prevention and the NIHR Cambridge Biomedical Research Centre (grant nos. BRC-1215-20014 and NIHR203312). M.I. was also supported by the UK Economic and Social Research 878 Council (grant no. ES/T013192/1). The present study was supported by the Victorian Government’s Operational Infrastructure Support program and by core funding from the British Heart Foundation (grant no. RG/18/13/33946) and the NIHR Cambridge Biomedical Research Centre (grant nos. BRC-1215-20014 and NIHR203312). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. This work was supported by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcome.Funder: SR was supported by a British Health Foundation programme grant (RG/18/13/33946).Funder: MOR was funded by the Research Council of Finland (grant no. 338818).Funder: LL was supported by the European Union’s Horizon 2020 research and innovation program (grant no. 952914).Funder: TN was supported by the Finnish Foundation for Cardiovascular Research, the Sigrid Jusélius Foundation, the Southwestern Finland Hospital District, and the Research Council of Finland (grants no. 321351 and 354447).Funder: VS was supported by the Finnish Foundation for Cardiovascular Research and by the Juho Vainio Foundation.Funder: ASH was supported by the Research Council of Finland, grant no. 321356.Funder: MI was supported by the Munz Chair of Cardiovascular Prediction and Prevention and the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014; NIHR203312). MI was also supported by the UK Economic and Social Research 878 Council (ES/T013192/1).Multiomics has shown promise in noninvasive risk profiling and early detection of various common diseases. In the present study, in a prospective population-based cohort with ~18 years of e-health record follow-up, we investigated the incremental and combined value of genomic and gut metagenomic risk assessment compared with conventional risk factors for predicting incident coronary artery disease (CAD), type 2 diabetes (T2D), Alzheimer disease and prostate cancer. We found that polygenic risk scores (PRSs) improved prediction over conventional risk factors for all diseases. Gut microbiome scores improved predictive capacity over baseline age for CAD, T2D and prostate cancer. Integrated risk models of PRSs, gut microbiome scores and conventional risk factors achieved the highest predictive performance for all diseases studied compared with models based on conventional risk factors alone. The present study demonstrates that integrated PRSs and gut metagenomic risk models improve the predictive value over conventional risk factors for common chronic diseases
Recommended from our members
Integration of polygenic and gut metagenomic risk prediction for common diseases.
Multiomics has shown promise in noninvasive risk profiling and early detection of various common diseases. In the present study, in a prospective population-based cohort with ~18 years of e-health record follow-up, we investigated the incremental and combined value of genomic and gut metagenomic risk assessment compared with conventional risk factors for predicting incident coronary artery disease (CAD), type 2 diabetes (T2D), Alzheimer disease and prostate cancer. We found that polygenic risk scores (PRSs) improved prediction over conventional risk factors for all diseases. Gut microbiome scores improved predictive capacity over baseline age for CAD, T2D and prostate cancer. Integrated risk models of PRSs, gut microbiome scores and conventional risk factors achieved the highest predictive performance for all diseases studied compared with models based on conventional risk factors alone. The present study demonstrates that integrated PRSs and gut metagenomic risk models improve the predictive value over conventional risk factors for common chronic diseases