The Dual Role of TAM Receptors in Autoimmune Diseases and Cancer: An Overview

Wium, Martha; Paccez, Juliano D.; Zerbini, Luiz F.

doi:10.3390/cells7100166

Cited by 53 publications

(56 citation statements)

References 180 publications

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“…In psoriasis subjects, AXL, CA-15-3 and SP-D were significantly downregulated by apremilast. Of note, AXL and its upstream stimulus, IL-16 are known to be involved in Th1 16,17 , Treg 18 , and Th17 19 regulation. In psoriatic arthritis subjects, MPIF-1 (CCL23) was upregulated in the apremilast arm.…”

Section: Systematic Discovery Of Disease Biomarkersmentioning

confidence: 99%

Large-scale Analyses of Disease Biomarkers and Apremilast Pharmacodynamic Effects

Медведева

Stokes

Eisinger

et al. 2020

Sci Rep

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Section: Systematic Discovery Of Disease Biomarkersmentioning

confidence: 99%

Large-scale Analyses of Disease Biomarkers and Apremilast Pharmacodynamic Effects

Медведева

Stokes

Eisinger

et al. 2020

Sci Rep

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Section: Apremilast Effects At Each Time Pointmentioning

confidence: 99%

Large-scale Analyses of Disease Biomarkers and Apremilast Pharmacodynamic Effects

Медведева¹,

Stokes²,

Eisinger

et al. 2019

Preprint

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Discoveries of biomarkers that provide shared link between disease severity, drug-induced pharmacodynamic effects and response status in human trials can provide number of values for patient benefits: elucidating current therapeutic mechanismof-action, and, back-translating to fast-track development of next-generation therapeutics. Both opportunities are predicated on proactive generation of human molecular profiles that capture longitudinal trajectories before and after pharmacological intervention. Here, we present the largest plasma proteomic biomarker dataset available to-date and the corresponding analyses from placebo-controlled Phase III clinical trials of the phosphodiesterase type 4 inhibitor apremilast in psoriasis (PSOR), psoriatic arthritis (PsA), and ankylosing spondylitis (AS) from 526 subjects overall. Using approximately 150 plasma analytes tracked across three time points, we identified IL-17A and KLK-7 as biomarkers for disease severity and apremilast pharmacodynamic effect in psoriasis patients. Combined decline rate of KLK-7, PEDF, MDC and ANGPTL4 by Week 16 represented biomarkers for the responder subgroup, shedding insights into therapeutic mechanisms. In ankylosing spondylitis patients, IL-6 and LRG-1 were identified as biomarkers with concordance to disease severity. Apremilast-induced LRG-1 increase was consistent with the overall lack of efficacy in ankylosing spondylitis. Taken together, these findings expanded the mechanistic knowledge base of apremilast and provided translational foundations to accelerate future efforts including compound differentiation, combination, and repurposing.

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“…Phosphoproteomic scans of differences in protein phosphorylation in pwMS have revealed constitutive high levels of activation of MAPK signalling in immune cells from pwMS 33 . Inhibitors of MEK1, a downstream target of MAPK signalling, have been trialled in rheumatoid arthritis, Crohn's disease, psoriasis, and multiple myeloma 34 .MERTK is a member of the TAM (Tyro3, Axl, and Mer) family of receptor tyrosine kinases which have multiple endogenous ligands, and exist in both membrane-bound and circulating (decoy receptor) forms35 . Upon ligand binding, MERTK signals via PI3K/AKT, MAPK, and NFkB pathways, in addition to several others35 .…”

mentioning

confidence: 99%

“…Inhibitors of MEK1, a downstream target of MAPK signalling, have been trialled in rheumatoid arthritis, Crohn's disease, psoriasis, and multiple myeloma 34 .MERTK is a member of the TAM (Tyro3, Axl, and Mer) family of receptor tyrosine kinases which have multiple endogenous ligands, and exist in both membrane-bound and circulating (decoy receptor) forms35 . Upon ligand binding, MERTK signals via PI3K/AKT, MAPK, and NFkB pathways, in addition to several others35 . Physiological roles of MERTK include regulation of platelet function, inflammation, and phagocytosis36 .…”

mentioning

confidence: 99%

“…Physiological roles of MERTK include regulation of platelet function, inflammation, and phagocytosis36 . Disruption of phagocytic clearance of dead and dying cells is thought to contribute to aberrant exposure of Damage-Associated Molecular Patterns (DAMPs), such as dsDNA, which may act as a substrate for loss of immune tolerance to self-antigens and subsequent autoimmunity35 . In addition, MERTK appears to facilitate astrocytic clearance of synaptic debris in the CNS37 .…”

mentioning

confidence: 99%

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Summary-data-based mendelian randomisation reveals druggable targets for multiple sclerosis

Jacobs

Taylor

Awad

et al. 2020

Preprint

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Background:Multiple Sclerosis (MS) is a complex autoimmune disease caused by a combination of genetic and environmental factors. Translation of Genome-Wide Association Study (GWAS) findings in MS into therapeutics and effective preventive strategies has been limited to date. Methods:We used Summary Data-Based Mendelian Randomisation (SMR) to synthesise findings from public expression quantitative trait locus (eQTL; eQTLgen and CAGE), methylation quantitative trait locus (mQTL; Lothian Birth Cohort and Brisbane Systems Genetics Study), and MS GWAS datasets (International Multiple Sclerosis Genetics Consortium). By correlating the effects of methylation on MS (M-2-MS), methylation on expression (M-2-E), and expression on MS susceptibility (E-2-MS), we prioritise genetic loci with strong evidence of causally influencing MS susceptibility. We overlay these findings onto a list of 'druggable' genes, i.e. genes which are currently, or could theoretically, be targeted by therapeutic compounds. We use GeNets and STRING to identify protein-protein interactions and druggable pathways enriched in our results. We extend these findings to a model of Epstein-Barr Virus-infected B cells, Lymphoblastoid Cell Lines (LCLs). We conducted a systematic review of prioritised genes using the Open Targets platform to identify completed and planned trials targeted prioritised genes in MS and related disease areas. Results:Expression of 45 genes in peripheral was strongly associated with MS susceptibility (False discovery rate 0.05). Of these 45 genes, 20 encode a protein which is currently targeted by an existing therapeutic compound. These genes were enriched for Gene Ontology terms pertaining to immune system function and leukocyte signalling. We refined this prioritised gene list by restricting to loci where CpG site methylation was associated with MS susceptibility (M-2-MS), with gene expression (M-2-E), and where expression was associated with MS susceptibility (E-2-MS). This approach yielded a list of 15 prioritised druggable target genes for which there was evidence of a causal pathway linking methylation, expression, and MS. Five of these 15 genes are targeted by existing drugs (CD40, ERBB2, VEGFB, MERTK, and PARP1), and three were replicated in a smaller eQTL dataset (CD40, MERTK, and PARP1). In LCLs, SMR prioritised 7 druggable gene targets, of which only one was priortised by the multi-omic approach in peripheral blood (FCRL3). Systematic review of Open Targets revealed multiple early-phase trials targeting 13/20 prioritised genes in disorders related to MS. Conclusions:We use public datasets and SMR to identify a list of prioritised druggable genetic targets in Multiple Sclerosis. We hope our findings could be translated into effective repurposing of existing drugs to provide novel therapies for MS and, potentially, provide a platform for developing preventive therapies.

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The Dual Role of TAM Receptors in Autoimmune Diseases and Cancer: An Overview

Cited by 53 publications

References 180 publications

Large-scale Analyses of Disease Biomarkers and Apremilast Pharmacodynamic Effects

Large-scale Analyses of Disease Biomarkers and Apremilast Pharmacodynamic Effects

Large-scale Analyses of Disease Biomarkers and Apremilast Pharmacodynamic Effects

Summary-data-based mendelian randomisation reveals druggable targets for multiple sclerosis

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