ReviewChemical reviews2026
Mechanisms and Manifestations of Ligand Bias in Signaling: Focus on Receptor Tyrosine Kinases.
Review in Chemical reviews, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
5 authors.
Funding
Abstract
The development of organisms and the maintenance of tissue homeostasis depend on complex intercellular signaling networks that govern basic cell functions. Because cells are typically exposed to many diverse ligands simultaneously, distinct intracellular mechanisms of cell signaling have evolved, allowing cells to accurately sense and respond to their environment. Here, we discuss the concept of ligand bias, which describes the ability of different ligands to preferentially activate specific signaling pathways, and thus produce divergent responses through the same receptor. Although bias can be challenging to identify and quantify, protocols have been established to (i) determine whether preferences exist and (ii) quantify these preferences. We review these protocols and their utility in studies of signaling by RTKs, the largest family of single-pass membrane receptors. We summarize the literature suggesting that RTKs engage in biased signaling and discuss the utility of signaling bias in therapeutics design. We discuss parallels with GPCR biased signaling, and lessons that have been learned after years of GPCR signaling research. Finally, we propose that RTK ligand bias appeared in evolution to diversify the morphogen signaling pathways that direct the development of tissues and organs in the mammalian body.
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42542977What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.