Quantitative and qualitative differences in the activation of a fibroblast growth factor receptor by different FGF ligands.

The FGF system is the most complex of all receptor tyrosine kinase signaling networks with 18 FGF ligands and four FGFRs that deliver morphogenic signals to pattern most embryonic structures. Even when a single FGFR is expressed in the tissue, different FGFs can trigger dramatically different biological responses via this receptor. Here we show both quantitative and qualitative differences in the signaling of one of the FGF receptors, FGFR1c, in response to different FGFs.

Ligand bias underlies differential signaling of multiple FGFs via FGFR1.

The differential signaling of multiple FGF ligands through a single fibroblast growth factor (FGF) receptor (FGFR) plays an important role in embryonic development. Here, we use quantitative biophysical tools to uncover the mechanism behind differences in FGFR1c signaling in response to FGF4, FGF8, and FGF9, a process which is relevant for limb bud outgrowth. We find that FGF8 preferentially induces FRS2 phosphorylation and extracellular matrix loss, while FGF4 and FGF9 preferentially induce FGFR1c phosphorylation and cell growth arrest.

Ovarian stimulation with excessive FSH doses causes cumulus cell and oocyte dysfunction in small ovarian reserve heifers.

Excessive FSH doses during ovarian stimulation in the small ovarian reserve heifer (SORH) cause premature cumulus expansion and follicular hyperstimulation dysgenesis (FHD) in nearly all ovulatory-size follicles with predicted disruptions in cell-signaling pathways in cumulus cells and oocytes (before ovulatory hCG stimulation). These observations support the hypothesis that excessive FSH dysregulates cumulus cell function and oocyte maturation. To test this hypothesis, we determined whether excessive FSH-induced differentially expressed genes (DEGs) in cumulus cells identified in our previously published transcriptome analysis were altered independent of extreme phenotypic differences observed amongst ovulatory-size follicles, and assessed predicted roles of these DEGs in cumulus and oocyte biology.

Quantitative assessment of ligand bias from bias plots: The bias coefficient "kappa".

The current methods for quantifying ligand bias involve the construction of bias plots and the calculations of bias coefficients that can be compared using statistical methods. However, widely used bias coefficients can diverge in their abilities to identify ligand bias and can give false positives. As the empirical bias plots are considered the most reliable tools in bias identification, here we develop an analytical description of bias plot trajectories and introduce a bias coefficient, kappa, which is calculated from these trajectories.