Browsing by Author "Zheng, Kevin"

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    ItemOpen Access
    A longitudinal study of convergence between Black and White COVID-19 mortality: A county fixed effects approach.
    (Lancet regional health. Americas, 2021-09) Lawton, Ralph; Zheng, Kevin; Zheng, Daniel; Huang, Erich

    Background

    Non-Hispanic Black populations have suffered much greater per capita COVID-19 mortality than White populations. Previous work has shown that rates of Black and White mortality have converged over time. Understanding of COVID-19 disparities over time is complicated by geographic changes in prevalence, and some prior research has claimed that regional shifts in COVID-19 prevalence may explain the convergence.

    Methods

    Using county-level COVID-19 mortality data stratified by race, we investigate the trajectory of Black and White per capita mortality from June 2020-January 2021. We use a county fixed-effects model to estimate changes within counties, then extend our models to leverage county-level variation in prevalence to study the effects of prevalence versus time trajectories in mortality disparities.

    Findings

    Over this period, cumulative mortality rose by 61% and 90% for Black and White populations respectively, decreasing the mortality ratio by 0.4 (25.8%). These trends persisted when a county-level fixed-effects model was applied. Results revealed that county-level changes in prevalence nearly fully explain changes in mortality disparities over time.

    Interpretation

    Results suggest mechanisms underpinning convergence in Black/White mortality are not driven by fixed county-level characteristics or changes in the regional dispersion of COVID-19, but instead by changes within counties. Further, declines in the Black/White mortality ratio over time appear primarily linked to county-level changes in COVID-19 prevalence rather than other county-level factors that may vary with time. Research into COVID-19 disparities should focus on mechanisms that operate within-counties and are consistent with a prevalence-disparity relationship.

    Funding

    This work was supported by the National Center for Advancing Translational Sciences [E.H.: UL1TR002553].
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    ItemOpen Access
    Biased agonists of the chemokine receptor CXCR3 differentially signal through Gαi:β-arrestin complexes.
    (Science signaling, 2022-03-22) Zheng, Kevin; Smith, Jeffrey S; Eiger, Dylan S; Warman, Anmol; Choi, Issac; Honeycutt, Christopher C; Boldizsar, Noelia; Gundry, Jaimee N; Pack, Thomas F; Inoue, Asuka; Caron, Marc G; Rajagopal, Sudarshan
    G protein-coupled receptors (GPCRs) are the largest family of cell surface receptors and signal through the proximal effectors, G proteins and β-arrestins, to influence nearly every biological process. The G protein and β-arrestin signaling pathways have largely been considered separable; however, direct interactions between Gα proteins and β-arrestins have been described that appear to be part of a distinct GPCR signaling pathway. Within these complexes, Gαi/o, but not other Gα protein subtypes, directly interacts with β-arrestin, regardless of the canonical Gα protein that is coupled to the GPCR. Here, we report that the endogenous biased chemokine agonists of CXCR3 (CXCL9, CXCL10, and CXCL11), together with two small-molecule biased agonists, differentially formed Gαi:β-arrestin complexes. Formation of the Gαi:β-arrestin complexes did not correlate well with either G protein activation or β-arrestin recruitment. β-arrestin biosensors demonstrated that ligands that promoted Gαi:β-arrestin complex formation generated similar β-arrestin conformations. We also found that Gαi:β-arrestin complexes did not couple to the mitogen-activated protein kinase ERK, as is observed with other receptors such as the V2 vasopressin receptor, but did couple with the clathrin adaptor protein AP-2, which suggests context-dependent signaling by these complexes. These findings reinforce the notion that Gαi:β-arrestin complex formation is a distinct GPCR signaling pathway and enhance our understanding of the spectrum of biased agonism.