细胞生物学
激酶
生物
等离子体电池
细胞分化
效应器
分泌物
细胞
信号转导
抗体
基因
生物化学
免疫学
作者
Emily Robinson,Matthew A. Care,Reuben Tooze,Gina M. Doody
出处
期刊:Journal of Immunology
[The American Association of Immunologists]
日期:2018-05-01
卷期号:200 (1_Supplement): 171.3-171.3
标识
DOI:10.4049/jimmunol.200.supp.171.3
摘要
Abstract The generation of antibody-secreting plasma cells is a step-wise process involving initial B-cell activation and expansion, followed by the persistence of end-stage effector cells that reside in supportive niches. The transition through these stages requires reprogramming for high levels of secretion and adaptation to the bioenergetic demands of producing immunoglobulin. To improve our understanding of the pathways involved, we applied gene co-expression network analysis to temporal data derived from in vitro differentiating human plasma cells. We identified salt-inducible kinase 1 (SIK1) as a highly connected hub gene involved in the transition from a cycling plasmablast to a quiescent plasma cell. SIK1 is part of a kinase family previously linked to the control of metabolism and the negative regulation of TLR signaling, in part through the inactivation of cAMP-regulated transcriptional co-activators (CRTCs). To assess the contribution of SIK family kinases during plasma cell differentiation, we utilized the SIK inhibitors HG-9-91-01 and YKL-05-099. In contrast to macrophages where SIK inhibition enhances anti-inflammatory polarization, exposure of primary human or murine B-cells that have activated plasma cell differentiation to the SIK inhibitors lead to a profound reduction in cell viability. These effects were also replicated in a subset of multiple myeloma cell lines. The results are consistent with the described requirement for CRTC2 inactivation to exit the germinal center reaction and initiate plasma cell differentiation. Our findings extend these earlier observations and provide scope for identification of additional mechanisms that control plasma cell quiescence and secretory capacity.
科研通智能强力驱动
Strongly Powered by AbleSci AI