Cellular gp96 upregulates AFP expression by blocking NR5A2 SUMOylation and ubiquitination in hepatocellular carcinoma

Alpha-fetoprotein (AFP) is the most widely used biomarker for the diagnosis of hepatocellular carcinoma (HCC). However, asubstantial proportion of HCC patients have either normal or marginally increased AFP levels in serum, and the underlyingmechanisms are not fully understood. In the present study, we provided in vitro and in vivo evidence that heat shock protein gp96promoted AFP expression at the transcriptional level in HCC. NR5A2 was identified as a key transcription factor for the AFP gene, andits stability was enhanced by gp96. A further mechanistic study by co-immunoprecipitation, GST pull-down, and molecular dockingshowed gp96 and the SUMO E3 ligase RanBP2 competitively binding to NR5A2 at the sites spanning from aa 507 to aa 539. Thebinding of gp96 inhibited SUMOylation, ubiquitination, and subsequent degradation of NR5A2. In addition, clinical analysis of HCCpatients indicated that gp96 expression in tumors was positively correlated with serum AFP levels. Therefore, our study uncovered anovel mechanism that gp96 regulates the stability of its client proteins by directly affecting their SUMOylation and ubiquitination.These findings will help in designing more accurate AFP-based HCC diagnosis and progression monitoring approaches.

McSNAC:A software to approximate first-order signaling networks from mass cytometry data

Background:Mass cytometry(CyTOF)gives unprecedented opportunity to simultaneously measure up to 40 proteins in single cells,with a theoretical potential to reach 100 proteins.This high-dimensional single-cell information can be very useful in dissecting mechanisms of cellular activity.In particular,measuring abundances of signaling proteins like phospho-proteins can provide detailed information on the dynamics of single-cell signaling processes.However,computational analysis is required to reconstruct such networks with a mechanistic model.Methods:We propose our Mass cytometry Signaling Network Analysis Code(McSNAC),a new software capable of reconstructing signaling networks and estimating their kinetic parameters from CyTOF data.McSNAC approximates signaling networks as a network of first-order reactions between proteins.This assumption often breaks down as signaling reactions can involve binding and unbinding,enzymatic reactions,and other nonlinear constructions.Furthermore,McSNAC may be limited to approximating indirect interactions between protein species,as cytometry experiments are only able to assay a small fraction of protein species involved in signaling.Results:We carry out a series of in silico experiments here to show(1)McSNAC is capable of accurately estimating the ground-truth model in a scalable manner when given data originating from a first-order system;(2)McSNAC is capable of qualitatively predicting outcomes to perturbations of species abundances in simple second-order reaction models and in a complex in silico nonlinear signaling network in which some proteins are unmeasured.Conclusions:These findings demonstrate that McSNAC can be a valuable screening tool for generating models of signaling networks from time-stamped CyTOF data.