The BCR serves as both signal transducer and Ag transporter. Binding of Ags to the BCR induces signaling cascades and Ag processing and presentation, two essential cellular events for B cell activation. BCR-initiated signaling increases BCR-mediated Ag-processing efficiency by increasing the rate and specificity of Ag transport. Previous studies showed a critical role for the actin cytoskeleton in these two processes. In this study, we found that actin-binding protein 1 (Abp1/HIP-55/SH3P7) functioned as an actin-binding adaptor protein, coupling BCR signaling and Ag-processing pathways with the actin cytoskeleton. Gene knockout of Abp1 and overexpression of the Src homology 3 domain of Abp1 inhibited BCR-mediated Ag internalization, consequently reducing the rate of Ag transport to processing compartments and the efficiency of BCR-mediated Ag processing and presentation. BCR activation induced tyrosine phosphorylation of Abp1 and translocation of both Abp1 and dynamin 2 from the cytoplasm to plasma membrane, where they colocalized with the BCR and cortical F-actin. Mutations of the two tyrosine phosphorylation sites of Abp1 and depolymerization of the actin cytoskeleton interfered with BCR-induced Abp1 recruitment to the plasma membrane. The inhibitory effect of a dynamin proline-rich domain deletion mutant on the recruitment of Abp1 to the plasma membrane, coimmunoprecipitation of dynamin with Abp1, and coprecipitation of Abp1 with GST fusion of the dyanmin proline-rich domain demonstrate the interaction of Abp1 with dynamin 2. These results demonstrate that the BCR regulates the function of Abp1 by inducing Abp1 phosphorylation and actin cytoskeleton rearrangement, and that Abp1 facilitates BCR-mediated Ag processing by simultaneously interacting with dynamin and the actin cytoskeleton.

Recently artificial intelligence (AI) and machine learning (ML) models have demonstrated remarkable progress with applications developed in various domains. It is also increasingly discussed that AI and ML models and applications should be transparent, explainable, and trustworthy. Accordingly, the field of Explainable AI (XAI) is expanding rapidly. XAI holds substantial promise for improving trust and transparency in AI-based systems by explaining how complex models such as the deep neural network (DNN) produces their outcomes. Moreover, many researchers and practitioners consider that using provenance to explain these complex models will help improve transparency in AI-based systems. In this paper, we conduct a systematic literature review of provenance, XAI, and trustworthy AI (TAI) to explain the fundamental concepts and illustrate the potential of using provenance as a medium to help accomplish explainability in AI-based systems. Moreover, we also discuss the patterns of recent developments in this area and offer a vision for research in the near future. We hope this literature review will serve as a starting point for scholars and practitioners interested in learning about essential components of provenance, XAI, and TAI.

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