Cyclophilin A (CyPA), a ubiquitously distributed intracellular protein, is a peptidylprolyl cis-trans-isomerase and the major target of the potent immunosuppressive drug cyclosporin A. Although expressed predominantly as an intracellular molecule, CyPA is secreted by cells in response to inflammatory stimuli and is a potent neutrophil and eosinophil chemoattractant in vitro and in vivo. The mechanisms underlying CyPA-mediated signaling and chemotaxis are unknown. Here, we identified CD147 as a cell surface receptor for CyPA and demonstrated that CD147 is an essential component in the CyPA-initiated signaling cascade that culminates in ERK activation. Both signaling and chemotactic activities of CyPA depended also on the presence of heparans, which served as primary binding sites for CyPA on target cells. The proline 180 and glycine 181 residues in the extracellular domain of CD147 were critical for signaling and chemotactic activities mediated by CD147. Also crucial were active site residues of CyPA, because rotamase-defective CyPA mutants failed to initiate signaling events. These results establish cyclophilins as natural ligands for CD147 and suggest an unusual, rotamase-dependent mechanism of signaling.

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Anti-CRISPRs are widespread amongst bacteriophage and promote bacteriophage infection by inactivating the bacterial host's CRISPR–Cas defence system. Identifying and characterizing anti-CRISPR proteins opens an avenue to explore and control CRISPR–Cas machineries for the development of new CRISPR–Cas based biotechnological and therapeutic tools. Past studies have identified anti-CRISPRs in several model phage genomes, but a challenge exists to comprehensively screen for anti-CRISPRs accurately and efficiently from genome and metagenome sequence data. Here, we have developed an ensemble learning based predictor, PaCRISPR, to accurately identify anti-CRISPRs from protein datasets derived from genome and metagenome sequencing projects. PaCRISPR employs different types of feature recognition united within an ensemble framework. Extensive cross-validation and independent tests show that PaCRISPR achieves a significantly more accurate performance compared with homology-based baseline predictors and an existing toolkit. The performance of PaCRISPR was further validated in discovering anti-CRISPRs that were not part of the training for PaCRISPR, but which were recently demonstrated to function as anti-CRISPRs for phage infections. Data visualization on anti-CRISPR relationships, highlighting sequence similarity and phylogenetic considerations, is part of the output from the PaCRISPR toolkit, which is freely available at http://pacrispr.erc.monash.edu/.