Recent research has led to contradictory notions regarding the conventional theory that apoptotic cell death can evoke inflammatory or immunogenic responses orchestrated by released damage-associated patterns (DAMPs). By inducing IL-1β from bone marrow-derived macrophages in an effort to determine the inflammatory mediators released from apoptotic cells, we found that exosomal fractions called “apoptotic exosome-like vesicles” (AEVs) prepared from apoptotic-conditioned medium were the main inflammatory factors. These AEVs showed characteristics of exosomes in their size, density, morphology, and protein expression but had unique marker proteins, sphingosine-1-phosphate receptors 1 and 3 (S1PR1 and 3). Their biogenesis was completely dependent on cellular sphingosine-1-phosphate (S1P)/S1PRs signaling from multiple fine spindles of plasma membrane accompanied by F-actin, S1PR1, S1PR3, and CD63 at the early apoptotic phase and progressing to the maturation of F-actin–guided multivesicular endosomes mediated by Gβγ subunits of S1PRs downstream. S1P-loaded S1PRs on AEVs were critical factors for inducing IL-1β via NF-κB transcriptional factor and p38 MAPK, possibly through the RHOA/NOD2 axis, in differentiating macrophages. The AEVs induced genes of proinflammatory cytokines, chemokines, and mediators in both in vitro and in vivo models. In conclusion, AEVs could be key inflammatory mediators, acting as DAMPs that could explain the pathogeneses of various chronic inflammations, autoimmune diseases, or cancers in the future.

a b s t r a c tPosttranslational modifications play a crucial role in modulating protein structure and function. Genetic incorporation of unnatural amino acids into a specific site of a protein facilitates the systematic study of protein modifications including acetylation. We here report the directed evolution of pyrrolysyl-tRNA synthetase (PylRS) from Methanosarcina mazei to create N-acetyl lysyl-tRNA synthetases (AcKRSs) using a new selection system based on the killing activity of the toxic ccdB gene product. The amino acid specificity of these and of published [1,2] AckRSs was tested in vitro and in vivo, and the enzyme-kinetic properties of the AckRSs were evaluated for the first time.

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Fluorescence labelling of an intracellular biomolecule in native living cells is a powerful strategy to achieve in-depth understanding of the biomolecule's roles and functions. Besides being nontoxic and specific, desirable labelling probes should be highly cell permeable without nonspecific interactions with other cellular components to warrant high signal-to-noise ratio. While it is critical, rational design for such probes is tricky. Here we report the first predictive model for cell permeable background-free probe development through optimized lipophilicity, water solubility and charged van der Waals surface area. The model was developed by utilizing high-throughput screening in combination with cheminformatics. We demonstrate its reliability by developing CO-1 and AzG-1, a cyclooctyne- and azide-containing BODIPY probe, respectively, which specifically label intracellular target organelles and engineered proteins with minimum background. The results provide an efficient strategy for development of background-free probes, referred to as ‘tame' probes, and novel tools for live cell intracellular imaging.

Analysis of protein dynamics using single-molecule fluorescence resonance energy transfer (smFRET) is widely used to understand the structure and function of proteins. Nonetheless, site-specific labeling of proteins with a pair of donor and acceptor dyes still remains a challenge. Here we present a general and facile method for site-specific dual labeling of proteins by incorporating two different, readily available, unnatural amino acids (p-acetylphenylalanine and alkynyllysine) for smFRET. We used newly evolved alkynyllysine-specific aminoacyl-tRNA synthetase/tRNA(UCA) and p-acetylphenylalanyl-tRNA synthetase/tRNA(CUA). The utility of our approach was demonstrated by analyzing the conformational change of dual-labeled calmodulin using smFRET measurements. The present labeling approach is devoid of major limitations in conventional cysteine-based labeling. Therefore, our method will significantly increase the repertoire of proteins available for FRET study and expand our ability to explore more complicated molecular dynamics.

The advent of advanced metering infrastructure (AMI) generates a large volume of data related with energy service. This paper exploits data mining approach for customer baseline load (CBL) estimation in demand response (DR) management. CBL plays a significant role in measurement and verification process, which quantifies the amount of demand reduction and authenticates the performance. The proposed data-driven baseline modeling is based on the unsupervised learning technique. Specifically we leverage both the self organizing map (SOM) and K-means clustering for accurate estimation. This two-level approach efficiently reduces the large data set into representative weight vectors in SOM, and then these weight vectors are clustered by K-means clustering to find the load pattern that would be similar to the potential load pattern of the DR event day. To verify the proposed method, we conduct nationwide scale experiments where three major cities' residential consumption is monitored by smart meters. Our evaluation compares the proposed solution with the various types of day matching techniques, showing that our approach outperforms the existing methods by up to a 68.5% lower error rate.

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