Circadian rhythms are essential to health. Their disruption is associated with metabolic diseases in experimental animals and man. Local metabolic rhythms represent an output of tissue-based circadian clocks. Attempts to define how local metabolism is temporally coordinated have focused on gene expression by defining extensive and divergent "circadian transcriptomes" involving 5%-10% of genes assayed. These analyses are inevitably incomplete, not least because metabolic coordination depends ultimately upon temporal regulation of proteins. We therefore conducted a systematic analysis of a mammalian "circadian proteome." Our analysis revealed that up to 20% of soluble proteins assayed in mouse liver are subject to circadian control. Many of these circadian proteins are novel and cluster into discrete phase groups so that the liver's enzymatic profile contrasts dramatically between day and night. Unexpectedly, almost half of the cycling proteins lack a corresponding cycling transcript, as determined by quantitative PCR, microarray, or both and revealing for the first time the extent of posttranscriptional mechanisms as circadian control points. The circadian proteome includes rate-limiting factors in vital pathways, including urea formation and sugar metabolism. These findings provide a new perspective on the extensive contribution of circadian programming to hepatic physiology.

Site-selective chemical conjugation of synthetic molecules to proteins expands their functional and therapeutic capacity. Current protein modification methods, based on synthetic and biochemical technologies, can achieve site selectivity, but these techniques often require extensive sequence engineering or are restricted to the N- or C-terminus. Here we show the computer-assisted design of sulfonyl acrylate reagents for the modification of a single lysine residue on native protein sequences. This feature of the designed sulfonyl acrylates, together with the innate and subtle reactivity differences conferred by the unique local microenvironment surrounding each lysine, contribute to the observed regioselectivity of the reaction. Moreover, this site selectivity was predicted computationally, where the lysine with the lowest pKa was the kinetically favored residue at slightly basic pH. Chemoselectivity was also observed as the reagent reacted preferentially at lysine, even in those cases when other nucleophilic residues such as cysteine were present. The reaction is fast and proceeds using a single molar equivalent of the sulfonyl acrylate reagent under biocompatible conditions (37 °C, pH 8.0). This technology was demonstrated by the quantitative and irreversible modification of five different proteins including the clinically used therapeutic antibody Trastuzumab without prior sequence engineering. Importantly, their native secondary structure and functionality is retained after the modification. This regioselective lysine modification method allows for further bioconjugation through aza-Michael addition to the acrylate electrophile that is generated by spontaneous elimination of methanesulfinic acid upon lysine labeling. We showed that a protein–antibody conjugate bearing a site-specifically installed fluorophore at lysine could be used for selective imaging of apoptotic cells and detection of Her2+ cells, respectively. This simple, robust method does not require genetic engineering and may be generally used for accessing diverse, well-defined protein conjugates for basic biology and therapeutic studies.

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We have characterised the proteolytic cleavage events responsible for the shedding of triggering receptor expressed on myeloid cells 2 (TREM2) from primary cultures of human macrophages, murine microglia and TREM2‐expressing human embryonic kidney (HEK293) cells. In all cell types, a soluble 17 kDa N‐terminal cleavage fragment was shed into the conditioned media in a constitutive process that is inhibited by G1254023X and metalloprotease inhibitors and siRNA targeting ADAM10. Inhibitors of serine proteases and matrix metalloproteinases 2/9, and ADAM17 siRNA did not block TREM2 shedding. Peptidomimetic protease inhibitors highlighted a possible cleavage site, and mass spectrometry confirmed that shedding occurred predominantly at the H157‐S158 peptide bond for both wild‐type and H157Y human TREM2 and for the wild‐type murine orthologue. Crucially, we also show that the Alzheimer's disease‐associated H157Y TREM2 variant was shed more rapidly than wild type from HEK293 cells, possibly by a novel, batimastat‐ and ADAM10‐siRNA‐independent, sheddase activity. These insights offer new therapeutic targets for modulating the innate immune response in Alzheimer's and other neurological diseases.

The organization of eukaryotic cells into distinct subcompartments is vital for all functional processes, and aberrant protein localization is a hallmark of many diseases. Microscopy methods, although powerful, are usually low-throughput and dependent on the availability of fluorescent fusion proteins or highly specific and sensitive antibodies. One method that provides a global picture of the cell is localization of organelle proteins by isotope tagging (LOPIT), which combines biochemical cell fractionation using density gradient ultracentrifugation with multiplexed quantitative proteomics mass spectrometry, allowing simultaneous determination of the steady-state distribution of hundreds of proteins within organelles. Proteins are assigned to organelles based on the similarity of their gradient distribution to those of well-annotated organelle marker proteins. We have substantially re-developed our original LOPIT protocol (published by Nature Protocols in 2006) to enable the subcellular localization of thousands of proteins per experiment (hyperLOPIT), including spatial resolution at the suborganelle and large protein complex level. This Protocol Extension article integrates all elements of the hyperLOPIT pipeline, including an additional enrichment strategy for chromatin, extended multiplexing capacity of isobaric mass tags, state-of-the-art mass spectrometry methods and multivariate machine-learning approaches for analysis of spatial proteomics data. We have also created an open-source infrastructure to support analysis of quantitative mass-spectrometry-based spatial proteomics data (http://bioconductor.org/packages/pRoloc) and an accompanying interactive visualization framework (http://www. bioconductor.org/packages/pRolocGUI). The procedure we outline here is applicable to any cell culture system and requires ∼1 week to complete sample preparation steps, ∼2 d for mass spectrometry data acquisition and 1-2 d for data analysis and downstream informatics.