UHRF1 is best known for its positive role in the maintenance of DNMT1-mediated DNA methylation and is implicated in a variety of tumor processes. In this paper, we provided evidence to demonstrate a role of UHRF2 in cell motility and invasion through the regulation of the epithelial-mesenchymal transition (EMT) process by acting as a transcriptional co-regulator of the EMT-transcription factors (TFs). We ectopically expressed UHRF2 in gastric cancer cell lines and performed multidimensional proteomics analyses. Proteome profiling analysis suggested a role of UHRF2 in repression of cell-cell adhesion; analysis of proteome-wide TF DNA binding activities revealed the up-regulation of many EMT-TFs in UHRF2-overexpressing cells. These data suggest that UHRF2 is a regulator of cell motility and the EMT program. Indeed, cell invasion experiments demonstrated that silencing of UHRF2 in aggressive cells impaired their abilities of migration and invasion in vitro. Further ChIP-seq identified UHRF2 genomic binding motifs that coincide with several TF binding motifs including EMT-TFs, and the binding of UHRF2 to CDH1 promoter was validated by ChIP-qPCR. Moreover, the interactome analysis with IP-MS uncovered the interaction of UHRF2 with TFs including TCF7L2 and several protein complexes that regulate chromatin remodeling and histone modifications, suggesting that UHRF2 is a transcription co-regulator for TFs such as TCF7L2 to regulate the EMT process. Taken together, our study identified a role of UHRF2 in EMT and tumor metastasis and demonstrated an effective approach to obtain clues of UHRF2 function without prior knowledge through combining evidence from multidimensional proteomics analyses. Molecular & Cellular

Eukaryotic cells store neutral lipids in cytoplasmic lipid droplets (LDs) enclosed in a monolayer of phospholipids and associated proteins [LD proteins (LDPs)]. Growing evidence has demonstrated that LDPs play important roles in the pathogenesis of liver diseases. However, the composition of liver LDPs and the role of their alterations in hepatosteatosis are not well-understood. In this study, we performed liver proteome and LD sub-proteome profiling to identify enriched proteins in LDs as LDPs, and quantified their changes in a high-fat diet (HFD)-induced fatty liver model. Among 5,000 quantified liver proteins, 101 were enriched by greater than 10-fold in the LD sub-proteome and were classified as LDPs. Differential profiling of LDPs in HFD-induced fatty liver provided a list of candidate LDPs for functional investigation. We tested the function of an upregulated LDP, S100a10, in vivo with adenovirus-mediated gene silencing and found, unexpectedly, that knockdown of S100a10 accelerated progression of HFD-induced liver steatosis. The S100A10 interactome revealed a connection between S100A10 and lipid transporting proteins, suggesting that S100A10 regulates the development and formation of LDs by transporting and trafficking. This study identified LD-enriched sub-proteome in homeostatic as well as HFD-induced fatty livers, providing a rich resource for the LDP research field.

MotivationMass spectrometry (MS) based quantification of proteins/peptides has become a powerful tool in biological research with high sensitivity and throughput. The accuracy of quantification, however, has been problematic as not all peptides are suitable for quantification. Several methods and tools have been developed to identify peptides that response well in mass spectrometry and they are mainly based on predictive models, and rarely consider the linearity of the response curve, limiting the accuracy and applicability of the methods. An alternative solution is to select empirically superior peptides that offer satisfactory MS response intensity and linearity in a wide dynamic range of peptide concentration.ResultsWe constructed a reference database for proteome quantification based on experimental mass spectrum response curves. The intensity and dynamic range of over 2 647 773 transitions from 121 318 peptides were obtained from a set of dilution experiments, covering 11 040 gene products. These transitions and peptides were evaluated and presented in a database named SCRIPT-MAP. We showed that the best-responder (BR) peptide approach for quantification based on SCRIPT-MAP database is robust, repeatable and accurate in proteome-scale protein quantification. This study provides a reference database as well as a peptides/transitions selection method for quantitative proteomics.Availability and implementationSCRIPT-MAP database is available at http://www.firmiana.org/responders/.Supplementary information Supplementary data are available at Bioinformatics online.