The diffuse-type gastric cancer (DGC) is a subtype of gastric cancer with the worst prognosis and few treatment options. Here we present a dataset from 84 DGC patients, composed of a proteome of 11,340 gene products and mutation information of 274 cancer driver genes covering paired tumor and nearby tissue. DGC can be classified into three subtypes (PX1–3) based on the altered proteome alone. PX1 and PX2 exhibit dysregulation in the cell cycle and PX2 features an additional EMT process; PX3 is enriched in immune response proteins, has the worst survival, and is insensitive to chemotherapy. Data analysis revealed four major vulnerabilities in DGC that may be targeted for treatment, and allowed the nomination of potential immunotherapy targets for DGC patients, particularly for those in PX3. This dataset provides a rich resource for information and knowledge mining toward altered signaling pathways in DGC and demonstrates the benefit of proteomic analysis in cancer molecular subtyping.

As a circadian organ, liver executes diverse functions in different phase of the circadian clock. This process is believed to be driven by a transcription program. Here, we present a transcription factor (TF) DNA-binding activity-centered multi-dimensional proteomics landscape of the mouse liver, which includes DNA-binding profiles of different TFs, phosphorylation, and ubiquitylation patterns, the nuclear sub-proteome, the whole proteome as well as the transcriptome, to portray the hierarchical circadian clock network of this tissue. The TF DNA-binding activity indicates diurnal oscillation in four major pathways, namely the immune response, glucose metabolism, fatty acid metabolism, and the cell cycle. We also isolate the mouse liver Kupffer cells and measure their proteomes during the circadian cycle to reveal a cell-type resolved circadian clock. These comprehensive data sets provide a rich data resource for the understanding of mouse hepatic physiology around the circadian clock.

Transcription factors (TFs) are families of proteins that bind to specific DNA sequences, or TF response elements (TFREs), and function as regulators of many cellular processes. Because of the low abundance of TFs, direct quantitative measurement of TFs on a proteome scale remains a challenge. In this study, we report the development of an affinity reagent that permits identification of endogenous TFs at the proteome scale. The affinity reagent is composed of a synthetic DNA containing a concatenated tandem array of the consensus TFREs (catTFRE) for the majority of TF families. By using catTFRE to enrich TFs from cells, we were able to identify as many as 400 TFs from a single cell line and a total of 878 TFs from 11 cell types, covering more than 50% of the gene products that code for the DNA-binding TFs in the genome. We further demonstrated that catTFRE pull-downs could quantitatively measure proteome-wide changes in DNA binding activity of TFs in response to exogenous stimulation by using a labelfree MS-based quantification approach. Applying catTFRE on the evaluation of drug effects, we described a panoramic view of TF activations and provided candidates for the elucidation of molecular mechanisms of drug actions. We anticipate that the catTFRE affinity strategy will find widespread applications in biomedical research.TF activity profiling | transcriptional coregulator | drug effects screening A lmost all biological processes, ranging from cell cycle regulation to organ development, are controlled by the transcriptional regulatory system (1). In the classic cell membraneto-nucleus signal transduction paradigm, transcription factors (TFs) are the final effectors. They are activated and bind to consensus DNA sequences to execute specific transcriptional programs in response to the signal. Thus, the ability to monitor TF activity is important for the delineation of signal transduction pathways when the cells are perturbed (e.g., treated with a drug), or when organs are under the influence of developmental cues.Approximately 1,500 TF coding genes are reported to be in the human genome (2). TFs can be grouped into different families depending on the structure of their DNA binding domains. There are approximately 50 TF families (2), and each family prefers to bind a specific DNA consensus sequence. For example, nuclear receptors (NRs) are ligand-modulated TFs that recognize one or two hormone response element sequences such as 5′-AGAACA-3′ or 5′-AGGTCA-3′ (3). Previous studies have demonstrated the importance of linking an extracellular signaling event to the activation of TFs. For example, assigning Forkhead box (Fox) P3 to a signaling module that is crucial for regulatory T-cell development has accelerated our understanding of signal transductions and gene functions (4).The abundance of TFs in the cells is currently inferred from mRNA profiling. Yang et al. identified 45 of 49 known NRs from several tissues in mice and linked NR expression to the circadian clock (5). Bookout et al. surveyed the expression of al...

The current in-depth proteomics makes use of long chromatography gradient to get access to more peptides for protein identification, resulting in covering of as many as 8000 mammalian gene products in 3 days of mass spectrometer running time. Here we report a fast sequencing (Fast-seq) workflow of the use of dual reverse phase high performance liquid chromatography -mass spectrometry (HPLC-MS) with a short gradient to achieve the same proteome coverage in 0.5 day. We adapted this workflow to a quantitative version (Fast quantification, Fast-quan) that was compatible to large-scale protein quantification. We subjected two identical samples to the Fast-quan workflow, which allowed us to systematically evaluate different parameters that impact the sensitivity and accuracy of the workflow. Using the statistics of significant test, we unraveled the existence of substantial falsely quantified differential proteins and estimated correlation of false quantification rate and parameters that are applied in label-free quantification. We optimized the setting of parameters that may substantially minimize the rate of falsely quantified differential proteins, and further applied them on a real biological process. With improved efficiency and throughput, we expect that the Fast-seq/Fast-quan workflow, allowing pair wise comparison of two proteomes in 1 day may make MS available to the masses and impact biomedical research in a positive way. Molecular & Cellular Proteomics 12: 10.1074/mcp.M112.025023, 2370-2380, 2013.The performance of mass spectrometry has been improved tremendously over the last few years (1-3), making mass spectrometry-based proteomics a viable approach for largescale protein analysis in biological research. Scientists around the world are striving to fulfill the promise of identifying and quantifying almost all gene products expressed in a cell line or tissue. This would make mass spectrometry-based protein analysis an approach that is compatible to the second-generation mRNA deep-seq technique (4, 5).Two liquid chromatography (LC)-MS strategies have been employed to achieve deep proteome coverage. One is a single run with a long chromatography column and gradient to take advantage of the resolving power of HPLC to reduce the complexity of peptide mixtures; the other is a sequential run with two-dimensional separation (typically ion-exchange and reverse phase) to reduce peptide complexity. It was reported by two laboratories that 2761 and 4500 proteins were identified with a 10 h chromatography gradient on a dual pressure linear ion-trap orbitrap mass spectrometer (LTQ Orbitrap Velos)(6 -8). Similarly, 3734 proteins were identified using a 8 h gradient on a 2 m long column with a hybrid triple quadrupole -time of flight (Q-TOF, AB sciex 5600 Q-TOF)(9) mass spectrometer. The two-dimensional approach has yielded more identification with longer time. For example, 10,006 proteins (representing over 9000 gene products, GPs) 1 were identified in U2OS cell (10), and 10,255 proteins (representing 9207 GPs) from HeL...

Transcription factors (TFs) drive various biological processes ranging from embryonic development to carcinogenesis. Here, we employ a recently developed concatenated tandem array of consensus TF response elements (catTFRE) approach to profile the activated TFs in 24 adult and 8 fetal mouse tissues on proteome scale. A total of 941 TFs are quantitatively identified, representing over 60% of the TFs in the mouse genome. Using an integrated omics approach, we present a TF network in the major organs of the mouse, allowing data mining and generating knowledge to elucidate the roles of TFs in various biological processes, including tissue type maintenance and determining the general features of a physiological system. This study provides a landscape of TFs in mouse tissues that can be used to elucidate transcriptional regulatory specificity and programming and as a baseline that may facilitate understanding diseases that are regulated by TFs.

Parenchymatous organs consist of multiple cell types, primarily defined as parenchymal cells (PCs) and nonparenchymal cells (NPCs). The cellular characteristics of these organs are not well understood. Proteomic studies facilitate the resolution of the molecular details of different cell types in organs. These studies have significantly extended our knowledge about organogenesis and organ cellular composition. Here, we present an atlas of the cell-type-resolved liver proteome. In-depth proteomics identified 6000 to 8000 gene products (GPs) for each cell type and a total of 10,075 GPs for four cell types. This data set revealed features of the cellular composition of the liver: (1) hepatocytes (PCs) express the least GPs, have a unique but highly homogenous proteome pattern, and execute fundamental liver functions; (2) the division of labor among PCs and NPCs follows a model in which PCs make the main components of pathways, but NPCs trigger the pathways; and (3) crosstalk among NPCs and PCs maintains the PC phenotype. This study presents the liver proteome at cell resolution, serving as a research model for dissecting the cell type constitution and organ features at the molecular level.

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Acquisition of drug-resistant phenotypes is often associated with chemotherapy in osteosarcoma. Studies show that high-mobility group box 1 (HMGB1) plays an important role in facilitating autophagy and promotes drug resistance in osteosarcoma cells. In this study, we determined the targeting role of miR-22 to HMGB1 and the regulation of miR-22 on the HMGB1-mediated cell autophagy and on the cell proliferation, migration, and invasion of osteosarcoma cells. Results demonstrated that miR-22 well paired with the 3'-UTR of HMGB1 downregulated the HMGB1 expression and blocked the HMGB1-mediated autophagy during chemotherapy in osteosarcoma cells in vitro. Further study showed that the blockage of autophagy by miR-22 inhibited the osteosarcoma cell proliferation, migration, and invasion. In summary, this study implied the negative regulation of miR-22 on the HMGB1-mediated autophagy in osteosarcoma cells.