Han Sun

Longikaurin A, a natural ent-kaurane, induces G2/M phase arrest via downregulation of Skp2 and apoptosis induction through ROS/JNK/c-Jun pathway in hepatocellular carcinoma cells

Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer, and is also highly resistant to conventional chemotherapy treatments. In this study, we report that Longikaurin A (LK-A), an ent-kaurane diterpenoid isolated from the plant Isodon ternifolius, induced cell cycle arrest and apoptosis in human HCC cell lines. LK-A also suppressed tumor growth in SMMC-7721 xenograft models, without inducing any notable major organ-related toxicity. LK-A treatment led to reduced expression of the proto-oncogene S phase kinase-associated protein 2 (Skp2) in SMMC-7721 cells. Lower Skp2 levels correlated with increased expression of p21 and p-cdc2 (Try15), and a corresponding decrease in protein levels of Cyclin B1 and cdc2. Overexpression of Skp2 significantly inhibited LK-A-induced cell cycle arrest in SMMC-7721 cells, suggesting that LK-A may target Skp2 to arrest cells at the G2/M phase. LK-A also induced reactive oxygen species (ROS) production and apoptosis in SMMC-7721 cells. LK-A induced phosphorylation of c-Jun N-terminal kinase (JNK), but not extracellular signal-regulated kinase and P38 MAP kinase. Treatment with, the JNK inhibitor SP600125 prevented LK-A-induced apoptosis in SMMC-7721 cells. Moreover, the antioxidant N-acetylcysteine prevented phosphorylation of both JNK and c-Jun. Taken together, these data indicate that LK-A induces cell cycle arrest and apoptosis in cancer cells by dampening Skp2 expression, and thereby activating the ROS/JNK/c-Jun signaling pathways. LK-A is therefore a potential lead compound for development of antitumor drugs targeting HCC.

SysPTM 2.0: an updated systematic resource for post-translational modification

Post-translational modifications (PTMs) of proteins play essential roles in almost all cellular processes, and are closely related to physiological activity and disease development of living organisms. The development of tandem mass spectrometry (MS/MS) has resulted in a rapid increase of PTMs identified on proteins from different species. The collection and systematic ordering of PTM data should provide invaluable information for understanding cellular processes and signaling pathways regulated by PTMs. For this original purpose we developed SysPTM, a systematic resource installed with comprehensive PTM data and a suite of web tools for annotation of PTMs in 2009. Four years later, there has been a significant advance with the generation of PTM data and, consequently, more sophisticated analysis requirements have to be met. Here we submit an updated version of SysPTM 2.0 (http://lifecenter.sgst.cn/SysPTM/), with almost doubled data content, enhanced web-based analysis tools of PTMBlast, PTMPathway, PTMPhylog, PTMCluster. Moreover, a new session SysPTM-H is constructed to graphically represent the combinatorial histone PTMs and dynamic regulation of histone modifying enzymes, and a new tool PTMGO is added for functional annotation and enrichment analysis. SysPTM 2.0 not only facilitates resourceful annotation of PTM sites but allows systematic investigation of PTM functions by the user. Citation details: Li,J., Jia,J., Li,H. et al. SysPTM 2.0: an updated systematic resource for post-translational modification. Database (2014) Vol. 2014: article ID bau025; doi:10.1093/database/bau025. Database URL: http://lifecenter.sgst.cn/SysPTM/

dbDEPC 2.0: updated database of differentially expressed proteins in human cancers

A large amount of differentially expressed proteins (DEPs) have been identified in various cancer proteomics experiments, curation and annotation of these proteins are important in deciphering their roles in oncogenesis and tumor progression, and may further help to discover potential protein biomarkers for clinical applications. In 2009, we published the first database of DEPs in human cancers (dbDEPCs). In this updated version of 2011, dbDEPC 2.0 has more than doubly expanded to over 4000 protein entries, curated from 331 experiments across 20 types of human cancers. This resource allows researchers to search whether their interested proteins have been reported changing in certain cancers, to compare their own proteomic discovery with previous studies, to picture selected protein expression heatmap across multiple cancers and to relate protein expression changes with aberrance in other genetic level. New important developments include addition of experiment design information, advanced filter tools for customer-specified analysis and a network analysis tool. We expect dbDEPC 2.0 to be a much more powerful tool than it was in its first release and can serve as reference to both proteomics and cancer researchers. dbDEPC 2.0 is available at http://lifecenter.sgst.cn/dbdepc/index.do.

Identification of gene fusions from human lung cancer mass spectrometry data

BackgroundTandem mass spectrometry (MS/MS) technology has been applied to identify proteins, as an ultimate approach to confirm the original genome annotation. To be able to identify gene fusion proteins, a special database containing peptides that cross over gene fusion breakpoints is needed.MethodsIt is impractical to construct a database that includes all possible fusion peptides originated from potential breakpoints. Focusing on 6259 reported and predicted gene fusion pairs from ChimerDB 2.0 and Cancer Gene Census, we for the first time created a database CanProFu that comprehensively annotates fusion peptides formed by exon-exon linkage between these pairing genes.ResultsApplying this database to mass spectrometry datasets of 40 human non-small cell lung cancer (NSCLC) samples and 39 normal lung samples with stringent searching criteria, we were able to identify 19 unique fusion peptides characterizing gene fusion events. Among them 11 gene fusion events were only found in NSCLC samples. And also, 4 alternative splicing events were characterized in cancerous or normal lung samples.ConclusionsThe database and workflow in this work can be flexibly applied to other MS/MS based human cancer experiments to detect gene fusions as potential disease biomarkers or drug targets.

The discovery of novel protein-coding features in mouse genome based on mass spectrometry data

Identifying protein-coding genes in eukaryotic genomes remains a challenge in post-genome era due to the complex gene models. We applied a proteogenomics strategy to detect un-annotated protein-coding regions in mouse genome. High-accuracy tandem mass spectrometry (MS/MS) data from diverse mouse samples were generated by LTQ-Orbitrap mass spectrometer in house. Two searchable diagnostic proteomic datasets were constructed, one with all possible encoding exon junctions, and the other with all putative encoding exons, for the discovery of novel exon splicing events and novel uninterrupted protein-coding regions. Altogether 29,586 unique peptides were identified. Aligning backwards to the mouse genome, the translation of 4471 annotated genes was validated by the known peptides; and 172 genic events were defined in mouse genome by the novel peptides. The approach in the current work can provide substantial evidences for eukaryote genome annotation in encoding genes.

Adenanthin targets peroxiredoxin I/II to kill hepatocellular carcinoma cells

Adenanthin, a natural diterpenoid isolated from the leaves of Isodon adenanthus, has recently been reported to induce leukemic cell differentiation by targeting peroxiredoxins (Prx) I and II. On the other hand, increasing lines of evidence propose that these Prx proteins would become potential targets to screen drugs for the prevention and treatment of solid tumors. Therefore, it is of significance to explore the potential activities of adenanthin on solid tumor cells. Here, we demonstrate that Prx I protein is essential for the survival of hepatocellular carcinoma (HCC) cells, and adenanthin can kill these malignant liver cells in vitro and xenografts. We also show that the cell death-inducing activity of adenanthin on HCC cells is mediated by the increased reactive oxygen species (ROS) levels. Furthermore, the silencing of Prx I or Prx II significantly enhances the cytotoxic activity of adenanthin on HCC, whereas the ectopic expression of Prx I and Prx II but not their mutants of adenanthin-bound cysteines can rescue adenanthin-induced cytotoxicity in Prxs-silenced HCC cells. Taken together, our results propose that adenanthin targets Prx I/II to kill HCC cells and its therapeutic significance warrants to be further explored in HCC patients.

Integration of mass spectrometry and RNA-Seq data to confirm human ab initio predicted genes and lncRNAs.

MS/MS has been used to improve genome annotation in various organisms. The classical approach is to construct comprehensive theoretical peptide database with six frame translation model from the whole ORF of a genome and search against this database with real MS/MS spectra. In this work we took a more focused approach, we constructed a database containing only peptides from the ab initio predicted genes from current human genome annotation, and all theoretical peptides from currently annotated lncRNAs, and searched such a database with MS/MS data from human Hela cell line. The purpose of this design is to find translation evidence for ab initio predicted genes and to rule out possible wrongly defined lncRNAs in a systematic proteogenomics effort. To validate proteogenomics results, we integrated RNA‐Seq data analysis for the same Hela cell line which generated MS/MS data, and performed MRM experiment on self‐cultured Hela cell line samples. Six peptides were found to support ab initio predicted genes with both RNA‐Seq and MRM validations, while none was found to support a translated lncRNA. This workflow could be flexibly applied to other human samples and datasets to help further improve human gene annotation.

Identification of HPV Integration and Gene Mutation in HeLa Cell Line by Integrated Analysis of RNA-Seq and MS/MS Data

HeLa cell line, which was derived from cervical carcinoma, provides an idea platform to study both the integration of human papillomavirus and the massive mutations occurring on the cancer cell genome. Proteogenomics is a field with the intersection of proteomics and genomics to perform gene annotation and identify gene mutation. In this work, we first identified the SNV/INDEL, structural variation (SV), and virus infection/integration events from RNA-Seq data of HeLa cell line; then, by applying proteogenomics strategy, we were able to detect some of the genomic events with the tandem mass spectrometry (MS/MS) data from the same sample. Furthermore, some of the mutated peptides were experimentally validated using multiple reaction monitoring technology. The integrated analysis of the RNA-Seq and MS/MS data not only renders the discovery of HeLa cell genome variations more credible but also illustrates a practical workflow for protein-coding mutation discovery in cancer-related studies.

Construction and Deciphering of Human Phosphorylation-Mediated Signaling Transduction Networks.

Protein phosphorylation is the most abundant reversible covalent modification. Human protein kinases participate in almost all biological pathways, and approximately half of the kinases are associated with disease. PhoSigNet was designed to store and display human phosphorylation-mediated signal transduction networks, with additional information related to cancer. It contains 11 976 experimentally validated directed edges and 216 871 phosphorylation sites. Moreover, 3491 differentially expressed proteins in human cancer from dbDEPC, 18 907 human cancer variation sites from CanProVar, and 388 hyperphosphorylation sites from PhosphoSitePlus were collected as annotation information. Compared with other phosphorylation-related databases, PhoSigNet not only takes the kinase-substrate regulatory relationship pairs into account, but also extends regulatory relationships up- and downstream (e.g., from ligand to receptor, from G protein to kinase, and from transcription factor to targets). Furthermore, PhoSigNet allows the user to investigate the impact of phosphorylation modifications on cancer. By using one set of in-house time series phosphoproteomics data, the reconstruction of a conditional and dynamic phosphorylation-mediated signaling network was exemplified. We expect PhoSigNet to be a useful database and analysis platform benefiting both proteomics and cancer studies.

Phosphoproteomics study on the activated PKCδ-induced cell death.

The proteolytic activation of protein kinase Cδ (PKCδ) generates a catalytic fragment called PKCδ-CF, which induces cell death. However, the mechanisms underlying PKCδ-CF-mediated cell death are largely unknown. On the basis of an engineering leukemic cell line with inducible expression of PKCδ-CF, here we employ SILAC-based quantitative phosphoproteomics to systematically and dynamically investigate the overall phosphorylation events during cell death triggered by PKCδ-CF expression. Totally, 3000 phosphorylation sites were analyzed. Considering the fact that early responses to PKCδ-CF expression initiate cell death, we sought to identify pathways possibly related directly with PKCδ by further analyzing the data set of phosphorylation events that occur in the initiation stage of cell death. Interacting analysis of this data set indicates that PKCδ-CF triggers complicated networks to initiate cell death, and motif analysis and biochemistry verification reveal that several kinases in the downstream of PKCδ conduct these networks. By analysis of the specific sequence motif of kinase-substrate, we also find 59 candidate substrates of PKCδ from the up-regulated phosphopeptides, of which 12 were randomly selected for in vitro kinase assay and 9 were consequently verified as substrates of PKCδ. To our greatest understanding, this study provides the most systematic analysis of phosphorylation events initiated by the cleaved activated PKCδ, which would vastly extend the profound understanding of PKCδ-directed signal pathways in cell death. The MS data have been deposited to the ProteomeXchange with identifier PXD000225.