Proteomics of TGF-beta signaling and its impact on breast cancer

Bioinformatics analysis of prognosis-related long non-coding RNAs in invasive breast carcinoma

Breast cancer is one of the most common types of cancer among women worldwide and needs more sensitive prognostic biomarkers to improve its treatment. In the present study, differentially expressed long non-coding RNAs (lncRNAs) in invasive breast carcinoma from The Cancer Genome Atlas and cBioPortal database were investigated, identifying 292 differentially expressed lncRNAs in 1,100 cases. By analyzing the overall survival rate, 10 lncRNAs were significantly correlated with poor prognosis. To explore the underlying molecular mechanisms of the 10 prognosis-related lncRNAs, bioinformatic methods were used to predict the potential target miRNAs, mRNAs and proteins, and to construct a lncRNA-miRNA-mRNA regulatory network and lncRNA-protein interaction network. Finally, the functions of the target genes and proteins were insvestigated using Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analyses. The results showed that these 10 lncRNAs could be novel prognostic markers for invasive breast carcinoma and the present study aimed to provide novel insight into the diagnosis and treatment of breast cancer.

Differential Proteome Analysis Identifies TGF-β-Related Pro-Metastatic Proteins in a 4T1 Murine Breast Cancer Model

Transforming growth factor-β (TGF-β) has a dual role in tumorigenesis, acting as either a tumor suppressor or as a pro-oncogenic factor in a context-dependent manner. Although TGF-β antagonists have been proposed as anti-metastatic therapies for patients with advanced stage cancer, how TGF-β mediates metastasis-promoting effects is poorly understood. Establishment of TGF-β-related protein expression signatures at the metastatic site could provide new mechanistic information and potentially allow identification of novel biomarkers for clinical intervention to discriminate TGF-β oncogenic effects from tumor suppressive effects. In the present study, we found that systemic administration of the TGF-β receptor kinase inhibitor, SB-431542, significantly inhibited lung metastasis from transplanted 4T1 mammary tumors in Balb/c mice. The differentially expressed proteins in the comparison of lung metastases from SB-431542 treated and control vehicle-treated groups were analyzed by a quantitative LTQ Orbitrap Velos system coupled with stable isotope dimethyl labeling. A total of 36,239 peptides from 6,694 proteins were identified, out of which 4,531 proteins were characterized as differentially expressed. A subset of upregulated proteins in the control group was validated by western blotting and immunohistochemistry. The eukaryotic initiation factor (eIF) family members constituted the most enriched protein pathway in vehicle-treated compared with SB-43512-treated lung metastases, suggesting that increased protein expression of specific eIF family members, especially eIF4A1 and eEF2, is related to the metastatic phenotype of advanced breast cancer and can be down-regulated by TGF-β pathway inhibitors. Thus our proteomic approach identified eIF pathway proteins as novel potential mediators of TGF-β tumor-promoting activity.

Web Services for Bioinformatics

A large number of tools are available to Bioinformaticians to analyze the rapidly growing databanks of molecular biological data. These databanks represent complex biological systems and in order to understand them, it is often necessary to link many disparate data sets and use more than one analysis tool. However, owing to the lack of standards for data sets and the interfaces of the tools this is not a trivial task. Over the past few years, web services has become a popular way of sharing the data and tools distributed over the web and used by different researchers all over the globe. In this chapter we discuss the interoperability problem of databanks and tools and how web services are being used to try to solve it. These efforts have resulted in the evolution of web services tools from HTML/web form-based tools not suited for automatic workflow generation to advances in Semantic Web and Ontologies that have revolutionized the role of semantics. Also included is a discussion on two extensively used Web Service systems for Life Sciences, myGrid and Semantic-MOBY. In the end we discuss how the state-of-art research and technological development in Semantic Web, Ontology and Database Management can help address these issues.

Omics-based molecular target and biomarker identification

Genomic, proteomic, and other omic-based approaches are now broadly used in biomedical research to facilitate the understanding of disease mechanisms and identification of molecular targets and biomarkers for therapeutic and diagnostic development. While the Omics technologies and bioinformatics tools for analyzing Omics data are rapidly advancing, the functional analysis and interpretation of the data remain challenging due to the inherent nature of the generally long workflows of Omics experiments. We adopt a strategy that emphasizes the use of curated knowledge resources coupled with expert-guided examination and interpretation of Omics data for the selection of potential molecular targets. We describe a downstream workflow and procedures for functional analysis that focus on biological pathways, from which molecular targets can be derived and proposed for experimental validation.

Proteomics in cancer

Proteomic studies have generated numerous datasets of potential diagnostic, prognostic, and therapeutic significance in human cancer. Two key technologies underpinning these studies in cancer tissue are two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) and mass spectrometry (MS). Although surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF)-MS is the mainstay for serum or plasma analysis, other methods including isotope-coded affinity tag technology, reverse-phase protein arrays, and antibody microarrays are emerging as alternative proteomic technologies. Because there is little overlap between studies conducted with these approaches, confirmation of these advanced technologies remains an elusive goal. This problem is further exacerbated by lack of uniform patient inclusion and exclusion criteria, low patient numbers, poor supporting clinical data, absence of standardized sample preparation, and limited analytical reproducibility (in particular of 2D-PAGE). Despite these problems, there is little doubt that the proteomic approach has the potential to identify novel diagnostic biomarkers in cancer. In therapeutic proteomics, the challenge is significant due to the complexity systems under investigation (i.e., cells generate over 10(5) different polypeptides). However, the most significant contribution of therapeutic proteomics research is expected to derive not from single experiments, but from the synthesis and comparison of large datasets obtained under different conditions (e.g., normal, inflammation, cancer) and in different tissues and organs. Thus, standardized processes for storing and retrieving data obtained with different technologies by different research groups will have to be developed. Shifting the emphasis of cancer proteomics from technology development and data generation to careful study design, data organization, formatting, and mining is crucial to answer clinical questions in cancer research.

Interactome of transforming growth factor-beta type I receptor (TbetaRI): inhibition of TGFbeta signaling by Epac1

Transforming growth factor-beta (TGFbeta) is a potent regulator of cell growth, differentiation, and apoptosis. Type I TGFbeta receptor (TbetaRI) is the key receptor for initiation of intracellular signaling by TGFbeta. Here we report proteomics-based identification of proteins that form a complex with TbetaRI. Using 2D-GE and MALDI TOF mass spectrometry, we identified 16 proteins that specifically interacted with a GST-fused TbetaRI Thr204Asp construct with constitutively active serine/threonine kinase. We confirmed interactions of the receptor with cAMP regulated guanine nucleotide exchange factor 1 (Epac1), beta-spectrin, PIASy, and beta-catenin proteins using immunoblotting. Interaction of the receptor with Epac1 required intact kinase activity of TbetaRI but was not affected by deletion of cAMP-binding domain of Epac1. TGFbeta1-induced C-terminal phosphorylation of Smad2 was inhibited in vivo and in vitro in the presence of Epac1. Epac1 inhibited also TGFbeta1/TbetaRI-dependent transcriptional activation, as evaluated by luciferase reporter assays. We observed that expression of Epac1 counteracted TGFbeta/TbetaRI-dependent decrease of cell adhesion and TGFbeta/TbetaRI-induced stimulation of cell migration. Thus, we have reported novel TRI-interacting proteins and have shown that Epac1 inhibited TGFbeta-dependent regulation of cell migration and adhesion.

Contribution of oncoproteomics to cancer biomarker discovery

Oncoproteomics is the study of proteins and their interactions in a cancer cell by proteomic technologies. Proteomic research first came to the fore with the introduction of two-dimensional gel electrophoresis. At the turn of the century, proteomics has been increasingly applied to cancer research with the wide-spread introduction of mass spectrometry and proteinchip. There is an intense interest in applying proteomics to foster an improved understanding of cancer pathogenesis, develop new tumor biomarkers for diagnosis, and early detection using proteomic portrait of samples. Oncoproteomics has the potential to revolutionize clinical practice, including cancer diagnosis and screening based on proteomic platforms as a complement to histopathology, individualized selection of therapeutic combinations that target the entire cancer-specific protein network, real-time assessment of therapeutic efficacy and toxicity, and rational modulation of therapy based on changes in the cancer protein network associated with prognosis and drug resistance. Besides, oncoproteomics is also applied to the discovery of new therapeutic targets and to the study of drug effects. In pace with the successful completion of the Human Genome Project, the wave of proteomics has raised the curtain on the postgenome era. The study of oncoproteomics provides mankind with a better understanding of neoplasia. In this article, the discovery of cancer biomarkers in recent years is reviewed. The challenges ahead and perspectives of oncoproteomics for biomarkers development are also addressed. With a wealth of information that can be applied to a broad spectrum of biomarker research projects, this review serves as a reference for biomarker researchers, scientists working in proteomics and bioinformatics, oncologists, pharmaceutical scientists, biochemists, biologists, and chemists.

Glycoproteome profiling of transforming growth factor-β (TGFβ) signaling: Nonglycosylated cell death-inducing DFF-like effector A inhibits TGFβ1-dependent apoptosis

Transforming growth factor-beta (TGFbeta) is a potent regulator of cell growth, differentiation, and apoptosis. TGFbeta binds to specific serine/threonine kinase receptors, which leads to activation of Smad-dependent and Smad-independent signaling pathways. O-Glycosylation is a dynamic PTM which has been observed in many regulatory proteins, but has not been studied in the context of TGFbeta signaling. To explore the effect of TGFbeta1 on protein O-glycosylation in human breast epithelial cells, we performed analyses of proteins which were affinity purified with Helix pomatia agglutinin (HPA). HPA lectin allowed enrichment of proteins containing GalNAc and GlcNAc linked to serine and threonine residues. Using 2-DE and MALDI-TOF-MS, we identified 21 HPA-precipitated proteins, which were affected by treatment of cells with TGFbeta1. Among these proteins, regulators of cell survival, apoptosis, trafficking, and RNA processing were identified. We found that TGFbeta1 inhibited the appearance of cell death-inducing DFF-like effector A (CIDE-A) in 2-D gels with HPA-precipitated proteins. CIDE-A is a cell death activator which promotes DNA fragmentation. We observed that TGFbeta1 did not affect expression of CIDE-A, but inhibited its glycosylation. We found that deglycosylation of CIDE-A correlated with enhanced nuclear export of the protein, and that high level of nonglycosylated CIDE-A inhibited TGFbeta1-dependent cell death. Thus, inhibition of the glycosylation of CIDE-A may be a mechanism to protect cells from apoptosis.