The Tang Laboratory
Proteomics contributes to a better understanding of the molecular basis of diseases at the systems biology level, and has the potential to identify new therapeutic targets and potential diagnostic and prognostic biomarkers. The Tang laboratory collaborates with other researchers using state-of-the-art high-resolution mass spectrometry and related experimental strategies to investigate proteome changes and protein posttranslational modifications associated with cancers, such as melanoma, prostate, and ovarian cancers, and other diseases. For some studies, proteomics results are combined with other omics technologies – particularly metabolomics and lipidomics – to provide a more complete picture of the molecular contributors to diseases.
The Tang laboratory is actively involved in multiple collaborative projects that focus on defining disease-related cellular mechanisms and discovering therapeutic targets of diseases. These projects involve diverse experimental strategies that include global proteome profiling, quantification of disease biomarkers, characterization of protein post-translational modifications, identification of protein interactomes, and global polar metabolite and lipid profiling. Results from these analyses have provided insights into mechanisms underlying different cancers such as melanoma, prostate cancer, and ovarian cancer, as well as identified putative biomarkers for disease states.
The Tang laboratory is also involved in improving proteomics technologies in key areas including:
- Chemical crosslink-MS. Chemical crosslinking combined with mass spectrometry (MS) analysis is a powerful method to study protein-protein interaction networks and to obtain valuable structural information from protein complexes. Both traditional non-cleavable and MS-cleavable cross-linkers can be used for identification of protein-protein interaction sites, but MS-cleavable crosslinkers are advantageous because of their ability to generate distinguishing fragment ions during MS/MS that greatly improve identification of crosslinked peptides and crosslinked sites. These diagnostic fragment ions will also reduce the search space during data analysis, allowing the crosslinkers to be used in whole proteome labeling studies. We are interested in developing a robust and reliable workflow for efficient identification of crosslinked sites on proteins using MS-cleavable cross-linkers, such as DSSO and DSBU.
- MS-based glycomics and glycoproteomics. Glycosylation is one of the most abundant post-translational modifications (PTMs) in mammalian cells and is crucial for a wide range of biofunctions. Aberrant glycosylation of proteins has been linked to various diseases, including cancers. The major strength of MS-based analyses is the isolation and fragmentation of analytes to obtain structural information. MS-based glycomics typically consists of the following steps: glycan release by either PNGase F treatment (for N-linked glycans) or β-elimination (for O-linked glycans), glycan enrichment using solid phase separation techniques, glycan derivatization, and LC-MS/MS identification. Global profiling of released glycans has been used to distinguish healthy and disease states. However, information on the protein carriers of glycans and residue site localization is lost after glycan release. The more powerful MS-based glycoproteomics approach that we plan to focus on involves structural analysis of glycopeptides. Protein extracts are proteolytically digested followed by glycopeptide enrichment with subsequent LC-MS/MS analysis. Since the glycan is left intact on the peptide, this method allows the identification of the glycosylated proteins and quantitation of the glycan structures on the glycoproteins.
Fanem, M.E., Chhabra, Y., Alicea, G.M., Maranto, D.A., Douglass, S.M., Webster, M.R. Rebecca, V.W., Marino, G.E., Almeida, F., et al. “Stromal Changes in the Aged Lung Induce an Emergence From Melanoma Dormancy.” Nature. 2022 Jun;606(7913):396-405. doi: 10.1038/s41586-022-04774-2. Epub 2022 Jun 1.
Yan, Q., Wulfridge, P., Doherty, J., Fernandez-Luna, J.L., Real, P.J., Tang, HY., Sarma, K. “Proximity Labeling Identifies a Repertoire of Site-specific R-loop Modulators.” Nat Commun. 2022 Jan 10;13(1):53. doi: 10.1038/s41467-021-27722-6.
Agarwal, E., Goldman, A.R., Tang, HY., Kossenkov, A.V., Ghosh, J.C., Languino, L.R., Vaira, V., Speicher, D.W., Altieri, D.C. “A Cancer Ubiquitome Landscape Identifies Metabolic Reprogramming as Target of Parkin Tumor Suppression.” Sci Adv. 2021 Aug 25;7(35):eabg7287. doi: 10.1126/sciadv.abg7287. Print 2021 Aug.
Tang, HY., Goldman, A.R., Zhang, X., Speicher, D.W., Dang, C.V. “Measuring MYC-Mediated Metabolism in Tumorigenesis.” Methods Mol Biol. 2021;2318:231-239. doi: 10.1007/978-1-0716-1476-1_11.
Wu, S., Fukumoto, T., Lin, J., Nacarelli, T., Wang, Y., Ong, D., Liu, H., Fatkhutdinov, N., Zundell, J.A., Karakashev, S., Zhou, W., et al. “Targeting Glutamine Dependence Through GLS1 Inhibition Suppresses ARID1A-inactivated Clear Cell Ovarian Carcinoma.” Nat Cancer. 2021 Feb;2(2):189-200. doi: 10.1038/s43018-020-00160-x. Epub 2021 Jan 11.
David W. Speicher, Ph.D.
Professor and Program Co-Leader, Molecular & Cellular Oncogenesis Program, Ellen and Ronald Caplan Cancer Center
Director, Center for Systems & Computational Biology
Member, The Wistar Institute Melanoma Research Center
Scientific Director, Proteomics & Metabolomics Facility