Tropomyosin1 isoforms underlie epithelial to mesenchymal plasticity, metastatic dissemination, and resistance to chemotherapy in high-grade serous ovarian cancer.

Phenotypic plasticity, defined as the ability of individual cells with stable genotypes to exert different phenotypes upon exposure to specific environmental cues, represent the quintessential hallmark of the cancer cell en route from the primary lesion to distant organ sites where metastatic colonization will occur. Phenotypic plasticity is driven by a broad spectrum of epigenetic mechanisms that allow for the reversibility of epithelial-to-mesenchymal and mesenchymal-to-epithelial transitions (EMT/MET). By taking advantage of the co-existence of epithelial and quasi-mesenchymal cells within immortalized cancer cell lines, we have analyzed the role of EMT-related gene isoforms in the regulation of epithelial mesenchymal plasticity (EMP) in high grade serous ovarian cancer. When compared with colon cancer, a distinct spectrum of downstream targets characterizes quasi-mesenchymal ovarian cancer cells, likely to reflect the different modalities of metastasis formation between these two types of malignancy, i.e. hematogenous in colon and transcoelomic in ovarian cancer. Moreover, upstream RNA-binding proteins differentially expressed between epithelial and quasi-mesenchymal subpopulations of ovarian cancer cells were identified that underlie differential regulation of EMT-related isoforms. In particular, the up- and down-regulation of RBM24 and ESRP1, respectively, represent a main regulator of EMT in ovarian cancer cells. To validate the functional and clinical relevance of our approach, we selected and functionally analyzed the Tropomyosin 1 gene (TPM1), encoding for a protein that specifies the functional characteristics of individual actin filaments in contractile cells, among the ovarian-specific downstream AS targets. The low-molecular weight Tpm1.8/9 isoforms are specifically expressed in patient-derived ascites and promote invasion through activation of EMT and Wnt signaling, together with a broad spectrum of inflammation-related pathways. Moreover, Tpm1.8/9 expression confers resistance to taxane- and platinum-based chemotherapy. Small molecule inhibitors that target the Tpm1 isoforms support targeting Tpm1.8/9 as therapeutic targets for the development of future tailor-made clinical interventions.

Computational Prediction of the Epitopes of HA1 Protein of Influenza Viruses to its Neutralizing Antibodies.

In this work, we have used a new method to predict the epitopes of HA1 protein of influenza virus to several antibodies HC19, CR9114, BH151 and 4F5. While our results reproduced the binding epitopes of H3N2 or H5N1 for the neutralizing antibodies HC19, CR9114, and BH151 as revealed from the available crystal structures, additional epitopes for these antibodies were also suggested. Moreover, the predicted epitopes of H5N1 HA1 for the newly developed antibody 4F5 are located at the receptor binding domain, while previous study identified a region 76-WLLGNP-81 as the epitope. The possibility of antibody recognition of influenza virus via different mechanism by binding to different epitopes of an antigen is also discussed.

Computational Identification of Antibody Epitopes on the Dengue Virus NS1 Protein.

We have previously described a method to predict antigenic epitopes on proteins recognized by specific antibodies. Here we have applied this method to identify epitopes on the NS1 proteins of the four Dengue virus serotypes (DENV1-4) that are bound by a small panel of monoclonal antibodies 1H7.4, 1G5.3 and Gus2. Several epitope regions were predicted for these antibodies and these were found to reflect the experimentally observed reactivities. The known binding epitopes on DENV2 for the antibodies 1H7.4 and 1G5.3 were identified, revealing the reasons for the serotype specificity of 1H7.4 and 1G5.3, and the non-selectivity of Gus2. As DENV NS1 is critical for virus replication and a key vaccine candidate, epitope prediction will be valuable in designing appropriate vaccine control strategies. The ability to predict potential epitopes by computational methods significantly reduces the amount of experimental work required to screen peptide libraries for epitope mapping.

Antibody recognition of Shiga toxins (Stxs): computational identification of the epitopes of Stx2 subunit A to the antibodies 11E10 and S2C4.

We have recently developed a new method to predict the epitopes of the antigens that are recognized by a specific antibody. In this work, we applied the method to identify the epitopes of the Shiga toxin (Stx2 subunit A) that were bound by two specific antibodies 11E10 and S2C4. The predicted epitopes of Stx2 binding to the antibody 11E10 resembles the recognition surface constructed by the regions of Stx2 identified experimentally. For the S2C4, our results indicate that the antibody recognizes the Stx2 at two different regions on the protein surface. The first region (residues 246-254: ARSVRAVNE) is similar to the recognition region of the 11E10, while the second region is formed by two epitopes. The second region is particularly significant because it includes the amino acid sequence region that is diverse between Stx2 and other Stx (residues 176-188: QREFRQALSETAPV). This new recognition region is believed to play an important role in the experimentally observed selectivity of S2C4 to the Stx2.

Computational identification of epitopes in the glycoproteins of novel bunyavirus (SFTS virus) recognized by a human monoclonal antibody (MAb 4-5).

In this work, we have developed a new approach to predict the epitopes of antigens that are recognized by a specific antibody. Our method is based on the "multiple copy simultaneous search" (MCSS) approach which identifies optimal locations of small chemical functional groups on the surfaces of the antibody, and identifying sequence patterns of peptides that can bind to the surface of the antibody. The identified sequence patterns are then used to search the amino-acid sequence of the antigen protein. The approach was validated by reproducing the binding epitope of HIV gp120 envelop glycoprotein for the human neutralizing antibody as revealed in the available crystal structure. Our method was then applied to predict the epitopes of two glycoproteins of a newly discovered bunyavirus recognized by an antibody named MAb 4-5. These predicted epitopes can be verified by experimental methods. We also discuss the involvement of different amino acids in the antigen-antibody recognition based on the distributions of MCSS minima of different functional groups.

Modeling the epidermal growth factor -- epidermal growth factor receptor l2 domain interaction: implications for the ligand binding process.

Signaling from the epidermal growth factor (EGF) receptor is triggered by the binding of ligands such as EGF or transforming growth factor alpha (TGF-alpha) and subsequent receptor dimerization. An understanding of these processes has been hindered by the lack of structural information about the ligand-bound, dimerized EGF receptor. Using an NMR-derived structure of EGF and a homology model of the major ligand binding domain of the EGF receptor and experimental data, we modeled the binding of EGF to this EGF receptor fragment. In this low resolution model of the complex, EGF sits across the second face of the EGF receptor L2 domain and EGF residues 10-16, 36-37, 40-47 bind to this face. The structural model is largely consistent with previously published NMR data describing the residues of TGF-alpha which interact strongly with the EGF receptor. Other EGF residues implicated in receptor binding are accounted by our proposal that the ligand binding is a two-step process with the EGF binding to at least one other site of the receptor. This three-dimensional model is expected to be useful in the design of ligand-based antagonists of the receptor.