Fusion protein based on Grb2-SH2 domain for cancer therapy

Anti-c-MET Fab-Grb2-Gab1 Fusion Protein-Mediated Interference of c-MET Signaling Pathway Induces Methuosis in Tumor Cells

Bio-macromolecules have potential applications in cancer treatment due to their high selectivity and efficiency in hitting therapeutic targets. However, poor cell membrane permeability has limited their broad-spectrum application in cancer treatment. The current study developed highly internalizable anti-c-MET antibody Fab fusion proteins with intracellular epitope peptide chimera to achieve the dual intervention from the extracellular to intracellular targets in tumor therapy. In vitro experiments demonstrated that the fusion proteins could interfere with the disease-associated intracellular signaling pathways and inhibit the uncontrolled proliferation of tumor cells. Importantly, investigation of the underlying mechanism revealed that these protein chimeras could induce vacuolation in treated cells, thus interfering with the normal extension and arrangement of microtubules as well as the mitosis, leading to the induction of methuosis-mediated cell death. Furthermore, in vivo tumor models indicated that certain doses of fusion proteins could inhibit the A549 xenograft tumors in NOD SCID mice. This study thus provides new ideas for the intracellular delivery of bio-macromolecules and the dual intervention against tumor cell signaling pathways.

Recombinant fusion proteins FPTD-Grb2-SH2 and FPTD-Grb2-SH2M inhibit the proliferation of breast cancer cells in vitro

Growth factor receptor bound protein 2 (Grb2) is a key adaptor performing a principal role in the oncogenic Ras signaling pathway. In the present study, we generated two fusion proteins. One contained an Src homology 2 (SH2) domain of Grb2, a signal peptide sequence, FLAG-tag sequence, PTD region and we named it FPTD-Grb2-SH2, while the other contained one mutant SH2 domain, added to a signal peptide sequence, FLAG-tag sequence, PTD region and we named it FPTD-Grb2-SH2M. Western blot analysis and immunofluorescence assay were used to investigate the expression and location of the fusion proteins in breast cancer cells. The proliferation and migration of the cells were estimated by MTT and Transwell cell migration assays, respectively. Flow cytometric analysis was performed to evaluate the apoptosis of the breast cancer cells. The recombinant proteins FPTD-Grb2-SH2 and FPTD-Grb2-SH2M were successfully expressed in the breast cancer cell lines regardless of HER2-phenotype, and they suppressed breast cancer cell growth and migration as expected from the lack of SH3 domain. Both FPTD-Grb2-SH2 and FPTD-Grb2-SH2M exhibited significant toxicity to breast cancer cells. The present study demonstrated that the recombinant proteins FPTD-Grb2-SH2 and FPTD-Grb2-SH2M may be used for anticancer drug development.

Genome scale modeling in systems biology: algorithms and resources

In recent years, in silico studies and trial simulations have complemented experimental procedures. A model is a description of a system, and a system is any collection of interrelated objects; an object, moreover, is some elemental unit upon which observations can be made but whose internal structure either does not exist or is ignored. Therefore, any network analysis approach is critical for successful quantitative modeling of biological systems. This review highlights some of most popular and important modeling algorithms, tools, and emerging standards for representing, simulating and analyzing cellular networks in five sections. Also, we try to show these concepts by means of simple example and proper images and graphs. Overall, systems biology aims for a holistic description and understanding of biological processes by an integration of analytical experimental approaches along with synthetic computational models. In fact, biological networks have been developed as a platform for integrating information from high to low-throughput experiments for the analysis of biological systems. We provide an overview of all processes used in modeling and simulating biological networks in such a way that they can become easily understandable for researchers with both biological and mathematical backgrounds. Consequently, given the complexity of generated experimental data and cellular networks, it is no surprise that researchers have turned to computer simulation and the development of more theory-based approaches to augment and assist in the development of a fully quantitative understanding of cellular dynamics.

Phosphotyrosine recognition domains: the typical, the atypical and the versatile

SH2 domains are long known prominent players in the field of phosphotyrosine recognition within signaling protein networks. However, over the years they have been joined by an increasing number of other protein domain families that can, at least with some of their members, also recognise pTyr residues in a sequence-specific context. This superfamily of pTyr recognition modules, which includes substantial fractions of the PTB domains, as well as much smaller, or even single member fractions like the HYB domain, the PKCδ and PKCθ C2 domains and RKIP, represents a fascinating, medically relevant and hence intensely studied part of the cellular signaling architecture of metazoans. Protein tyrosine phosphorylation clearly serves a plethora of functions and pTyr recognition domains are used in a similarly wide range of interaction modes, which encompass, for example, partner protein switching, tandem recognition functionalities and the interaction with catalytically active protein domains. If looked upon closely enough, virtually no pTyr recognition and regulation event is an exact mirror image of another one in the same cell. Thus, the more we learn about the biology and ultrastructural details of pTyr recognition domains, the more does it become apparent that nature cleverly combines and varies a few basic principles to generate a sheer endless number of sophisticated and highly effective recognition/regulation events that are, under normal conditions, elegantly orchestrated in time and space. This knowledge is also valuable when exploring pTyr reader domains as diagnostic tools, drug targets or therapeutic reagents to combat human diseases.

Modeling of tumor progression in NSCLC and intrinsic resistance to TKI in loss of PTEN expression

EGFR signaling plays a very important role in NSCLC. It activates Ras/ERK, PI3K/Akt and STAT activation pathways. These are the main pathways for cell proliferation and survival. We have developed two mathematical models to relate to the different EGFR signaling in NSCLC and normal cells in the presence or absence of EGFR and PTEN mutations. The dynamics of downstream signaling pathways vary in the disease state and activation of some factors can be indicative of drug resistance. Our simulation denotes the effect of EGFR mutations and increased expression of certain factors in NSCLC EGFR signaling on each of the three pathways where levels of pERK, pSTAT and pAkt are increased. Over activation of ERK, Akt and STAT3 which are the main cell proliferation and survival factors act as promoting factors for tumor progression in NSCLC. In case of loss of PTEN, Akt activity level is considerably increased. Our simulation results show that in the presence of erlotinib, downstream factors i.e. pAkt, pSTAT3 and pERK are inhibited. However, in case of loss of PTEN expression in the presence of erlotinib, pAkt level would not decrease which demonstrates that these cells are resistant to erlotinib.