Browsing by Subject "Multivalency"

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    Chemically self-assembled antibody nanorings (CSANS): Design and characterization of an anti-CD3 IGM biomimetic.
    (2010-10) Li, Qing
    Based on our development of a highly efficient protocol for the chemically controlled self-assembling of protein nanorings, we have sought to exploit our methodology for engineering multivalent chemically self-assembled antibody-nanorings (CSANs) for tissue imaging and drug delivery. Two novel DHFR-DHFR-anti-CD3 scFv fusion proteins were constructed (13DDantiCD3 and 1DDantiCD3). In addition, the two DHFR cysteines were mutated to either alanine or serine to enhance correct folding. The protein was expressed in BL21 (DE3) cells, renatured with the SLS-based refolding protocol and purified by methotrexate affinity chromatography. Incubation of 13DDantiCD3 with the chemical dimerizer, bisMTX, resulted in almost exclusive formation of the bivalent CSANs, while incubation with 1DDantiCD3 resulted in formation of octavalent CSANs. Both antibody nanorings selectively blocked the killing of the CD3+ human T-leukemia HPB-MLT by a diphtheria-anti-CD3 immunotoxin. FACS analysis revealed nearly identical dissociation constants for both the selfassembled and parental monoclonal antibody and a 3-fold lower K d for the octavalent species. The chemically dimerized scFv's were shown to be stable in cell culture at 37°C and the dimerization was shown to be reversible by the addition of excess amounts of the non-toxic FDA approved DHFR antagonist trimethoprim. We also demonstrate that, similar to the parental bivalent anti-CD3 monoclonal antibody (MAB), anti-CD3 CSANs selectively bind to CD3+ leukemia cells, and undergo rapid internalization through a caveolin-independent pathway that requires cholesterol, actin polymerization and protein tyrosine kinase activation. While treatment with the monoclonal antibody leads to T-cell activation and nearly complete loss (i.e. 90%) of surface displayed T-cell receptor (TCR), only 25-30% of the TCR down regulate and no significant T-cell proliferation is observed after treatment of peripheral blood mononuclear cells (PBMCs) with anti-CD3 CSANs. Consistent with the proliferation findings, 15-25% less CD25 (IL-2 receptor) was found on the surface of PBMCs treated with either the polyvalent or bivalent anti-CD3 CSANs, respectively, than on PBMCs treated with the parental MAB. Comparative experiments with F(ab')2 derived from the MAB confirm that the activation of the T-cells by the MAB is dependent on the Fc domain, and thus interactions of the PBMC T-cells with accessory cells, such as macrophages. Taken together, our results demonstrate that anti-CD3 CSANs with valencies ranging from 2 to 8 could be employed for radionuclide, drug or potentially oligonucleotide delivery to T-cells without, as has been observed for other antibody conjugated nanoparticles, the deleterious affects of activation observed for MAB. Further the CSAN construct may be adapted for the preparation of other multivalent scFvs.
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    Deciphering Heterogeneities in Cellular Decision Making
    (2023-12) Kang, Jennifer
    This thesis presents a multifaceted exploration into the complexities of cellular decision making, integrating experimental, computational, and engineering approaches to unravel the intricacies of heterogeneous cellular responses to noisy and conflicting extracellular cues. At the core of this research lies the investigation of how cells resolve conflicts in signaling, particularly when exposed to competing stimuli, and how this understanding can be harnessed to engineer novel therapeutic strategies.In the first segment of our study, we explore the intricate dynamics of cellular signaling and conflict resolution using rat pheochromocytoma (PC12) cells as a model. Challenging the traditional understanding, our research uncovers that both low and high concentrations of epidermal growth factor (EGF) can induce sustained extracellular signal-regulated kinase (ERK) activation, propelling cells towards differentiation, a role previously attributed exclusively to nerve growth factor (NGF). This is a significant deviation from the established paradigm where EGF is known to drive proliferation through transient ERK activation, and NGF is associated with sustained ERK activation leading to differentiation. Our findings are bolstered by both population-level and single-cell measurements. To further elucidate this phenomenon, we employed an enhanced mechanistic model. This model reveals a critical aspect: attenuated EGF receptor degradation due to reduced receptor phosphorylation is a key factor driving the sustained ERK activity at both low and high EGF concentrations. Moving beyond the qualitative labels of 'transient' and 'sustained', the model introduces a quantitative metric for ERK activation – its time integral. This metric, governed by receptor-ligand binding and degradation rates, is predictive of cell fate in cultures co-stimulated with EGF and NGF. It facilitates an ultrasensitive switch between proliferation and differentiation, leading to distinct and predictable cell fate outcomes. Building on these findings, the thesis then explores the design of self-assembling multivalent protein constructs. These constructs demonstrate the ability to create synthetic signaling hubs that precisely control kinase activities with spatial accuracy, offering a novel approach to manipulate cellular decision making processes. This advancement opens up possibilities for designing novel signaling paradigms, moving beyond conventional approaches in cell signaling. The third aspect of the research applies machine learning to optimize T cell expansion in immunotherapy. By analyzing kinase activities and signaling pathways, we identify key determinants for enhancing the proliferation and stemness during T cell expansion in vitro, crucial for effective T cell therapy. This approach integrates systems biology, which focuses on the complex interactions within biological systems, with computational analytics, a field that applies algorithmic and statistical techniques to biological data. Together, this synergy enhances our ability to predict and optimize therapeutic outcomes. Collectively, this thesis underscores the importance of a systems biology approach in understanding cellular heterogeneity and decision making. The integration of cellular biology, molecular biology, computational modeling, synthetic biology, and machine learning illustrates the potential for innovative research in controlling and guiding cellular behavior, particularly in the realm of targeted cancer therapies. The findings and methodologies presented here not only advance our knowledge of cellular signaling dynamics but also provide foundational insights for the development of more effective and precise cellular therapies.