Carlos Tassa

Disrupted in Schizophrenia 1 Regulates Neuronal Progenitor Proliferation via Modulation of GSK3β/β-Catenin Signaling

The Disrupted in Schizophrenia 1 (DISC1) gene is disrupted by a balanced chromosomal translocation (1; 11) (q42; q14.3) in a Scottish family with a high incidence of major depression, schizophrenia, and bipolar disorder. Subsequent studies provided indications that DISC1 plays a role in brain development. Here, we demonstrate that suppression of DISC1 expression reduces neural progenitor proliferation, leading to premature cell cycle exit and differentiation. Several lines of evidence suggest that DISC1 mediates this function by regulating GSK3beta. First, DISC1 inhibits GSK3beta activity through direct physical interaction, which reduces beta-catenin phosphorylation and stabilizes beta-catenin. Importantly, expression of stabilized beta-catenin overrides the impairment of progenitor proliferation caused by DISC1 loss of function. Furthermore, GSK3 inhibitors normalize progenitor proliferation and behavioral defects caused by DISC1 loss of function. Together, these results implicate DISC1 in GSK3beta/beta-catenin signaling pathways and provide a framework for understanding how alterations in this pathway may contribute to the etiology of psychiatric disorders.

Dextran-Coated Iron Oxide Nanoparticles: A Versatile Platform for Targeted Molecular Imaging, Molecular Diagnostics, and Therapy

Advances in our understanding of the genetic basis of disease susceptibility coupled with prominent successes for molecular targeted therapies have resulted in an emerging strategy of personalized medicine. This approach envisions risk stratification and therapeutic selection based on an individuals genetic makeup and physiologic state (the latter assessed through cellular or molecular phenotypes). Molecularly targeted nanoparticles can play a key role in this vision through noninvasive assessments of molecular processes and specific cell populations in vivo, sensitive molecular diagnostics, and targeted delivery of therapeutics. A superparamagnetic iron oxide nanoparticle with a cross-linked dextran coating, or CLIO, is a powerful and illustrative nanoparticle platform for these applications. These structures and their derivatives support diagnostic imaging by magnetic resonance (MRI), optical, and positron emission tomography (PET) modalities and constitute a versatile platform for conjugation to targeting ligands. A variety of conjugation methods exist to couple the dextran surface to different functional groups; in addition, a robust bioorthogonal [4 + 2] cycloaddition reaction between 1,2,4,5-tetrazene (Tz) and trans-cyclooctene (TCO) can conjugate nanoparticles to targeting ligands or label pretargeted cells. The ready availability of conjugation methods has given rise to the synthesis of libraries of small molecule modified nanoparticles, which can then be screened for nanoparticles with specificity for a specific cell type. Since most nanoparticles display their targeting ligands in a multivalent manner, a detailed understanding of the kinetics and affinity of a nanoparticles interaction with its target (as determined by surface plasmon resonance) can yield functionally important insights into nanoparticle design. In this Account, we review applications of the CLIO platform in several areas relevant to the mission of personalized medicine. We demonstrate rapid and highly sensitive molecular profiling of cancer markers ex vivo, as part of detailed, individualized molecular phenotyping. The CLIO platform also facilitates targeted magnetic resonance and combined modality imaging (such as MR/PET/fluorescence/CT) to enable multiplexed measurement of molecular phenotypes in vivo for early diagnosis and disease classification. Finally, the targeted delivery of a photodynamic therapy agent as part of a theranostic nanoparticle successfully increased local cell toxicity and minimized systemic side effects.

Quantitative Nanostructure−Activity Relationship Modeling

Evaluation of biological effects, both desired and undesired, caused by manufactured nanoparticles (MNPs) is of critical importance for nanotechnology. Experimental studies, especially toxicological, are time-consuming, costly, and often impractical, calling for the development of efficient computational approaches capable of predicting biological effects of MNPs. To this end, we have investigated the potential of cheminformatics methods such as quantitative structure-activity relationship (QSAR) modeling to establish statistically significant relationships between measured biological activity profiles of MNPs and their physical, chemical, and geometrical properties, either measured experimentally or computed from the structure of MNPs. To reflect the context of the study, we termed our approach quantitative nanostructure-activity relationship (QNAR) modeling. We have employed two representative sets of MNPs studied recently using in vitro cell-based assays: (i) 51 various MNPs with diverse metal cores (Proc. Natl. Acad. Sci. 2008, 105, 7387-7392) and (ii) 109 MNPs with similar core but diverse surface modifiers (Nat. Biotechnol. 2005, 23, 1418-1423). We have generated QNAR models using machine learning approaches such as support vector machine (SVM)-based classification and k nearest neighbors (kNN)-based regression; their external prediction power was shown to be as high as 73% for classification modeling and having an R(2) of 0.72 for regression modeling. Our results suggest that QNAR models can be employed for: (i) predicting biological activity profiles of novel nanomaterials, and (ii) prioritizing the design and manufacturing of nanomaterials toward better and safer products.

Binding affinity and kinetic analysis of targeted small molecule-modified nanoparticles

Nanoparticles bearing surface-conjugated targeting ligands are increasingly being explored for a variety of biomedical applications. The multivalent conjugation of targeting ligands on the surface of nanoparticles is presumed to enhance binding to the desired target. However, given the complexities inherent in the interactions of nanoparticle surfaces with proteins, and the structural diversity of nanoparticle scaffolds and targeting ligands, our understanding of how conjugation of targeting ligands affects nanoparticle binding remains incomplete. Here, we use surface plasmon resonance (SPR) to directly and quantitatively study the affinity and binding kinetics of nanoparticles that display small molecules conjugated to their surface. We studied the interaction between a single protein target and a structurally related series of targeting ligands whose intrinsic affinity varies over a 4500-fold range and performed SPR at protein densities that reflect endogenous receptor densities. We report that even weak small molecule targeting ligands can significantly enhance target-specific avidity (by up to 4 orders of magnitude) through multivalent interactions and also observe a much broader range of kinetic effects than has been previously reported. Quantitative measurement of how the affinity and kinetics of nanoparticle binding vary as a function of different surface conjugations is a rapid, generalizable approach to nanoparticle characterization that can inform the design of nanoparticles for biomedical applications.

Modeling Biological Activities of Nanoparticles

Products are increasingly incorporating nanomaterials, but we have a poor understanding of their adverse effects. To assess risk, regulatory authorities need more experimental testing of nanoparticles. Computational models play a complementary role in allowing rapid prediction of potential toxicities of new and modified nanomaterials. We generated quantitative, predictive models of cellular uptake and apoptosis induced by nanoparticles for several cell types. We illustrate the potential of computational methods to make a contribution to nanosafety.

Supramolecular Host–Guest Interaction for Labeling and Detection of Cellular Biomarkers

Be my guest: A supramolecular host-guest interaction is utilized for highly efficient bioorthogonal labeling of cellular targets. Antibodies labeled with a cyclodextrin host molecule bind to adamantane-labeled magnetofluorescent nanoparticles (see picture) and provide an amplifiable strategy for biomarker detection that can be adapted to different diagnostic techniques such as molecular profiling or magnetic cell sorting.

Specific pathogen detection using bioorthogonal chemistry and diagnostic magnetic resonance.

The development of faster and more sensitive detection methods capable of identifying specific bacterial species and strains has remained a longstanding clinical challenge. Thus to date, the diagnosis of bacterial infections continues to rely on the performance of time-consuming microbiological cultures. Here, we demonstrate the use of bioorthogonal chemistry for magnetically labeling specific pathogens to enable their subsequent detection by nuclear magnetic resonance. Antibodies against a bacterial target of interest were first modified with trans-cyclooctene and then coupled to tetrazine-modified magnetic nanoprobes, directly on the bacteria. This labeling method was verified by surface plasmon resonance as well as by highly specific detection of Staphylococcus aureus using a miniaturized diagnostic magnetic resonance system. Compared to other copper-free bioorthogonal chemistries, the cycloaddition reaction reported here displayed faster kinetics and yielded higher labeling efficiency. Considering the short assay times and the portability of the necessary instrumentation, it is feasible that this approach could be adapted for clinical use in resource-limited settings.

Modelling and predicting the biological effects of nanomaterials.

The commercial applications of nanoparticles are growing rapidly, but we know relatively little about how nanoparticles interact with biological systems. Their value – but also their risk – is related to their nanophase properties being markedly different to those of the same material in bulk. Experiments to determine how nanoparticles are taken up, distributed, modified, and elicit any adverse effects are essential. However, cost and time considerations mean that predictive models would also be extremely valuable, particularly assisting regulators to minimize health and environmental risks. We used novel sparse machine learning methods that employ Bayesian neural networks to model three nanoparticle data sets using both linear and nonlinear machine learning methods. The first data comprised iron oxide nanoparticles decorated with 108 different molecules tested against five cell lines, HUVEC, pancreatic cancer, and three macrophage or macrophage-like lines. The second data set comprised 52 nanoparticles with various core compositions, coatings, and surface attachments. The nanoparticles were characterized using four descriptors (size, relaxivities, and zeta potential), and their biological effects on four cells lines assessed using four biological assays per cell line and four concentrations per assay. The third data set involved the biological responses to gold nanoparticles functionalized by 80 different small molecules. Nonspecific binding and binding to AChE were the biological endpoints modelled. The biological effects of nanoparticles were modelled using molecular descriptors for the molecules that decorated the nanoparticle surface. Models with good statistical quality were constructed for most biological endpoints. These proof-of-concept models show that modelling biological effects of nanomaterials is possible using modern modelling methods.

Bioorthogonal Approach to Identify Unsuspected Drug Targets in Live Cells

A proteomics method to pull down secondary drug targets from live cells is described. The drug of interest is modified with trans-cyclooctene (TCO) and incubated with live cells. Upon cell lysis, the modified drug bound to the protein is pulled down using magnetic beads decorated with a cleavable tetrazine-modified linker. Samples are then run on an SDS-PAGE gel and isolated bands are submitted for mass spectrometry analysis to identify drug targets.

On-chip bioorthogonal chemistry enables immobilization of in situ modified nanoparticles and small molecules for label-free monitoring of protein binding and reaction kinetics

Efficient methods to immobilize small molecules under continuous-flow microfluidic conditions would greatly improve label-free molecular interaction studies using biosensor technology. At present, small-molecule immobilization chemistries require special conditions and in many cases must be performed outside the detector and microfluidic system where real-time monitoring is not possible. Here, we have developed and optimized a method for on-chip bioorthogonal chemistry that enables rapid, reversible immobilization of small molecules with control over orientation and immobilization density, and apply this technique to surface plasmon resonance (SPR) studies. Immobilized small molecules reverse the orientation of canonical SPR interaction studies, and also enable a variety of new SPR applications including on-chip assembly and interaction studies of multicomponent structures, such as functionalized nanoparticles, and measurement of bioorthogonal reaction rates. We use this approach to demonstrate that on-chip assembled functionalized nanoparticles show a preserved ability to interact with their target protein, and to measure rapid bioorthogonal reaction rates with k(2) > 10(3) M(-1) s(-1). This method offers multiple benefits for microfluidic biological applications, including rapid screening of targeted nanoparticles with vastly decreased nanoparticle synthetic requirements, robust immobilization chemistry in the presence of serum, and a continuous flow technique that mimics biologic contexts better than current methods used to measure bioorthogonal reaction kinetics such as NMR or UV-vis spectroscopy (e.g., stopped flow kinetics). Taken together, this approach constitutes a flexible and powerful technique for evaluating a wide variety of reactions and intermolecular interactions for in vitro or in vivo applications.