Scott R. Broderick

Activation of innate immune responses in a pathogen-mimicking manner by amphiphilic polyanhydride nanoparticle adjuvants

Techniques in materials design, immunophenotyping, and informatics can be valuable tools for using a molecular based approach to design vaccine adjuvants capable of inducing protective immunity that mimics a natural infection but without the toxic side effects. This work describes the molecular design of amphiphilic polyanhydride nanoparticles that activate antigen presenting cells in a pathogen-mimicking manner. Biodegradable polyanhydrides are well suited as vaccine delivery vehicles due to their adjuvant-like ability to: 1) enhance the immune response, 2) preserve protein structure, and 3) control protein release. The results of these studies indicate that amphiphilic nanoparticles possess pathogen-mimicking properties as evidenced by their ability to activate dendritic cells similarly to LPS. Specific molecular descriptors responsible for this behavior were identified using informatics analyses, including the number of backbone oxygen moieties, percent of hydroxyl end groups, polymer hydrophobicity, and number of alkyl ethers. Additional findings from this work suggest that the molecular characteristics mediating APC activation are not limited to hydrophobicity but vary in complexity (e.g., presentation of oxygen-rich molecular patterns to cells) and elicit unique patterns of cellular activation. The approach outlined herein demonstrates the ability to rationally design pathogen-mimicking nanoparticle adjuvants for use in next-generation vaccines against emerging and re-emerging diseases.

Adaptation of Francisella tularensis to the Mammalian Environment Is Governed by Cues Which Can Be Mimicked In Vitro

ABSTRACT The intracellular bacterium Francisella tularensis survives in mammals, arthropods, and freshwater amoeba. It was previously established that the conventional media used for in vitro propagation of this microbe do not yield bacteria that mimic those harvested from infected mammals; whether these in vitro-cultivated bacteria resemble arthropod- or amoeba-adapted Francisella is unknown. As a foundation for our goal of identifying F. tularensis outer membrane proteins which are expressed during mammalian infection, we first sought to identify in vitro cultivation conditions that induce the bacteriums infection-derived phenotype. We compared Francisella LVS grown in brain heart infusion broth (BHI; a standard microbiological medium rarely used in Francisella research) to that grown in Mueller-Hinton broth (MHB; the most widely used F. tularensis medium, used here as a negative control) and macrophages (a natural host cell, used here as a positive control). BHI- and macrophage-grown F. tularensis cells showed similar expression of MglA-dependent and MglA-independent proteins; expression of the MglA-dependent proteins was repressed by the supraphysiological levels of free amino acids present in MHB. We observed that during macrophage infection, protein expression by intracellular bacteria differed from that by extracellular bacteria; BHI-grown bacteria mirrored the latter, while MHB-grown bacteria resembled neither. Naïve macrophages responding to BHI- and macrophage-grown bacteria produced markedly lower levels of proinflammatory mediators than those in cells exposed to MHB-grown bacteria. In contrast to MHB-grown bacteria, BHI-grown bacteria showed minimal delay during intracellular replication. Cumulatively, our findings provide compelling evidence that growth in BHI yields bacteria which recapitulate the phenotype of Francisella organisms that have emerged from macrophages.

Identifying the 'inorganic gene' for high-temperature piezoelectric perovskites through statistical learning.

This paper develops a statistical learning approach to identify potentially new high-temperature ferroelectric piezoelectric perovskite compounds. Unlike most computational studies on crystal chemistry, where the starting point is some form of electronic structure calculation, we use a data-driven approach to initiate our search. This is accomplished by identifying patterns of behaviour between discrete scalar descriptors associated with crystal and electronic structure and the reported Curie temperature (TC) of known compounds; extracting design rules that govern critical structure–property relationships; and discovering in a quantitative fashion the exact role of these materials descriptors. Our approach applies linear manifold methods for data dimensionality reduction to discover the dominant descriptors governing structure–property correlations (the ‘genes’) and Shannon entropy metrics coupled to recursive partitioning methods to quantitatively assess the specific combination of descriptors that govern the link between crystal chemistry and TC (their ‘sequencing’). We use this information to develop predictive models that can suggest new structure/chemistries and/or properties. In this manner, BiTmO3–PbTiO3 and BiLuO3–PbTiO3 are predicted to have a TC of 730°C and 705°C, respectively. A quantitative structure–property relationship model similar to those used in biology and drug discovery not only predicts our new chemistries but also validates published reports.

Rational Design of Pathogen-Mimicking Amphiphilic Materials as Nanoadjuvants

An opportunity exists today for cross-cutting research utilizing advances in materials science, immunology, microbial pathogenesis, and computational analysis to effectively design the next generation of adjuvants and vaccines. This study integrates these advances into a bottom-up approach for the molecular design of nanoadjuvants capable of mimicking the immune response induced by a natural infection but without the toxic side effects. Biodegradable amphiphilic polyanhydrides possess the unique ability to mimic pathogens and pathogen associated molecular patterns with respect to persisting within and activating immune cells, respectively. The molecular properties responsible for the pathogen-mimicking abilities of these materials have been identified. The value of using polyanhydride nanovaccines was demonstrated by the induction of long-lived protection against a lethal challenge of Yersinia pestis following a single administration ten months earlier. This approach has the tantalizing potential to catalyze the development of next generation vaccines against diseases caused by emerging and re-emerging pathogens.

Identifying Factors Controlling Protein Release from Combinatorial Biomaterial Libraries via Hybrid Data Mining Methods

Polyanhydrides are a class of degradable biomaterials that have shown much promise for applications in drug and vaccine delivery. Their properties can be tailored for controlled drug release, drug/protein stability, and immune regulation (adjuvant effect). Identifying the relationship between the molecular structures of the polymers and the drug release kinetics profiles would help understand the release mechanism and aid in the accurate prediction of drug release and the rational design of polymer-based drug carrier systems. The molecular structure descriptors that had the most impact on the release kinetics were identified using a prediction/optimization data mining approach. Using this new approach for modeling nonlinear release kinetics behavior, we determined that the descriptors which had the greatest effect on the release kinetics were the number of backbone -COO- nonconjugated bonds, the number of aromatic rings, and the number of -CH₂- bonds.

Mapping energetics of atom probe evaporation events through first principles calculations.

The purpose of this work is to use atomistic modeling to determine accurate inputs into the atom probe tomography (APT) reconstruction process. One of these inputs is evaporation field; however, a challenge occurs because single ions and dimers have different evaporation fields. We have calculated the evaporation field of Al and Sc ions and Al-Al and Al-Sc dimers from an L1₂-Al₃Sc surface using ab initio calculations and with a high electric field applied to the surface. The evaporation field is defined as the electric field at which the energy barrier size is calculated as zero, corresponding to the minimum field that atoms from the surface can break their bonds and evaporate from the surface. The evaporation field of the surface atoms are ranked from least to greatest as: Al-Al dimer, Al ion, Sc ion, and Al-Sc dimer. The first principles results were compared with experimental data in the form of an ion evaporation map, which maps multi-ion evaporations. From the ion evaporation map of L1₂-Al₃Sc, we extract relative evaporation fields and identify that an Al-Al dimer has a lower evaporation field than an Al-Sc dimer. Additionally, comparatively an Al-Al surface dimer is more likely to evaporate as a dimer, while an Al-Sc surface dimer is more likely to evaporate as single ions. These conclusions from the experiment agree with the ab initio calculations, validating the use of this approach for modeling APT energetics.

Informatics guided discovery of surface structure-chemistry relationships in catalytic nanoparticles

A data driven discovery strategy based on statistical learning principles is used to discover new correlations between electronic structure and catalytic activity of metal surfaces. From the quantitative formulations derived from this informatics based model, a high throughput computational framework for predicting binding energy as a function of surface chemistry and adsorption configuration that bypasses the need for repeated electronic structure calculations has been developed.

Tracking chemical processing pathways in combinatorial polymer libraries via data mining

Changes in the molecular structure and composition of interpenetrating polymer networks (IPNs) can be used to tailor their properties. While the properties of IPNs are typically different than polymer blends, a clear understanding of the impact of changing polymerization sequence on the physical properties and the corresponding molecular bonding is needed. To address this issue, a data mining approach is used to identify the change with polymerization sequence of tensile and rheological properties of acrylate-epoxy IPNs. The experimental approach used to study the molecular structure is high throughput Fourier transform infrared (FTIR) spectroscopy. Analysis of the FTIR spectra of IPNs synthesized with different polymerization sequences leads to an understanding of the molecular bonding responsible for the tensile and rheological properties. From the interpretation of the wavenumber bands and associated molecular bonds, we find that the polymerization sequence most affects hydrogen bonding and aromatic ring bond energies. This work defines the relationships between chemistry, structure, processing, and properties of the IPN samples.

A systems approach to designing next generation vaccines: combining α-galactose modified antigens with nanoparticle platforms.

Innovative vaccine platforms are needed to develop effective countermeasures against emerging and re-emerging diseases. These platforms should direct antigen internalization by antigen presenting cells and promote immunogenic responses. This work describes an innovative systems approach combining two novel platforms, αGalactose (αGal)-modification of antigens and amphiphilic polyanhydride nanoparticles as vaccine delivery vehicles, to rationally design vaccine formulations. Regimens comprising soluble αGal-modified antigen and nanoparticle-encapsulated unmodified antigen induced a high titer, high avidity antibody response with broader epitope recognition of antigenic peptides than other regimen. Proliferation of antigen-specific CD4+ T cells was also enhanced compared to a traditional adjuvant. Combining the technology platforms and augmenting immune response studies with peptide arrays and informatics analysis provides a new paradigm for rational, systems-based design of next generation vaccine platforms against emerging and re-emerging pathogens.

Hemagglutinin-based polyanhydride nanovaccines against H5N1 influenza elicit protective virus neutralizing titers and cell-mediated immunity

H5N1 avian influenza is a significant global concern with the potential to become the next pandemic threat. Recombinant subunit vaccines are an attractive alternative for pandemic vaccines compared to traditional vaccine technologies. In particular, polyanhydride nanoparticles encapsulating subunit proteins have been shown to enhance humoral and cell-mediated immunity and provide protection upon lethal challenge. In this work, a recombinant H5 hemagglutinin trimer (H53) was produced and encapsulated into polyanhydride nanoparticles. The studies performed indicated that the recombinant H53 antigen was a robust immunogen. Immunizing mice with H53 encapsulated into polyanhydride nanoparticles induced high neutralizing antibody titers and enhanced CD4+ T cell recall responses in mice. Finally, the H53-based polyanhydride nanovaccine induced protective immunity against a low-pathogenic H5N1 viral challenge. Informatics analyses indicated that mice receiving the nanovaccine formulations and subsequently challenged with virus were similar to naïve mice that were not challenged. The current studies provide a basis to further exploit the advantages of polyanhydride nanovaccines in pandemic scenarios.