Biology subjects

Rice, J.

Publications and source records attributed to Rice, J..

5 recordsLinked to original sources

bioRxiv2023

Strong immunogenicity & protection in mice with PlaCCine: A COVID-19 DNA vaccine formulated with a functional polymer

DNA- based vaccines have demonstrated the potential as a safe and effective modality. PlaCCine, a DNA-based vaccine approach described subsequently relies on a synthetic DNA delivery system and is independent of virus or device. The synthetic functionalized polymer combined with DNA demonstrated stability over 12 months at 4C and for one month at 25C. Transfection efficiency compared to naked DNA increased by 5-15-fold in murine skeletal muscle. Studies of DNA vaccines expressing spike proteins from variants D614G (pVAC15), Delta (pVAC16), or a D614G + Delta combination (pVAC17) were conducted. Mice immunized intramuscular injection (IM) with pVAC15, pVAC16 or pVAC17 formulated with functionalized polymer and adjuvant resulted in induction of spike-specific humoral and cellular responses. Antibody responses were observed after one immunization. And endpoint IgG titers increased to greater than 1x 105 two weeks after the second injection. Neutralizing antibodies as determined by a pseudovirus competition assay were observed following vaccination with pVAC15, pVAC16 or pVAC17. Spike specific T cell immune responses were also observed following vaccination and flow cytometry analysis demonstrated the cellular immune responses included both CD4 and CD8 spike specific T cells. The immune responses in vaccinated mice were maintained for up to 14 months after vaccination. In an immunization and challenge study of K18 hACE2 transgenic mice pVAC15, pVAC16 and pVAC17 induced immune responses lead to decreased lung viral loads by greater than 90% along with improved clinical score. These findings suggest that PlaCCine DNA vaccines are effective and stable and further development against emerging SARS-CoV-2 variants is warranted.

immunology↗

bioRxiv2022

Design of a systemic small molecule clinical STING agonist using physics-based simulations and artificial intelligence

The protein STING (stimulator of interferon genes) is a central regulator of the innate immune system and plays an important role in antitumor immunity by inducing the production of cytokines such as type I interferon (IFN). Activation of STING stems from the selective recognition of endogenous cyclic dinucleotides (CDNs) by the large, polar, and flexible binding site, thus posing challenges to the design of small molecule agonists with drug-like physicochemical properties. In this work we present the design of SNX281, a small molecule STING agonist that functions through a unique self-dimerizing mechanism in the STING binding site, where the ligand dimer approximates the size and shape of a cyclic dinucleotide while maintaining drug-like small molecule properties. SNX281 exhibits systemic exposure, STING-mediated cytokine release, strong induction of type I IFN, potent in vivo antitumor activity, durable immune memory, and single-dose tumor elimination in mouse models via a Cmax-driven pharmacologic response. Bespoke computational methods - a combination of quantum mechanics, molecular dynamics, binding free energy simulations, and artificial intelligence - were developed during the course of the project to design SNX281 by explicitly accounting for the unique self-dimerization mechanism and the large-scale conformational change of the STING protein upon activation. Over the course of the project, we explored millions of virtual molecules while synthesizing and testing only 208 molecules in the lab. This work highlights the value of a multifaceted computationally-driven approach anchored by methods tailored to address target-specific problems encountered along the project progression from initial hit to the clinic.

cancer biology↗

bioRxiv2020

Fish harvesting advice under climate change: a risk-equivalent empirical approach

1The rate of climate change (CC) has accelerated to the point where it now affects the mid-to long-term sustainability of fishing strategies. Therefore, it is important to consider practical and effective ways to incorporate CC into fisheries advice so that the advice can be considered conditioned to CC. We developed a quantitative model to characterise the empirical relationship between a variable affected by climate and fish production. We then used model projections as a foundation for a risk analysis of CC effects on harvesting of Greenland halibut Reinhardtius hippoglossoides in the Gulf of St Lawrence, Canada. The risk-based approach quantified a) the relative change in risk of a status quo fishing strategy under various CC scenarios, and b) the change in fishery exploitation rates required to achieve a management objective over a specified time period at a level of risk considered acceptable (risk equivalent fishery exploitation advice). This empirical approach can be used to develop risk-based advice for any other external variable that affects stock production in addition to climate-related variables and it can be applied in most situations where there is an index of stock biomass and fisheries catch. Shifting the focus from process-based understanding of the responses of fish stocks to CC to quantification of how CC-contributed uncertainty can alter the risks associated with different fishing strategies and/or management options, can ensure timely delivery of robust scientific advice for fisheries under non-stationary environmental conditions.

ecology↗

bioRxiv2018

High content analysis methods enable high throughput nematode discovery screening for viability and movement behavior in a multiplex sample in response to natural product treatment.

Monitoring nematode parasite movement and mortality in response to various treatment samples usually involves tedious manual microscopic analysis. High Content Analysis instrumentation enables rapid and high throughput collecting of large numbers of treatment data on huge numbers of individual worms. These large sample sizes and increased sample diversity result in robust, reliable results with increased statistical significance. These methods would be applicable to relevant human, crop, or animal worm parasites.

systems biology↗

bioRxiv2017

Exome chip meta-analysis elucidates the genetic architecture of rare coding variants in smoking and drinking behavior

BackgroundSmoking and alcohol use behaviors in humans have been associated with common genetic variants within multiple genomic loci. Investigation of rare variation within these loci holds promise for identifying causal variants impacting biological mechanisms in the etiology of disordered behavior. Microarrays have been designed to genotype rare nonsynonymous and putative loss of function variants. Such variants are expected to have greater deleterious consequences on gene function than other variants, and significantly contribute to disease risk.\n\nMethodsIn the present study, we analyzed [~]250,000 rare variants from 17 independent studies. Each variant was tested for association with five addiction-related phenotypes: cigarettes per day, pack years, smoking initiation, age of smoking initiation, and alcoholic drinks per week. We conducted single variant tests of all variants, and gene-based burden tests of nonsynonymous or putative loss of function variants with minor allele frequency less than 1%.\n\nResultsMeta-analytic sample sizes ranged from 70,847 to 164,142 individuals, depending on the phenotype. Known loci tagged by common variants replicated, but there was no robust evidence for individually associated rare variants, either in gene based or single variant tests. Using a modified method-of-moment approach, we found that all low frequency coding variants, in aggregate, contributed 1.7% to 3.6% of the phenotypic variation for the five traits (p<.05).\n\nConclusionsThe findings indicate that rare coding variants contribute to phenotypic variation, but that much larger samples and/or denser genotyping of rare variants will be required to successfully identify associations with these phenotypes, whether individual variants or gene- based associations.

genetics↗