Modular control of innate immune signaling using self-assembly of immune signals

Vaccines play an increasingly important role in preventing and treating diseases ranging from infectious pathogens to cancer because these technologies harness the specificity of the immune system to clear disease without targeting the body’s own cells. To realize these goals, new understanding of adjuvants – molecules added to vaccines to enhance function – is needed to support design of next-generation vaccines that elicit responses tailored for specific diseases. We recently reported a simple nanotechnology platform based on self-assembly of peptide antigen and a molecular toll-like receptor agonist (TLRa) to create modular vaccine designs (ACS Nano 2016, ACS Nano 2015). These structures – termed immune polyelectrolyte multilayers (iPEMs) – juxtapose antigen and TLRa at high densities, and offer 100% cargo loading since no carrier component is needed. This modularity also creates the possibility of rationally designing iPEMs that trigger multiple immune pathways with distinct control over the relative activation levels. In cancer, for example, activating multiple innate pathways has been linked to improved patient outcomes in human clinical trials. To exploit iPEMs in this manner, we designed iPEM architectures incorporating a conserved human cancer antigen (Trp2), and a range of molecularly-defined TLRa that spanned different TLRa classes and species (i.e., mouse and human): agonists for TLR3, TLR9, and TLR13. iPEMs were assembled from Trp2 and one, two, or three TLRas, or alternatively, using two different TLRas at varying compositions. To form carrier free capsules using these design schemes, Trp2 was appended with cationic amino acids, then adsorbed onto a sacrificial colloidal template, with alternating adsorption steps employing the specified TLRas (anionic). Please click Additional Files below to see the full abstract

Outbreak response forecasting for vector borne diseases:theileria orientalis (Ikeda) in NZ cattle

Dynamical models of communicable diseases have become a prominent feature of national-level epidemic response. Developments in Bayesian inference have enabled these models to provide quantitative risk predic- tions in a real-time setting, learning from spatiotemporal data as it arrives from the field. However, these models rely heavily on accurate covariate data from which to make inference. Incursions of vector borne disease present a particular challenge in this respect, as exemplified by the recent introduction of Theileria orientalis (Ikeda), an obligate tick-borne disease of cattle, into New Zealand. Whereas the location of cattle and the animal movement network between farms is well recorded, little is known about the national scale ecology of the tick vector. This talk will present a Bayesian data assimilation approach to this problem, in which vector presence is modelled as a discrete-space latent process with a continuous-time seasonality. A joint likelihood function assimilates the epidemic data and results from a national disease surveillance pro- gramme designed for a different disease. A spatiotemporally inhomogeneous Poisson process is used to model the epidemic, with an a priori independent hierarchical binomial surveillance model. This joint model is fitted to observed case detection data using a non-centered trans-dimensional MCMC algorithm, integrating over the marginal posterior of the latent vector surface, censored herd infection times, and the presence of undetected infections. Importantly, the algorithm is implemented using GPGPU technology which acceler- ates within-chain likelihood calculations to an overnight timeframe. Finally, the predictive distribution is provided as a real time disease forecast for decision support purposes

Gas Bubbles Emerging from a Submerged Granular Bed

This fluid dynamics video was submitted to the Gallery of Fluid Motion for the 2009 APS Division of Fluid Dynamics Meeting in Minneapolis, Minnesota. In this video we show some results from a simple experiment where air was injected by a single nozzle at known constant flow rates in the bottom of a granular bed submerged in water. The injected air propagates through the granular bed in one of two modes. Mode 1 emergence involves small discrete bubbles taking tortuous paths through the interstitial space of the bed. Multiple small bubbles can be emitted from the bed in an array of locations at the same time during Mode 1 emergence. Mode 2 emergence involves large discrete bubbles locally fluidizing the granular bed and exiting the bed approximately above the injection site. Bead diameter, bead density, and air flow rate were varied to investigate the change in bubble release behavior at the top of the granular bed. This system is a useful model for methane seeps in lakes. Methane bubbles are released from the decomposition of organic matter in the lake bed. The initial size of the bubble determines how much of the gas is absorbed into the lake and how much of the gas reaches the surface and is released into the atmosphere. The size and behavior of the emerging bubbles may also affect the amount of vertical mixing occurring in the lake, as well as the mixing from the lake bed into the benthic layer.Comment: 2009 APS DFD Gallery of Fluid Motion Submissio

Segmented coronagraph design and analysis (SCDA): an initial design study of apodized vortex coronagraphs

The segmented coronagraph design and analysis (SCDA) study is a coordinated effort, led by Stuart Shaklan (JPL) and supported by NASA's Exoplanet Exploration Program (ExEP), to provide efficient coronagraph design concepts for exoplanet imaging with future segmented aperture space telescopes. This document serves as an update on the apodized vortex coronagraph designs devised by the Caltech/JPL SCDA team. Apodized vortex coronagraphs come in two flavors, where the apodization is achieved either by use of 1) a gray-scale semi-transparent pupil mask or 2) a pair of deformable mirrors in series. Each approach has attractive benefits. This document presents a comprehensive review of the former type. Future theoretical investigations will further explore the use of deformable mirrors for apodization.Comment: White Paper (2016-2017

Tolerance induction with quantum dots displaying tunable densities of self-antigen

During autoimmune diseases like type 1 diabetes or multiple sclerosis (MS), the immune system mistakenly recognizes and attacks healthy tissues in the body. In MS, myelin, which surrounds and protects the axons of neurons, is attacked by inflammatory cells leading to neurodegeneration. The current standard of care for MS patients is regular injection of immunosuppressive drugs that non-specifically suppress immune function, leaving patients immunocompromised and open to opportunistic infection. New investigations aim to address this problem with immunotherapy-based strategies that promote myelin-specific tolerance. Recent reports reveal that the development of inflammation or tolerance against certain molecules is influenced by the concentration and form of self-antigen presented to immune cells (i.e. free, particle).Strategies that allow tunable delivery of self-antigen are therefore of great interest to further probe these connections. Quantum dots (QDs) were chosen as the nanomaterial to investigate these questions because they can be conjugated with a large and controllable number of biomolecules.Additionally, their size facilitates rapid drainage through lymphatics to lymph nodes (LNs), where they accumulate and can be visualized by deep-tissue imaging due to their intrinsic fluorescence. Please click Additional Files below to see the full abstract

An approach for benchmarking the numerical solutions of stochastic compartmental models

An approach is introduced for comparing the estimated states of stochastic compartmental models for an epidemic or biological process with analytically obtained solutions from the corresponding system of ordinary differential equations (ODEs). Positive integer valued samples from a stochastic model are generated numerically at discrete time intervals using either the Reed-Frost chain Binomial or Gillespie algorithm. The simulated distribution of realisations is compared with an exact solution obtained analytically from the ODE model. Using this novel methodology this work demonstrates it is feasible to check that the realisations from the stochastic compartmental model adhere to the ODE model they represent. There is no requirement for the model to be in any particular state or limit. These techniques are developed using the stochastic compartmental model for a susceptible-infected-recovered (SIR) epidemic process. The Lotka-Volterra model is then used as an example of the generality of the principles developed here. This approach presents a way of testing/benchmarking the numerical solutions of stochastic compartmental models, e.g. using unit tests, to check that the computer code along with its corresponding algorithm adheres to the underlying ODE model.Comment: 21 pages 3 figure

Dissecting regulatory T cell expansion using polymer microparticles presenting defined ratios of self-antigen and regulatory cues

Biomaterials allow for the precision control over the combination and release of cargo needed to engineer cell outcomes. These capabilities are particularly attractive as new candidate therapies to treat autoimmune diseases, conditions where dysfunctional immune cells create pathogenic tissue environments during attack of self-molecules termed self-antigens. Here we extend past studies showing combinations of a small molecule immunomodulator co-delivered with self-antigen induces antigen-specific regulatory T cells. In particular, we sought to elucidate how different ratios of these components loaded in degradable polymer particles shape the antigen presenting cell (APC) -T cell interactions that drive differentiation of T cells toward either inflammatory or regulatory phenotypes. Using rapamycin (rapa) as a modulatory cue and myelin self-peptide (myelin oligodendrocyte glycoprotein- MOG) – self-antigen attacked during multiple sclerosis (MS), we integrate these components into polymer particles over a range of ratios and concentrations without altering the physicochemical properties of the particles. Using primary cell co-cultures, we show that while all ratios of rapa:MOG significantly decreased expression of co-stimulation molecules on dendritic cells (DCs), these levels were insensitive to the specific ratio. During co-culture with primary T cell receptor transgenic T cells, we demonstrate that the ratio of rapa:MOG controls the expansion and differentiation of these cells. In particular, at shorter time points, higher ratios induce regulatory T cells most efficiently, while at longer time points the processes are not sensitive to the specific ratio. We also found corresponding changes in gene expression and inflammatory cytokine secretion during these times. The in vitro results in this study contribute to in vitro regulatory T cell expansion techniques, as well as provide insight into future studies to explore other modulatory effects of rapa such as induction of maintenance or survival cues

Forecasting for outbreaks of vector-borne diseases: a data assimilation approach

In August 2012, the first case of a novel strain of /Theileria orientalis/ (Ikeda) was discovered in a dairy herd near Auckland, New Zealand. The strain was unusually pathogenic, causing haemolytic anaemia in up to 35% of animals within an infected herd. In the ensuing months, more cases were discovered in a pattern that suggested wave-like spread down New Zealand’s North Island. Theileria orientalis is a blood-borne parasite of cattle, which is transmitted by the tick vector /Haemaphysalis longicornis/. This tick was known to exist in New Zealand, but although its behaviour and life cycle were known from laboratory experiments surprisingly little was known about its country-wide distribution. Predicting the spread of /T. orientalis/ (Ikeda) for management and economic purposes was therefore complicated by not knowing which areas of the country would be conducive to transmission, if an infected cow happened to be imported via transportation. The approach to prediction presented here uses a Bayesian probability model of dynamical disease spread, in combination with a separable discrete-space, continuous-time spatial model of tick abundance. This joint model allows inference on tick abundance by combining information from independent disease screening, expert opinion, and the occurrence of theileriosis cases. A fast GPU-based implementation was used to provide timely predictions for the outbreak, with the predictive distribution used to provide evidence for policy decisions

Modelling the impact of social mixing and behaviour on infectious disease transmission: application to SARS-CoV-2

In regard to infectious diseases socioeconomic determinants are strongly associated with differential exposure and susceptibility however they are seldom accounted for by standard compartmental infectious disease models. These associations are explored here with a novel compartmental infectious disease model which, stratified by deprivation and age, accounts for population-level behaviour including social mixing patterns. As an exemplar using a fully Bayesian approach our model is fitted, in real-time if required, to the UKHSA COVID-19 community testing case data from England. Metrics including reproduction number and forecasts of daily case incidence are estimated from the posterior samples. From this UKHSA dataset it is observed that during the initial period of the pandemic the most deprived groups reported the most cases however this trend reversed after the summer of 2021. Forward simulation experiments based on the fitted model demonstrate that this reversal can be accounted for by differential changes in population level behaviours including social mixing and testing behaviour, but it is not explained by the depletion of susceptible individuals. In future epidemics, with a focus on socioeconomic factors the approach outlined here provides the possibility of identifying those groups most at risk with a view to helping policy-makers better target their support.Comment: Main article: 25 pages, 6 figures. Appendix 2 pages, 1 figure. Supplementary Material: 15 pages, 14 figures. Version 2 - minor updates: fixed typos, updated mathematical notation and small quantity of descriptive text added. Version 3 - minor update: made colour coding consistent across all time series figure