A biophysical model of viral escape from polyclonal antibodies.

A challenge in studying viral immune escape is determining how mutations combine to escape polyclonal antibodies, which can potentially target multiple distinct viral epitopes. Here we introduce a biophysical model of this process that partitions the total polyclonal antibody activity by epitope and then quantifies how each viral mutation affects the antibody activity against each epitope. We develop software that can use deep mutational scanning data to infer these properties for polyclonal antibody mixtures. We validate this software using a computationally simulated deep mutational scanning experiment and demonstrate that it enables the prediction of escape by arbitrary combinations of mutations. The software described in this paper is available at https://jbloomlab.github.io/polyclonal.

Predicting temperature-dependent transmission suitability of bluetongue virus in livestock.

The transmission of vector-borne diseases is governed by complex factors including pathogen characteristics, vector-host interactions, and environmental conditions. Temperature is a major driver for many vector-borne diseases including Bluetongue viral (BTV) disease, a midge-borne febrile disease of ruminants, notably livestock, whose etiology ranges from mild or asymptomatic to rapidly fatal, thus threatening animal agriculture and the economy of affected countries. Using modeling tools, we seek to predict where the transmission can occur based on suitable temperatures for BTV. We fit thermal performance curves to temperature-sensitive midge life-history traits, using a Bayesian approach. We incorporate these curves into S(T), a transmission suitability metric derived from the disease's basic reproductive number, [Formula: see text] This suitability metric encompasses all components that are known to be temperature-dependent. We use trait responses for two species of key midge vectors, Culicoides sonorensis and Culicoides variipennis present in North America. Our results show that outbreaks of BTV are more likely between 15[Formula: see text] C and [Formula: see text], with predicted peak transmission risk at 26 [Formula: see text] C. The greatest uncertainty in S(T) is associated with the following: the uncertainty in mortality and fecundity of midges near optimal temperature for transmission; midges' probability of becoming infectious post-infection at the lower edge of the thermal range; and the biting rate together with vector competence at the higher edge of the thermal range. We compare three model formulations and show that incorporating thermal curves into all three leads to similar BTV risk predictions. To demonstrate the utility of this modeling approach, we created global suitability maps indicating the areas at high and long-term risk of BTV transmission, to assess risk and to anticipate potential locations of disease establishment.

Tools for 3D Interactome Visualization.

In cells, intra- and intermolecular interactions of proteins confer function, and the dynamic modulation of this interactome is critical to meet the changing needs required to support life. Cross-linking and mass spectrometry (XL-MS) enable the detection of both intra- and intermolecular protein interactions in organelles, cells, tissues, and organs. Quantitative XL-MS enables the detection of interactome changes in cells due to environmental, phenotypic, pharmacological, or genetic perturbations. We have developed new informatics capabilities, the first to enable 3D visualization of multiple quantitative interactome data sets, acquired over time or with varied perturbation levels, to reveal relevant dynamic interactome changes. These new tools are integrated within release 3.0 of our online cross-linked peptide database and analysis tool suite XLinkDB. With the recent rapid expansion in XL-MS for protein structural studies and the extension to quantitative XL-MS measurements, 3D interactome visualization tools are of critical need.