Vaccinia virus H3L envelope protein is a major target of neutralizing antibodies in humans and elicits protection against lethal challenge in mice

The smallpox vaccine is the prototypic vaccine, yet the viral targets critical for vaccine-mediated protection remain unclear in humans. We have produced protein microarrays of a near-complete vaccinia proteome and used them to determine the major antigen specificities of the human humoral immune response to the smallpox vaccine (Dryvax). H3L, an intracellular mature virion envelope protein, was consistently recognized by high-titer antibodies in the majority of human donors, particularly after secondary immunization. We then focused on examining H3L as a valuable human antibody target. Purified human anti-H3L antibodies exhibited substantial vaccinia virus-neutralizing activity in vitro (50% plaque reduction neutralization test [PRNT50] = 44 microg/ml). Mice also make an immunodominant antibody response to H3L after vaccination with vaccinia virus, as determined by vaccinia virus protein microarray. Mice were immunized with recombinant H3L protein to examine H3L-specific antibody responses in greater detail. H3L-immunized mice developed high-titer vaccinia virus-neutralizing antibodies (mean PRNT50 = 1:3,760). Importantly, H3L-immunized mice were subsequently protected against lethal intranasal challenges with 1 or 5 50% lethal doses (LD50) of pathogenic vaccinia virus strain WR, demonstrating the in vivo value of an anti-H3L response. To formally demonstrate that neutralizing anti-H3L antibodies are protective in vivo, we performed anti-H3L serum passive-transfer experiments. Mice receiving H3L-neutralizing antiserum were protected from a lethal challenge with 3 LD50 of vaccinia virus strain WR (5/10 versus 0/10; P < 0.02). Together, these data show that H3L is a major target of the human anti-poxvirus antibody response and is likely to be a key contributor to protection against poxvirus infection and disease.     

Minimally Mutated HIV-1 Broadly Neutralizing Antibodies to Guide Reductionist Vaccine Design

An optimal HIV vaccine should induce broadly neutralizing antibodies (bnAbs) that neutralize diverse viral strains and subtypes. However, potent bnAbs develop in only a small fraction of HIV-infected individuals, all contain rare features such as extensive mutation, insertions, deletions, and/or long complementarity-determining regions, and some are polyreactive, casting doubt on whether bnAbs to HIV can be reliably induced by vaccination. We engineered two potent VRC01-class bnAbs that minimized rare features. According to a quantitative features frequency analysis, the set of features for one of these minimally mutated bnAbs compared favorably with all 68 HIV bnAbs analyzed and was similar to antibodies elicited by common vaccines. This same minimally mutated bnAb lacked polyreactivity in four different assays. We then divided the minimal mutations into spatial clusters and dissected the epitope components interacting with those clusters, by mutational and crystallographic analyses coupled with neutralization assays. Finally, by synthesizing available data, we developed a working-concept boosting strategy to select the mutation clusters in a logical order following a germline-targeting prime. We have thus developed potent HIV bnAbs that may be more tractable vaccine goals compared to existing bnAbs, and we have proposed a strategy to elicit them. This reductionist approach to vaccine design, guided by antibody and antigen structure, could be applied to design candidate vaccines for other HIV bnAbs or protective Abs against other pathogens.     

A distributed clustering algorithm for large-scale dynamic networks

We propose an algorithm that builds and maintains clusters over a network subject to mobility. This algorithm is fully decentralized and makes all the different clusters grow concurrently. The algorithm uses circulating tokens that collect data and move according to a random walk traversal scheme. Their task consists in (i) creating a cluster with the nodes it discovers and (ii) managing the cluster expansion; all decisions affecting the cluster are taken only by a node that owns the token. The size of each cluster is maintained higher than m nodes (m is a parameter of the algorithm). The obtained clustering is locally optimal in the sense that, with only a local view of each clusters, it computes the largest possible number of clusters (i.e. the sizes of the clusters are as close to m as possible). This algorithm is designed as a decentralized control algorithm for large scale networks and is mobility-adaptive: after a series of topological changes, the algorithm converges to a clustering. This recomputation only affects nodes in clusters where topological changes happened, and in adjacent clusters.