Vaccine induction of CD4-mimicking HIV-1 broadly neutralizing antibody precursors in macaques

The CD4-binding site (CD4bs) is a conserved epitope on HIV-1 envelope (Env) that can be targeted by protective broadly neutralizing antibodies (bnAbs). HIV-1 vaccines have not elicited CD4bs bnAbs for many reasons, including the occlusion of CD4bs by glycans, expansion of appropriate naive B cells with immunogens, and selection of functional antibody mutations. Here, we demonstrate that immunization of macaques with a CD4bs-targeting immunogen elicits neutralizing bnAb precursors with structural and genetic features of CD4-mimicking bnAbs. Structures of the CD4bs nAb bound to HIV-1 Env demonstrated binding angles and heavy-chain interactions characteristic of all known human CD4-mimicking bnAbs. Macaque nAb were derived from variable and joining gene segments orthologous to the genes of human VH1-46-class bnAb. This vaccine study initiated in primates the B cells from which CD4bs bnAbs can derive, accomplishing the key first step in the development of an effective HIV-1 vaccine.

Engineered immunogens to elicit antibodies against conserved coronavirus epitopes

Immune responses to SARS-CoV-2 primarily target the receptor binding domain of the spike protein, which continually mutates to escape acquired immunity. Other regions in the spike S2 subunit, such as the stem helix and the segment encompassing residues 815-823 adjacent to the fusion peptide, are highly conserved across sarbecoviruses and are recognized by broadly reactive antibodies, providing hope that vaccines targeting these epitopes could offer protection against both current and emergent viruses. Here we employ computational modeling to design scaffolded immunogens that display the spike 815-823 peptide and the stem helix epitopes without the distracting and immunodominant receptor binding domain. These engineered proteins bind with high affinity and specificity to the mature and germline versions of previously identified broadly protective human antibodies. Epitope scaffolds interact with both sera and isolated monoclonal antibodies with broadly reactivity from individuals with pre-existing SARS-CoV-2 immunity. When used as immunogens, epitope scaffolds elicit sera with broad betacoronavirus reactivity and protect as "boosts" against live virus challenge in mice, illustrating their potential as components of a future pancoronavirus vaccine.

Computing the inducibility of B cell lineages under a context-dependent model of affinity maturation: Applications to sequential vaccine design

A key challenge in B cell lineage-based vaccine design is understanding the inducibility of target neutralizing antibodies. We approach this problem through the use of detailed stochastic modeling of the somatic hypermutation process that occurs during affinity maturation. Under such a model, sequence mutation rates are context-dependent, rendering standard probability calculations for sequence evolution intractable. We develop an algorithmic approach to rapid, accurate approximation of key marginal sequence likelihoods required to inform modern sequential vaccine design strategies. These calculated probabilities are used to define an inducibility index for selecting among potential targets for immunogen design. We apply this approach to the problem of choosing targets for the design of boosting immunogens aimed at elicitation of the HIV broadly-neutralizing antibody DH270min11.

Engineered Immunogens to Elicit Antibodies Against Conserved Coronavirus Epitopes

Immune responses to SARS-CoV-2 primarily target the receptor binding domain of the spike protein, which continually mutates to escape acquired immunity. Other regions in the spike S2 subunit, such as the stem helix and the segment encompassing residues 815-823 adjacent to the fusion peptide, are highly conserved across sarbecoviruses and are recognized by broadly reactive antibodies, providing hope that vaccines targeting these epitopes could offer protection against both current and emergent viruses. Here we employed computational modeling to design scaffolded immunogens that display the spike 815-823 peptide and the stem helix epitopes without the distracting and immunodominant RBD. These engineered proteins bound with high affinity and specificity to the mature and germline versions of previously identified broadly protective human antibodies. Epitope scaffolds interacted with both sera and isolated monoclonal antibodies with broadly reactivity from individuals with pre-existing SARS-CoV-2 immunity. When used as immunogens, epitope scaffolds elicited sera with broad betacoronavirus reactivity and protected as "boosts" against live virus challenge in mice, illustrating their potential as components of a future pancoronavirus vaccine.

Mapping the environmental fitness landscape of a synthetic gene circuit

Gene expression actualizes the organismal phenotypes encoded within the genome in an environment-dependent manner. Among all encoded phenotypes, cell population growth rate (fitness) is perhaps the most important, since it determines how well-adapted a genotype is in various environments. Traditional biological measurement techniques have revealed the connection between the environment and fitness based on the gene expression mean. Yet, recently it became clear that cells with identical genomes exposed to the same environment can differ dramatically from the population average in their gene expression and division rate (individual fitness). For cell populations with bimodal gene expression, this difference is particularly pronounced, and may involve stochastic transitions between two cellular states that form distinct sub-populations. Currently it remains unclear how a cell population's growth rate and its subpopulation fractions emerge from the molecular-level kinetics of gene networks and the division rates of single cells. To address this question we developed and quantitatively characterized an inducible, bistable synthetic gene circuit controlling the expression of a bifunctional antibiotic resistance gene in Saccharomyces cerevisiae. Following fitness and fluorescence measurements in two distinct environments (inducer alone and antibiotic alone), we applied a computational approach to predict cell population fitness and subpopulation fractions in the combination of these environments based on stochastic cellular movement in gene expression space and fitness space. We found that knowing the fitness and nongenetic (cellular) memory associated with specific gene expression states were necessary for predicting the overall fitness of cell populations in combined environments. We validated these predictions experimentally and identified environmental conditions that defined a “sweet spot” of drug resistance. These findings may provide a roadmap for connecting the molecular-level kinetics of gene networks to cell population fitness in well-defined environments, and may have important implications for phenotypic variability of drug resistance in natural settings.

It is common belief that the properties of cells depend on their environment and on the genes they carry. Yet, many cases exist where individual cells in the same environment behave very differently, despite sharing the same genes. This creates a problem when we try to explain the behavior of a cell population based on the genes these cells carry. For example, it is difficult to predict how fast the overall number of cells increases based on the genes they all carry if some cells divide much faster than others. We addressed this problem using a synthetic gene circuit that could randomly allocate cells into drug resistant and drug sensitive states. We could control the fractions of cells and the time they resided in these states by adding an inducer to the growth solution. After measuring how fast cells transitioned between these two states, and how fast they grew in inducer and drug alone, we predicted computationally how fast they should grow when both inducer and drug are present. We validated experimentally these predictions and found a “sweet spot” of drug resistance where cells grew fastest in the presence of drugs.

Modeling synthetic gene oscillators

Genetic oscillators have long held the fascination of experimental and theoretical synthetic biologists alike. From an experimental standpoint, the creation of synthetic gene oscillators represents a yardstick by which our ability to engineer synthetic gene circuits can be measured. For theorists, synthetic gene oscillators are a playground in which to test mathematical models for the dynamics of gene regulation. Historically, mathematical models of synthetic gene circuits have varied greatly. Often, the differences are determined by the level of biological detail included within each model, or which approximation scheme is used. In this review, we examine, in detail, how mathematical models of synthetic gene oscillators are derived and the biological processes that affect the dynamics of gene regulation.

Evolution of a single gene highlights the complexity underlying molecular descriptions of fitness

Evolution by natural selection is the driving force behind the endless variation we see in nature, yet our understanding of how changes at the molecular level give rise to different phenotypes and altered fitness at the population level remains inadequate. The reproductive fitness of an organism is the most basic metric that describes the chance that an organism will succeed or fail in its environment and it depends upon a complex network of inter- and intramolecular interactions. A deeper understanding of the quantitative relationships relating molecular evolution to adaptation, and consequently fitness, can guide our understanding of important issues in biomedicine such as drug resistance and the engineering of new organisms with applications to biotechnology. We have developed the "weak link" approach to determine how changes in molecular structure and function can relate to fitness and evolutionary outcomes. By replacing adenylate kinase (AK), an essential gene, in a thermophile with a homologous AK from a mesophile we have created a maladapted weak link that produces a temperature-sensitive phenotype. The recombinant strain adapts to nonpermissive temperatures through point mutations to the weak link that increase both stability and activity of the enzyme AK at higher temperatures. Here, we propose a fitness function relating enzyme activity to growth rate and use it to create a dynamic model of a population of bacterial cells. Using metabolic control analysis we show that the growth rate exhibits thresholdlike behavior, saturating at high enzyme activity as other reactions in the energy metabolism pathway become rate limiting. The dynamic model accurately recapitulates observed evolutionary outcomes. These findings suggest that in vitro enzyme kinetic data, in combination with metabolic network analysis, can be used to create fitness functions and dynamic models of evolution within simple metabolic systems.