Broadly neutralizing antibodies (bnAbs) that neutralize diverse variants of a particular virus are of considerable therapeutic interest1. Recent advances have enabled us to isolate and engineer these antibodies as therapeutics, but eliciting them through vaccination remains challenging, in part due to our limited understanding of how antibodies evolve breadth2. Here, we analyze the landscape by which an anti-influenza receptor binding site (RBS) bnAb, CH65, evolved broad affinity to diverse H1 influenza strains3,4. We do this by generating an antibody library of all possible evolutionary intermediates between the unmutated common ancestor (UCA) and the affinity-matured CH65 antibody and measure the affinity of each intermediate to three distinct H1 antigens. We find that affinity to each antigen requires a specific set of mutations - distributed across the variable light and heavy chains - that interact non-additively (i.e., epistatically). These sets of mutations form a hierarchical pattern across the antigens, with increasingly divergent antigens requiring additional epistatic mutations beyond those required to bind less divergent antigens. We investigate the underlying biochemical and structural basis for these hierarchical sets of epistatic mutations and find that epistasis between heavy chain mutations and a mutation in the light chain at the VH-VL interface is essential for binding a divergent H1. Collectively, this work is the first to comprehensively characterize epistasis between heavy and light chain mutations and shows that such interactions are both strong and widespread. Together with our previous study analyzing a different class of anti-influenza antibodies5, our results implicate epistasis as a general feature of antibody sequence-affinity landscapes that can potentiate and constrain the evolution of breadth.

Bacterial colonies benefit from cellular heterogeneity, with cells differentiating into diverse states of physiology and gene expression. As colonies grow, such cells in distinct states arrange into spatial patterns. To uncover the functional role of these emergent patterns, we must understand how they arise from cellular growth, phenotypic inheritance, and mechanical interactions among cells. Here we present a simple, agent-based model to predict patterns formed by motile and extracellular matrix-producing cells in developing populations of Bacillus subtilis bacteria. By incorporating phenotypic inheritance, differential mechanical interactions of the two cell types, and the escape of peripheral motile cells, our model predicts the emergence of a pattern: matrix cells surround a fractal-like population of interior motile cells. We find that, while some properties of the emergent motile-matrix interface depend on the initial spatial arrangement of cells, the distribution of motile cells at large radii are a product solely of the model’s growth mechanism. Using a box-counting analysis, we find that the emergent motile-matrix interface exhibits a fractal dimension that increases as biofilms grow but eventually reaches a maximum as the thickness of the peripheral layer of matrix exceeds the capacity of the inner cells to push matrix cells out of the way. We find that the presence of the fractal interface correlates with a larger colony growth rate and increases the local proximity of motile and matrix cells, which could promote resource sharing. Our results show that simple computational models can account for morphological features of active systems like bacterial colonies, where colony-level phenotypes emerge from single cell-level properties and cells modifying their own environment.Author summaryLike cells in our bodies, bacterial cells can differentiate into different cell types, which perform different roles in colonies. During the growth of Bacillus subtilis colonies, motile cells, which can swim, and matrix cells, which produce sticky polymers to adhere cells together, form reproducible spatial patterns. Multiple factors could drive the formation of these patterns, including inheritance of the motility and matrix states as cells divide, and different mechanical interactions between different cell types as they push each other around during growth. We created an agent-based computational model, in which we represent bacterial cells as occupying squares within a grid. We find that through inheritance of motile and matrix state and greater resistance to physical pushing by matrix cells, our model produces patterns similar to those observed in experiments—an exterior population of matrix cells surrounding an interior group of motile cells with fractal arms that branch into the outer matrix layer. Our results show that simple models can account for complex phenomena like the growth of heterogeneous bacterial colonies.

No abstract