Natural variation of immune epitopes reveals intrabacterial antagonism

Plants and animals detect biomolecules termed Microbe-Associated Molecular Patterns (MAMPs) and induce immunity. Agricultural production is severely impacted by pathogens which can be controlled by transferring immune receptors. However, most studies use a single MAMP epitope and the impact of diverse multi-copy MAMPs on immune induction is unknown. Here we characterized the epitope landscape from five proteinaceous MAMPs across 4,228 plant-associated bacterial genomes. Despite the diversity sampled, natural variation was constrained and experimentally testable. Immune perception in both Arabidopsis and tomato depended on both epitope sequence and copy number variation. For example, Elongation Factor Tu is predominantly single copy and 92% of its epitopes are immunogenic. Conversely, 99.9% of bacterial genomes contain multiple Cold Shock Proteins and 46% carry a non-immunogenic form. We uncovered a new mechanism for immune evasion, intrabacterial antagonism, where a non-immunogenic Cold Shock Protein blocks perception of immunogenic forms encoded in the same genome. These data will lay the foundation for immune receptor deployment and engineering based on natural variation.

Evolutionary gain and loss of a plant pattern-recognition receptor for HAMP recognition

As a first step in innate immunity, pattern recognition receptors (PRRs) recognize distinct pathogen and herbivore-associated molecular patterns and mediate activation of immune responses, but specific steps in the evolution of new PRR sensing functions are not well understood. We employed comparative genomic and functional analyses to define evolutionary events leading to the sensing of the herbivore-associated peptide inceptin (In11) by the PRR Inceptin Receptor (INR) in legume plant species. Existing and de novo genome assemblies revealed that the presence of a functional INR gene corresponded with ability to respond to In11 across ~53 million years (my) of evolution. In11 recognition is unique to the clade of Phaseoloid legumes, and only a single clade of INR homologues from Phaseoloids was functional in a heterologous model. The syntenic loci of several non-Phaseoloid outgroup species nonetheless contain non-functional INR-like homologues, suggesting that an ancestral gene insertion event and diversification preceded the evolution of a specific INR receptor function ~28 mya. Chimeric and ancestrally reconstructed receptors indicated that 16 amino acid differences in the C1 leucine-rich repeat domain and C2 intervening motif mediate gain of In11 recognition. Thus, high PRR diversity was likely followed by a small number of mutations to expand innate immune recognition to a novel peptide elicitor. Analysis of INR evolution provides a model for functional diversification of other germline-encoded PRRs.

Biological networks across scales

Many biological systems across scales of size and complexity exhibit a time-varying complex network structure that emerges and self-organizes as a result of interactions with the environment. Network interactions optimize some intrinsic cost functions that are unknown and involve for example energy efficiency, robustness, resilience, and frailty. A wide range of networks exist in biology, from gene regulatory networks important for organismal development, protein interaction networks that govern physiology and metabolism, and neural networks that store and convey information to networks of microbes that form microbiomes within hosts, animal contact networks that underlie social systems, and networks of populations on the landscape connected by migration. Increasing availability of extensive (big) data is amplifying our ability to quantify biological networks. Similarly, theoretical methods that describe network structure and dynamics are being developed. Beyond static networks representing snapshots of biological systems, collections of longitudinal data series can help either at defining and characterizing network dynamics over time or analyzing the dynamics constrained to networked architectures. Moreover, due to interactions with the environment and other biological systems, a biological network may not be fully observable. Also, subnetworks may emerge and disappear as a result of the need for the biological system to cope with for example invaders or new information flows. The confluence of these developments renders tractable the question of how the structure of biological networks predicts and controls network dynamics. In particular, there may be structural features that result in homeostatic networks with specific higher-order statistics (e.g., multifractal spectrum), which maintain stability over time through robustness and/or resilience to perturbation. Alternative, plastic networks may respond to perturbation by (adaptive to catastrophic) shifts in structure. Here, we explore the opportunity for discovering universal laws connecting the structure of biological networks with their function, positioning them on the spectrum of time-evolving network structure, i.e. dynamics of networks, from highly stable to exquisitely sensitive to perturbation. If such general laws exist, they could transform our ability to predict the response of biological systems to perturbations-an increasingly urgent priority in the face of anthropogenic changes to the environment that affect life across the gamut of organizational scales.