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Many distantly related organisms have convergently evolved traits and lifestyles that enable them to live in similar ecological environments. However, the extent of phenotypic convergence evolving through the same or distinct genetic trajectories remains an open question. Here, we leverage a comprehensive dataset of genomic and phenotypic data from 1,049 yeast species in the subphylum Saccharomycotina (Kingdom Fungi, Phylum Ascomycota) to explore signatures of convergent evolution in cactophilic yeasts, ecological specialists associated with cacti. We inferred that the ecological association of yeasts with cacti arose independently approximately 17 times. Using a machine learning–based approach, we further found that cactophily can be predicted with 76% accuracy from both functional genomic and phenotypic data. The most informative feature for predicting cactophily was thermotolerance, which we found to be likely associated with altered evolutionary rates of genes impacting the cell envelope in several cactophilic lineages. We also identified horizontal gene transfer and duplication events of plant cell wall–degrading enzymes in distantly related cactophilic clades, suggesting that putatively adaptive traits evolved independently through disparate molecular mechanisms. Notably, we found that multiple cactophilic species and their close relatives have been reported as emerging human opportunistic pathogens, suggesting that the cactophilic lifestyle—and perhaps more generally lifestyles favoring thermotolerance—might preadapt yeasts to cause human disease. This work underscores the potential of a multifaceted approach involving high-throughput genomic and phenotypic data to shed light onto ecological adaptation and highlights how convergent evolution to wild environments could facilitate the transition to human pathogenicity. 

Plant protein kinases form redundant signaling pathways to perceive microbial pathogens and activate immunity. Bacterial pathogens repress cellular immune responses by secreting effectors, some of which bind and inhibit multiple host kinases. To understand how broadly bacterial effectors may bind protein kinases and the function of these kinase interactors, we first tested kinase–effector (K-E) interactions using the Pseudomonas syringae pv. tomato–tomato pathosystem. We tested interactions between five individual effectors (HopAI1, AvrPto, HopA1, HopM1, and HopAF1) and 279 tomato kinases in tomato cells. Over half of the tested kinases interacted with at least one effector, and 48% of these kinases interacted with more than three effectors, suggesting a role in the defense. Next, we characterized the role of select multi-effector–interacting kinases and revealed their roles in basal resistance, effector-triggered immunity (ETI), or programmed cell death (PCD). The immune function of several of these kinases was only detectable in the presence of effectors, suggesting that these kinases are critical when particular cell functions are perturbed or that their role is typically masked. To visualize the kinase networks underlying the cellular responses, we derived signal-specific networks. A comparison of the networks revealed a limited overlap between ETI and basal immunity networks. In addition, the basal immune network complexity increased when exposed to some of the effectors. The networks were used to successfully predict the role of a new set of kinases in basal immunity. Our work indicates the complexity of the larger kinase-based defense network and demonstrates how virulence- and avirulence-associated bacterial effectors alter sectors of the defense network.