The environmental pathogen Cryptococcus neoformans claims over 180,000 lives each year. Survival of this basidiomycete at host CO₂ concentrations has only recently been considered an important virulence trait. Through screening gene knockout libraries constructed in a CO₂-tolerant clinical strain, we found mutations leading to CO₂ sensitivity are enriched in pathways activated by heat stress, including calcineurin, Ras1-Cdc24, cell wall integrity, and Regulator of Ace2 and Morphogenesis (RAM). Overexpression of Cbk1, the conserved terminal kinase of the RAM pathway, partially restored defects of these mutants at host CO₂ or temperature levels. In ascomycetes such as Saccharomyces cerevisiae and Candida albicans, transcription factor Ace2 is an important target of Cbk1, activating genes responsible for cell separation. However, no Ace2 homolog or any downstream component of the RAM pathway has been identified in basidiomycetes. Through in vitro evolution and comparative genomics, we characterized mutations in suppressors of cbk1D in C. neoformans that partially rescued defects in CO₂ tolerance, thermotolerance, and morphology. One suppressor is the RNA translation repressor Ssd1, which is highly conserved in ascomycetes and basidiomycetes. The other is a novel ribonuclease domain-containing protein, here named PSC1, which is present in basidiomycetes and humans but surprisingly absent in most ascomycetes. Loss of Ssd1 in cbk1D partially restored cryptococcal ability to survive and amplify in the inhalation and intravenous murine models of cryptococcosis. Our discoveries highlight the overlapping regulation of CO₂ tolerance and thermotolerance, the essential role of the RAM pathway in cryptococcal adaptation to the host condition, and the potential importance of post-transcriptional control of virulence traits in this global pathogen.

Transmission of SARS-CoV-2 from humans to other species threatens wildlife conservation and may create novel sources of viral diversity for future zoonotic transmission. A variety of computational heuristics have been developed to pre-emptively identify susceptible host species based on variation in the angiotensin-converting enzyme 2 (ACE2) receptor used for viral entry. However, the predictive performance of these heuristics remains unknown. Using a newly compiled database of 96 species, we show that, while variation in ACE2 can be used by machine learning models to accurately predict animal susceptibility to sarbecoviruses (accuracy = 80.2%, binomial confidence interval [CI]: 70.8–87.6%), the sites informing predictions have no known involvement in virus binding and instead recapitulate host phylogeny. Models trained on host phylogeny alone performed equally well (accuracy = 84.4%, CI: 75.5–91.0%) and at a level equivalent to retrospective assessments of accuracy for previously published models. These results suggest that the predictive power of ACE2-based models derives from strong correlations with host phylogeny rather than processes which can be mechanistically linked to infection biology. Further, biased availability of ACE2 sequences misleads projections of the number and geographic distribution of at-risk species. Models based on host phylogeny reduce this bias, but identify a very large number of susceptible species, implying that model predictions must be combined with local knowledge of exposure risk to practically guide surveillance. Identifying barriers to viral infection or onward transmission beyond receptor binding and incorporating data which are independent of host phylogeny will be necessary to manage the ongoing risk of establishment of novel animal reservoirs of SARS-CoV-2.