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Abstract The budding yeast Saccharomyces cerevisiae is a proven model organism for elucidating conserved eukaryotic biology, but to date its extracellular vesicle (EV) biology is understudied. Here, we show yeast transmit information through the extracellular medium that increases survival when confronted with heat stress and demonstrate the EV‐enriched samples mediate this thermotolerance transfer. These samples contain vesicle‐like particles that are exosome‐sized and disrupting exosome biogenesis by targeting endosomal sorting complexes required for transport (ESCRT) machinery inhibits thermotolerance transfer. We find that Bro1, the yeast ortholog of the human exosome biomarker ALIX, is present in EV samples, and use Bro1 tagged with green fluorescent protein (GFP) to track EV release and uptake by endocytosis. Proteomics analysis reveals that heat shock protein 70 (HSP70) family proteins are enriched in EV samples that provide thermotolerance. We confirm the presence of the HSP70 ortholog stress‐seventy subunit A2 (Ssa2) in EV samples and find that mutant yeast cells lacking SSA2 produce EVs but they fail to transfer thermotolerance. We conclude that Ssa2 within exosomes shared between yeast cells contributes to thermotolerance. Through this work, we advance Saccharomyces cerevisiae as an emerging model organism for elucidating molecular details of eukaryotic EV biology and establish a role for exosomes in heat stress and proteostasis that seems to be evolutionarily conserved.

Land-atmosphere carbon exchange is known to be extremely heterogeneous in arctic ice-wedge polygonal tundra regions. In this study, a Kalman filter-based method was developed to estimate the spatio-temporal dynamics of daytime average net ecosystem exchange (NEEday) at 0.5-m resolution over a 550 m by 700 m study site. We integrated multi-scale, multi-type datasets, including normalized difference vegetation indices (NDVIs) obtained from a novel automated mobile sensor system (or tram system) and a greenness index map obtained from airborne imagery. We took advantage of the significant correlations between NDVI and NEEday identified based on flux chamber measurements. The weighted average of the estimated NEEday within the flux-tower footprint agreed with the flux tower data in term of its seasonal dynamics. We then evaluated the spatial variability of the growing season average NEEday, as a function of polygon geomorphic classes; i.e., the combination of polygon types—which are known to present different degradation stages associated with permafrost thaw—and microtopographic features (i.e., troughs, centers and rims). Our study suggests the importance of considering microtopographic features and their spatial coverage in computing spatially aggregated carbon exchange.

There is significant spatial and temporal variability associated with greenhouse gas (GHG) fluxes in high-latitude Arctic tundra environments. The objectives of this study are to investigate temporal variability in CO2 and CH4 fluxes at Barrow, AK and to determine the factors causing this variability using a novel entropy-based classification scheme. In particular, we analyzed which geomorphic, soil, vegetation and climatic properties most explained the variability in GHG fluxes (opaque chamber measurements) during the growing season over three successive years. Results indicate that multi-year variability in CO2 fluxes was primarily associated with soil temperature variability as well as vegetation dynamics during the early and late growing season. Temporal variability in CH4 fluxes was primarily associated with changes in vegetation during the growing season and its interactions with primary controls like seasonal thaw. Polygonal ground features, which are common to Arctic regions, also demonstrated significant multi-year variability in GHG fluxes. Our results can be used to prioritize field sampling strategies, with an emphasis on measurements collected at locations and times that explain the most variability in GHG fluxes. For example, we found that sampling primary environmental controls at the centers of high centered polygons in the month of September (when freeze-back period begins) can provide significant constraints on GHG flux variability - a requirement for accurately predicting future changes to GHG fluxes. Overall, entropy results document the impact of changing environmental conditions (e.g., warming, growing season length) on GHG fluxes, thus providing clues concerning the manner in which ecosystem properties may be shifted regionally in a future climate.