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Data from: Evidence for local adaptation to extreme heat across populations of a widespread tree from quantitative proteomics

posted on 2022-06-11, 04:07 authored by Timothy Maher, Mehdi Mirzaei, Dana Pascovici, Ian J. Wright, Paul A. Haynes, Rachael V. Gallagher
1. Heat waves are increasing in frequency and intensity globally with negative consequences for biological function. Assessing the effect of extreme heat on species requires an understanding of their adaptive capacity for mitigating physiological damage. Where long-term exposure to hot conditions in natural populations provides sufficient selection pressure, populations should exhibit signals of adaptive thermotolerance to temperature extremes. 2. Using quantitative proteomics, we tested this idea in the widespread and commercially-important tree species Eucalyptus grandis (Flooded Gum). Seedlings from six natural populations of E. grandis spanning a 2000 km gradient were exposed to a four-day extreme heat treatment (42-24°C day-night cycle) in experimental growth chambers. Populations differed in their long-term exposure to extreme heat conditions, defined both as the number of days annually ≥15°C above mean annual temperature (MAT), and average number of days annually with temperature maxima ≥ 35°C between 1960-1990. 3. Long-term exposure to extreme heat conditions in the field predicted the protein-level response of E. grandis to experimental heat waves. Relationships between long-term extreme heat exposure and protein increases were positive and linear for all combinations of extreme heat (days ≥15°C above MAT, mean days with temperature maxima ≥35°C annually) and expression (all differentially expressed proteins, isolated heat shock proteins, proteins involved in molecular stress responses). 4. Although extreme climate events are typically rare (e.g. 1 day 15° ≥ MAT per 5 years in some populations in our study), E. grandis populations sampled from across a 2000km range exhibit a clear capacity to increase expression of proteins involved in heat stress in response to simulated heat wave exposure. Presumably they respond similarly under natural heat wave conditions. 5. We show that a long-lived species with a broad environmental niche exhibits adaptive variation in protein response to temperature extremes at the population level. This implies that restoration, translocation and silvicultural programs should consider the molecular response of source populations to climatic extremes to maximise success under future climates. 6. Tree populations with low exposure to extreme heat conditions may be limited in their ability to respond to heat wave events, potentially limiting their adaptive capacity to withstand novel climate conditions.

Usage Notes

Protein expression under heat wave conditionsProtein ratios from quantitative proteomics of leaf samples collected after a four-day heat wave (42 degree) and control (28 degree) treatment for Eucalyptus grandis. Protein identity and quantity were determined using Proteome Discoverer V1.3 software (Thermo Scientific, United States) run from a local MASCOT server (Matrix Science, London, U.K.). The E. grandis protein sequences available through UniProt (, n = 44,150 proteins; October 2016) were used as a reference search database. The following parameters were used for peptide to spectrum matching in MASCOT: MS tolerance, ±10 ppm; enzyme, trypsin with one missed cleavage; fragment mass error, 0.1 Da; static modifications, carbamidomethylation of cysteine and 10-plex TMT tags on lysine residues and peptide N-termini; variable modifications, oxidation of methionine and deamidation of asparagine and glutamine. Spectra were also searched against a reversed-sequenced decoy database to determine false discovery rates (FDR) and filtered at a maximum FDR cut-off of 1% at the protein level. Only peptides below the MASCOT significance threshold filter of p = 0.05 were included in the final dataset, and proteins with at least two unique peptides were regarded as confident identifications. Relative quantification of proteins was achieved by pairwise comparison of TMT intensities, using the ratio of the labels for each of the treatment replicates (numerator) versus the labels of their corresponding pooled control reference (denominator).Appendix_s2.xlsx


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