in

Abiotic and past climatic conditions drive protein abundance variation among natural populations of the caddisfly Crunoecia irrorata

  • 1.

    West-Eberhard, M. J. Developmental Plasticity and Evolution (Oxford University Press, Oxford, 2003).

    Google Scholar 

  • 2.

    Beldade, P., Mateus, A. R. A. & Keller, R. A. Evolution and molecular mechanisms of adaptive developmental plasticity. Mol. Ecol. 20, 1347–1363 (2011).

    PubMed  Article  PubMed Central  Google Scholar 

  • 3.

    Dall, S. R. X., McNamara, J. M. & Leimar, O. Genes as cues: Phenotypic integration of genetic and epigenetic information from a Darwinian perspective. Trends Ecol. Evol. 30, 327–333 (2015).

    PubMed  Article  PubMed Central  Google Scholar 

  • 4.

    Scheiner, S. M. Genetics and evolution of phenotypic plasticity. Annu. Rev. Ecol. Syst. 24, 35–68 (1993).

    Article  Google Scholar 

  • 5.

    Ovaskainen, O. & Meerson, B. Stochastic models of population extinction. Trends Ecol. Evol. 25, 643–652 (2010).

    PubMed  Article  PubMed Central  Google Scholar 

  • 6.

    Lawson, C. R., Vindenes, Y., Bailey, L. & van de Pol, M. Environmental variation and population responses to global change. Ecol. Lett. 18, 724–736 (2015).

    PubMed  Article  PubMed Central  Google Scholar 

  • 7.

    Mayr, E. The growth of biological thought: Diversity, evolution, and inheritance. Am. Biol. Teach. 46, 462–463 (1984).

    Article  Google Scholar 

  • 8.

    Seebacher, F., White, C. R. & Franklin, C. E. Physiological plasticity increases resilience of ectothermic animals to climate change. Nat. Clim. Change 5, 61–66 (2015).

    ADS  Article  Google Scholar 

  • 9.

    Somero, G. N. The physiology of climate change: how potentials for acclimatization and genetic adaptation will determine ‘winners’ and ‘losers’. J. Exp. Biol. 213, 912–920 (2010).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 10.

    Riddell, E. A., Odom, J. P., Damm, J. D. & Sears, M. W. Plasticity reveals hidden resistance to extinction under climate change in the global hotspot of salamander diversity. Sci. Adv. 4, eaar5471 (2018).

    ADS  PubMed  PubMed Central  Article  Google Scholar 

  • 11.

    Noble, D. Claude Bernard, the first systems biologist, and the future of physiology. Exp. Physiol. 93, 16–26 (2008).

    PubMed  Article  PubMed Central  Google Scholar 

  • 12.

    Ideker, T., Galitski, T. & Hood, L. A new approach to decoding life: Systems biology. Annu. Rev. Genomics Hum. Genet. 2, 343–372 (2001).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 13.

    Whitehead, A. & Crawford, D. L. Variation within and among species in gene expression: Raw material for evolution. Mol. Ecol. 15, 1197–1211 (2006).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 14.

    Wittkopp, P. J., Haerum, B. K. & Clark, A. G. Regulatory changes underlying expression differences within and between Drosophila species. Nat. Genet. 40, 346–350 (2008).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 15.

    Gonzalez, E. G. et al. Population proteomics of the European hake (Merluccius merluccius). J. Proteome Res. 9, 6392–6404 (2010).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 16.

    Papakostas, S. et al. A proteomics approach reveals divergent molecular responses to salinity in populations of European whitefish (Coregonus lavaretus ). Mol. Ecol. 21, 3516–3530 (2012).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 17.

    Chevalier, F. et al. Proteomic investigation of natural variation between Arabidopsis ecotypes. Proteomics 4, 1372–1381 (2004).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 18.

    Mueller, R. S. et al. Ecological distribution and population physiology defined by proteomics in a natural microbial community. Mol. Syst. Biol. 6, 374 (2010).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  • 19.

    Snijder, B. & Pelkmans, L. Origins of regulated cell-to-cell variability. Nat. Rev. Mol. Cell Biol. 12, 119–125 (2011).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 20.

    Bradshaw, A. D. Evolutionary significance of phenotypic plasticity in plants. In Advances in Genetics Vol. 13 (eds Caspari, E. W. & Thoday, J. M.) 115–155 (Academic Press, New York, 1965).

    Google Scholar 

  • 21.

    Colman-Lerner, A. et al. Regulated cell-to-cell variation in a cell-fate decision system. Nature 437, 699–706 (2005).

    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 22.

    Fisher, M. A. & Oleksiak, M. F. Convergence and divergence in gene expression among natural populations exposed to pollution. BMC Genomics 8, 108 (2007).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  • 23.

    Pujolar, J. M. et al. Surviving in a toxic world: transcriptomics and gene expression profiling in response to environmental pollution in the critically endangered European eel. BMC Genomics 13, 507 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • 24.

    Davies, S. W., Marchetti, A., Ries, J. B. & Castillo, K. D. Thermal and pCO2 stress elicit divergent transcriptomic responses in a resilient coral. Front. Mar. Sci. 3, 112 (2016).

  • 25.

    Palumbi, S. R., Barshis, D. J., Traylor-Knowles, N. & Bay, R. A. Mechanisms of reef coral resistance to future climate change. Science 344, 895–898 (2014).

    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 26.

    Gygi, S. P., Rochon, Y., Franza, B. R. & Aebersold, R. Correlation between protein and mRNA abundance in yeast. Mol. Cell. Biol. 19, 1720–1730 (1999).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • 27.

    Liu, Y., Beyer, A. & Aebersold, R. On the dependency of cellular protein levels on mRNA abundance. Cell 165, 535–550 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 28.

    Bludau, I. & Aebersold, R. Proteomic and interactomic insights into the molecular basis of cell functional diversity. Nat. Rev. Mol. Cell Biol. https://doi.org/10.1038/s41580-020-0231-2 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  • 29.

    Lodish, H. et al. Molecular Cell Biology (W. H. Freeman, New York, 2000).

    Google Scholar 

  • 30.

    Watson, J. D. Molecular Biology of the Gene (Pearson Education, London, 2004).

    Google Scholar 

  • 31.

    Giardi, M. T., Masojídek, J. & Godde, D. Effects of abiotic stresses on the turnover of the D1 reaction centre II protein. Physiol. Plant. 101, 635–642 (1997).

    CAS  Article  Google Scholar 

  • 32.

    Raj, A. & van Oudenaarden, A. Nature, nurture, or chance: Stochastic gene expression and its consequences. Cell 135, 216–226 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • 33.

    Kærn, M., Elston, T. C., Blake, W. J. & Collins, J. J. Stochasticity in gene expression: From theories to phenotypes. Nat. Rev. Genet. 6, 451–464 (2005).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  • 34.

    Nikinmaa, M., Tiihonen, K. & Paajaste, M. Adrenergic control of red cell pH in salmonid fish: Roles of the sodium/proton exchange, Jacobs-Stewart cycle and membrane potential. J. Exp. Biol. 154, 257–271 (1990).

    CAS  Google Scholar 

  • 35.

    Pavlov, M. Y. & Ehrenberg, M. Optimal control of gene expression for fast proteome adaptation to environmental change. Proc. Natl. Acad. Sci. USA 110, 20527–20532 (2013).

    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 36.

    Adams, S. M., Giesy, J. P., Tremblay, L. A. & Eason, C. T. The use of biomarkers in ecological risk assessment: recommendations from the Christchurch conference on Biomarkers in Ecotoxicology. Biomarkers 6, 1–6 (2001).

    CAS  PubMed  Article  Google Scholar 

  • 37.

    Diz, A. P., Truebano, M. & Skibinski, D. O. F. The consequences of sample pooling in proteomics: An empirical study. Electrophoresis 30, 2967–2975 (2009).

    CAS  PubMed  Article  Google Scholar 

  • 38.

    Karp, N. A. & Lilley, K. S. Investigating sample pooling strategies for DIGE experiments to address biological variability. Proteomics 9, 388–397 (2009).

    CAS  PubMed  Article  Google Scholar 

  • 39.

    Bennike, T. B. et al. Comparing the proteome of snap frozen, RNAlater preserved, and formalin-fixed paraffin-embedded human tissue samples. EuPA Open Proteomics 10, 9–18 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 40.

    Johnsen, I. K. et al. Evaluation of a standardized protocol for processing adrenal tumor samples: Preparation for a European adrenal tumor bank. Horm. Metab. Res. 42, 93–101 (2010).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 41.

    Kruse, C. P. S., Basu, P., Luesse, D. R. & Wyatt, S. E. Transcriptome and proteome responses in RNAlater preserved tissue of Arabidopsis thaliana. PLoS ONE 12, e0175943–e0175943 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  • 42.

    Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).

    Article  Google Scholar 

  • 43.

    Hijmans, R. J. raster: Geographic Data Analysis and Modeling. R package version 3.3-13. https://CRAN.R-project.org/package=raster. (2020).

  • 44.

    Cox, J. et al. Andromeda: A peptide search engine integrated into the MaxQuant environment. J. Proteome Res. 10, 1794–1805 (2011).

    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 45.

    Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 46.

    Ebner, J. N., Ritz, D. & von Fumetti, S. Comparative proteomics of stenotopic caddisfly Crunoecia irrorata identifies acclimation strategies to warming. Mol. Ecol. 28, 4453–4469 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  • 47.

    Misof, B. et al. Phylogenomics resolves the timing and pattern of insect evolution. Science 346, 763–767 (2014).

    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 48.

    Jimenez-Morales, D., Campos, A.R. & Von Dollen, J. artMS: Analytical R tools for Mass Spectrometry. R package version 1.5.3. https://github.com/bioadavidjm/artMS. (2020).

  • 49.

    Chen, H. VennDiagram: Generate High-Resolution Venn and Euler Plots. R package version 1.6.20. https://CRAN.R-project.org/package=VennDiagram. (2018).

  • 50.

    R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/. (2019).

  • 51.

    Carrillo, B., Yanofsky, C., Laboissiere, S., Nadon, R. & Kearney, R. E. Methods for combining peptide intensities to estimate relative protein abundance. Bioinform. Oxf. Engl. 26, 98–103 (2010).

    CAS  Article  Google Scholar 

  • 52.

    Bolstad, B. preprocessCore: A collection of pre-processing functions. R package version 1.48.0. https://github.com/bmbolstad/proprocessCore. (2019).

  • 53.

    Hastie, T., Tibshirani, R., Balasubramanian, N. & Chu, G. impute: Imputation for microarray data. R package version 1.60.0. (2019).

  • 54.

    Bray, J. R. & Curtis, J. T. An ordination of the upland forest communities of Southern Wisconsin. Ecol. Monogr. 27, 325–349 (1957).

    Article  Google Scholar 

  • 55.

    Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-6. https://CRAN.R-project.org/package=vegan. (2019).

  • 56.

    Langfelder, P. & Horvath, S. WGCNA: An R package for weighted correlation network analysis. BMC Bioinform. 9, 559–559 (2008).

    Article  CAS  Google Scholar 

  • 57.

    Campbell-Staton, S. C. et al. Winter storms drive rapid phenotypic, regulatory, and genomic shifts in the green anole lizard. Science 357, 495–498 (2017).

    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 58.

    Campbell-Staton, S. C., Bare, A., Losos, J. B., Edwards, S. V. & Cheviron, Z. A. Physiological and regulatory underpinnings of geographic variation in reptilian cold tolerance across a latitudinal cline. Mol. Ecol. 27, 2243–2255 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 59.

    Horvath, S. Weighted network analysis: Applications in genomics and systems biology (Springer, New York, 2011).

    Google Scholar 

  • 60.

    Zhang, B. & Horvath, S. A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol. 4, Article17 (2005).

    MathSciNet  PubMed  MATH  Article  PubMed Central  Google Scholar 

  • 61.

    Smyth, G. K. limma: Linear models for microarray data. In Bioinformatics and Computational Biology Solutions Using R and Bioconductor (eds Gentleman, R. et al.) 397–420 (Springer, New York, 2005). https://doi.org/10.1007/0-387-29362-0_23.

    Google Scholar 

  • 62.

    Goodrich, B., Gabry, J., Ali, I. & Brilleman, S. rstanarm: Bayesian applied regression modeling via Stan. R package version 2.17.4. (Comprehensive R Archive Network (CRAN), 2018).

  • 63.

    Stearns, S. C. & Koella, J. C. The evolution of phenotypic plasticity in life-history traits: Predictions of reaction norms for age and size at maturity. Evolution 40, 893–913 (1986).

    PubMed  Article  PubMed Central  Google Scholar 

  • 64.

    Schmalhausen, I. I. Factors of Evolution: The Theory of Stabilizing Selection (Blakiston, Philadelphia, 1949).

    Google Scholar 

  • 65.

    Dray, S. & Dufour, A.-B. The ade4 package: Implementing the duality diagram for ecologists. J. Stat. Softw. 22, 1–20 (2007).

    Article  Google Scholar 

  • 66.

    Hijmans, R. J. geosphere: Spherical Trigonometry. R package version 1.5-10. https://CRAN.R-project.org/package=geosphere. (2019).

  • 67.

    Rieder, V. et al. DISMS2: A flexible algorithm for direct proteome-wide distance calculation of LC-MS/MS runs. BMC Bioinform. 18, 148 (2017).

  • 68.

    Grüning, B. et al. Bioconda: Sustainable and comprehensive software distribution for the life sciences. Nat. Methods 15, 475–476 (2018).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  • 69.

    Bairoch, A. & Apweiler, R. The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res. 28, 45–48 (2000).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • 70.

    Jones, P. et al. InterProScan 5: Genome-scale protein function classification. Bioinformatics 30, 1236–1240 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • 71.

    Tatusov, R. L., Galperin, M. Y., Natale, D. A. & Koonin, E. V. The COG database: A tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Res. 28, 33–36 (2000).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • 72.

    Huerta-Cepas, J. et al. EggNOG 5.0: A hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 47, D309–D314 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 73.

    Huerta-Cepas, J. et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-Mapper. Mol. Biol. Evol. 34, 2115–2122 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • 74.

    Alexa, A. & Rahnenfuhrer, J. topGO: Enrichment Analysis for Gene Ontology. R package version 2.38.1. (2019).

  • 75.

    El-Gebali, S. et al. The Pfam protein families database in 2019. Nucleic Acids Res. 47, D427–D432 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • 76.

    Fang, H. & Gough, J. dcGO: Database of domain-centric ontologies on functions, phenotypes, diseases and more. Nucleic Acids Res. 41, D536–D544 (2013).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 77.

    Kuhn, N. J., Setlow, B. & Setlow, P. Manganese(II) activation of 3-phosphoglycerate mutase of Bacillus megaterium: pH-Sensitive interconversion of active and inactive forms. Arch. Biochem. Biophys. 306, 342–349 (1993).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 78.

    Chander, M., Setlow, B. & Setlow, P. The enzymatic activity of phosphoglycerate mutase from gram-positive endospore-forming bacteria requires Mn2+ and is pH sensitive. Can. J. Microbiol. 44, 759–767 (1998).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 79.

    Ferrer, M., Chernikova, T. N., Yakimov, M. M., Golyshin, P. N. & Timmis, K. N. Chaperonins govern growth of Escherichia coli at low temperatures. Nat. Biotechnol. 21, 1266–1267 (2003).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 80.

    Strocchi, M., Ferrer, M., Timmis, K. N. & Golyshin, P. N. Low temperature-induced systems failure in Escherichia coli: Insights from rescue by cold-adapted chaperones. Proteomics 6, 193–206 (2006).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 81.

    Visudtiphole, V., Watthanasurorot, A., Klinbunga, S., Menasveta, P. & Kirtikara, K. Molecular characterization of Calreticulin: A biomarker for temperature stress responses of the giant tiger shrimp Penaeus monodon. Aquaculture 308, S100–S108 (2010).

    CAS  Article  Google Scholar 

  • 82.

    Wehrly, K. E., Wang, L. & Mitro, M. Field-based estimates of thermal tolerance limits for trout: Incorporating exposure time and temperature fluctuation. Trans. Am. Fish. Soc. 136, 365–374 (2007).

    Article  Google Scholar 

  • 83.

    Alberts, B. et al. Molecular Biology of the Cell (Garland Science, New York, 2002).

    Google Scholar 

  • 84.

    Hagner-Holler, S., Pick, C., Girgenrath, S., Marden, J. H. & Burmester, T. Diversity of stonefly hexamerins and implication for the evolution of insect storage proteins. Insect Biochem. Mol. Biol. 37, 1064–1074 (2007).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 85.

    Descazeaud, V., Mestre, E., Marquet, P. & Essig, M. Calcineurin regulation of cytoskeleton organization: A new paradigm to analyse the effects of calcineurin inhibitors on the kidney. J. Cell. Mol. Med. 16, 218–227 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • 86.

    Urra, H. et al. IRE1α governs cytoskeleton remodelling and cell migration through a direct interaction with filamin A. Nat. Cell Biol. 20, 942–953 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 87.

    Tong, M. & Jiang, Y. FK506-binding proteins and their diverse functions. Curr. Mol. Pharmacol. 9, 48–65 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  • 88.

    Miranti, C. K. & Brugge, J. S. Sensing the environment: A historical perspective on integrin signal transduction. Nat. Cell Biol. 4, E83–E90 (2002).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 89.

    Walsh, C. T., Garneau-Tsodikova, S. & Gatto, G. J. Protein posttranslational modifications: The chemistry of proteome diversifications. Angew. Chem. Int. Ed. 44, 7342–7372 (2005).

    CAS  Article  Google Scholar 

  • 90.

    Snape, J. R., Maund, S. J., Pickford, D. B. & Hutchinson, T. H. Ecotoxicogenomics: The challenge of integrating genomics into aquatic and terrestrial ecotoxicology. Aquat. Toxicol. 67, 143–154 (2004).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 91.

    Nikinmaa, M. & Rytkönen, K. T. Functional genomics in aquatic toxicology—Do not forget the function. Aquat. Toxicol. 105, 16–24 (2011).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 92.

    Kearney, E. B., Ackrell, B. A. C., Mayr, M. & Singer, T. P. Activation of succinate dehydrogenase by anions and pH. J. Biol. Chem. 249, 2016–2020 (1974).

    CAS  PubMed  PubMed Central  Google Scholar 

  • 93.

    Bissoli, G. et al. Peptidyl-prolyl cis-trans isomerase ROF2 modulates intracellular pH homeostasis in Arabidopsis. Plant J. Cell Mol. Biol. 70, 704–716 (2012).

    CAS  Article  Google Scholar 

  • 94.

    Simčič, T. & Brancelj, A. Effects of pH on electron transport system (ETS) activity and oxygen consumption in Gammarus fossarum, Asellus aquaticus and Niphargus sphagnicolus. Freshw. Biol. 51, 686–694 (2006).

    Article  CAS  Google Scholar 

  • 95.

    Kadrmas, J. L. & Beckerle, M. C. The LIM domain: From the cytoskeleton to the nucleus. Nat. Rev. Mol. Cell Biol. 5, 920–931 (2004).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 96.

    van der Flier, A. & Sonnenberg, A. Structural and functional aspects of filamins. Biochim. Biophys. Acta BBA Mol. Cell Res. 1538, 99–117 (2001).

    Article  Google Scholar 

  • 97.

    Sun, H. Q., Yamamoto, M., Mejillano, M. & Yin, H. L. Gelsolin, a multifunctional actin regulatory protein. J. Biol. Chem. 274, 33179–33182 (1999).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 98.

    Diskin, S. et al. Galectin-8 promotes cytoskeletal rearrangement in trabecular meshwork cells through activation of rho signaling. PLoS ONE 7, e44400 (2012).

  • 99.

    Motizuki, M., Yokota, S. & Tsurugi, K. Effect of low pH on organization of the actin cytoskeleton in Saccharomyces cerevisiae. Biochim. Biophys. Acta 1780, 179–184 (2008).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 100.

    Wang, F., Sampogna, R. V. & Ware, B. R. pH dependence of actin self-assembly. Biophys. J. 55, 293–298 (1989).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • 101.

    Sperelakis, N. Cell Physiology Source book (Academic Press, Amsterdam, 2012).

    Google Scholar 

  • 102.

    Tomanek, L. Proteomics to study adaptations in marine organisms to environmental stress. J. Proteomics 105, 92–106 (2014).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 103.

    Tomanek, L., Zuzow, M. J., Ivanina, A. V., Beniash, E. & Sokolova, I. M. Proteomic response to elevated PCO2 level in eastern oysters, Crassostrea virginica: Evidence for oxidative stress. J. Exp. Biol. 214, 1836–1844 (2011).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 104.

    Koritzinsky, M. et al. Two phases of disulfide bond formation have differing requirements for oxygen. J. Cell Biol. 203, 615–627 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • 105.

    L’Haridon, F. et al. A permeable cuticle is associated with the release of reactive oxygen species and induction of innate immunity. PLoS Pathog. 7, e1002148 (2011).

  • 106.

    Richards, A. G. Studies on arthropod cuticle—XIII: The penetration of dissolved oxygen and electrolytes in relation to the multiple barriers of the epicuticle. J. Insect Physiol. 1, 23–39 (1957).

    CAS  Article  Google Scholar 

  • 107.

    Wang, K. et al. Redox homeostasis: The linchpin in stem cell self-renewal and differentiation. Cell Death Dis. 4, e537–e537 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • 108.

    Szablowska-Gadomska, I., Zayat, V. & Buzanska, L. Influence of low oxygen tensions on expression of pluripotency genes in stem cells. Acta Neurobiol. Exp. (Warsz.) 71, 86–93 (2011).

    Google Scholar 

  • 109.

    Dreffs, A., Henderson, D., Dmitriev, A. V., Antonetti, D. A. & Linsenmeier, R. A. Retinal pH and acid regulation during metabolic acidosis. Curr. Eye Res. 43, 902–912 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • 110.

    Raeker, M. Ö et al. Targeted deletion of the zebrafish obscurin A RhoGEF domain affects heart, skeletal muscle and brain development. Dev. Biol. 337, 432 (2010).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 111.

    Serafim, A. et al. Application of an integrated biomarker response index (IBR) to assess temporal variation of environmental quality in two Portuguese aquatic systems. Ecol. Indic. 19, 215–225 (2012).

    CAS  Article  Google Scholar 

  • 112.

    Berra, E., Forcella, M., Giacchini, R., Rossaro, B. & Parenti, P. Biomarkers in Caddisfly Larvae of the Species Hydropsyche pellucidula (Curtis, 1834) (Trichoptera: Hydropsychidae) measured in natural populations and after short term exposure to fenitrothion. Bull. Environ. Contam. Toxicol. 76, 863–870 (2006).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 113.

    Wernersson, A.-S. et al. The European technical report on aquatic effect-based monitoring tools under the water framework directive. Environ. Sci. Eur. 27, 7 (2015).

    Article  CAS  Google Scholar 

  • 114.

    Ryan, J. A. & Hightower, L. E. Stress proteins as molecular biomarkers for environmental toxicology. In Stress-Inducible Cellular Responses (eds Feige, U. et al.) (Birkhäuser, Basel, 1996). https://doi.org/10.1007/978-3-0348-9088-5_28.

    Google Scholar 

  • 115.

    Sanders, B. M. Stress proteins in aquatic organisms: An environmental perspective. Crit. Rev. Toxicol. 23, 49–75 (1993).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 116.

    Barata, C. et al. Combined use of biomarkers and in situ bioassays in Daphnia magna to monitor environmental hazards of pesticides in the field. Environ. Toxicol. Chem. 26, 370–379 (2007).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 117.

    Dorts, J. et al. Ecotoxicoproteomics in gills of the sentinel fish species, Cottus gobio, exposed to perfluorooctane sulfonate (PFOS). Aquat. Toxicol. 103, 1–8 (2011).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 118.

    Daborn, P. J. et al. A single P450 allele associated with insecticide resistance in Drosophila. Science 297, 2253–2256 (2002).

    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 119.

    Amichot, M. et al. Point mutations associated with insecticide resistance in the Drosophila cytochrome P450 Cyp6a2 enable DDT metabolism. Eur. J. Biochem. 271, 1250–1257 (2004).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 120.

    Dunkov, B. C. et al. The Drosophila cytochrome P450 gene Cyp6a2: Structure, localization, heterologous expression, and induction by phenobarbital. DNA Cell Biol. 16, 1345–1356 (1997).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 121.

    Anders, S. & Huber, W. Differential expression analysis for sequence count data. Nat. Preced. https://doi.org/10.1038/npre.2010.4282.2 (2010).

    Article  Google Scholar 

  • 122.

    Soneson, C. & Delorenzi, M. A comparison of methods for differential expression analysis of RNA-seq data. BMC Bioinform. 14, 91 (2013).

    Article  Google Scholar 

  • 123.

    Kim, S. & Coulombe, P. A. Emerging role for the cytoskeleton as an organizer and regulator of translation. Nat. Rev. Mol. Cell Biol. 11, 75–81 (2010).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  • 124.

    Parker, A. L., Kavallaris, M. & McCarroll, J. A. Microtubules and their role in cellular stress in cancer. Front. Oncol. 4, 153 (2014).

    PubMed  PubMed Central  Article  Google Scholar 

  • 125.

    Skelly, D. A., Ronald, J. & Akey, J. M. Inherited variation in gene expression. Annu. Rev. Genomics Hum. Genet. 10, 313–332 (2009).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 126.

    Brem, R. B., Yvert, G., Clinton, R. & Kruglyak, L. Genetic dissection of transcriptional regulation in budding yeast. Science 296, 752–755 (2002).

    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 127.

    Arias, M. B., Poupin, M. J. & Lardies, M. A. Plasticity of life-cycle, physiological thermal traits and Hsp70 gene expression in an insect along the ontogeny: Effect of temperature variability. J. Therm. Biol. 36, 355–362 (2011).

    CAS  Article  Google Scholar 

  • 128.

    Place, S. P. & Hofmann, G. E. Constitutive expression of a stress-inducible heat shock protein gene, hsp70, in phylogenetically distant Antarctic fish. Polar Biol. 28, 261–267 (2005).

    Article  Google Scholar 

  • 129.

    Hotaling, S. et al. Mountain stoneflies may tolerate warming streams: evidence from organismal physiology and gene expression. bioRxiv 2019.12.16.878926 (2019). https://doi.org/10.1101/2019.12.16.878926.

  • 130.

    Cuellar, J. et al. Assisted protein folding at low temperature: Evolutionary adaptation of the Antarctic fish chaperonin CCT and its client proteins. Biol. Open 3, 261–270 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • 131.

    Cantonati, M., Füreder, L., Gerecke, R., Jüttner, I. & Cox, E. J. Crenic habitats, hotspots for freshwater biodiversity conservation: Toward an understanding of their ecology. Freshw. Sci. 31, 463–480 (2012).

    Article  Google Scholar 

  • 132.

    Hofmann, G. E. & Todgham, A. E. Living in the now: Physiological mechanisms to tolerate a rapidly changing environment. Annu. Rev. Physiol. 72, 127–145 (2010).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 133.

    Pörtner, H. O., Peck, L. & Somero, G. Thermal limits and adaptation in marine Antarctic ectotherms: An integrative view. Philos. Trans. R. Soc. B Biol. Sci. 362, 2233–2258 (2007).

    Article  CAS  Google Scholar 

  • 134.

    Shah, A. A. et al. Climate variability predicts thermal limits of aquatic insects across elevation and latitude. Funct. Ecol. https://doi.org/10.1111/1365-2435.12906 (2018).

    Article  Google Scholar 

  • 135.

    Treanor, H. B., Giersch, J. J., Kappenman, K. M., Muhlfeld, C. C. & Webb, M. A. H. Thermal tolerance of meltwater stonefly Lednia tumana nymphs from an alpine stream in Waterton-Glacier International Peace Park, Montana, USA. Freshw. Sci. 32, 597–605 (2013).

    Article  Google Scholar 

  • 136.

    Forsman, A. & Wennersten, L. Inter-individual variation promotes ecological success of populations and species: Evidence from experimental and comparative studies. Ecography 39, 630–648 (2016).

    Article  Google Scholar 

  • 137.

    Cogne, Y. et al. Comparative proteomics in the wild: Accounting for intrapopulation variability improves describing proteome response in a Gammarus pulex field population exposed to cadmium. Aquat. Toxicol. 214, 105244 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 138.

    Gotelli, N. J., Ellison, A. M. & Ballif, B. A. Environmental proteomics, biodiversity statistics and food-web structure. Trends Ecol. Evol. 27, 436–442 (2012).

    PubMed  PubMed Central  Article  Google Scholar 

  • 139.

    Liu, F. et al. New Perspectives on host-parasite interplay by comparative transcriptomic and proteomic analyses of Schistosoma japonicum. PLOS Pathog. 2, e29 (2006).

    PubMed  PubMed Central  Article  Google Scholar 

  • 140.

    Nold, S. C. & Zwart, G. Patterns and governing forces in aquatic microbial communities. Aquat. Ecol. 32, 17–35 (1998).

    CAS  Article  Google Scholar 

  • 141.

    Pass, D. A. et al. The effect of anthropogenic arsenic contamination on the earthworm microbiome. Environ. Microbiol. 17, 1884–1896 (2015).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 142.

    Perez-Riverol, Y. et al. The PRIDE database and related tools and resources in 2019: Improving support for quantification data. Nucleic Acids Res. 47, D442–D450 (2019).

    CAS  Article  Google Scholar 


  • Source: Ecology - nature.com

    China’s researchers have valuable experiences that the world needs to hear about

    Conventional analysis methods underestimate the plant-available pools of calcium, magnesium and potassium in forest soils