More stories

  • in

    Ultracold storage ensures a future for endangered plants

    Here at the Germplasm Bank of Wild Species of China at the Kunming Institute of Botany, we want to preserve the seeds of as many wild plants as possible from across China’s vast land area. I work on developing the best techniques to freeze plant seeds and tissues at ultracold temperatures, to maintain their viability for years. The idea is that if we plant these seeds again in hundreds of years, a plant will grow.The picture shows me taking a sample of embryos from the seeds of a magnolia tree out of a liquid-nitrogen cryopreservation tank to test whether they’ll regrow when thawed. I dress in protective equipment from head to toe to protect me from the nitrogen, which has a temperature of −196 °C.When I came to the institute in 2009 as a PhD student, it had just purchased its first liquid-nitrogen cryopreservation system, but no one knew how to operate it. I was the one to work it out.Over the years, human activities and climate change have had a negative impact on plant biodiversity. The ultimate goal of the plant seed bank is to collect and preserve all wild plant species in China that are endangered, rare or valuable. We want to save these species before they go extinct. We’ve collected seeds from nearly 11,000 plant species, but that’s only one-third of what grows in China.Many wild plants have genes that help them to survive in harsh environments and make them disease- or drought-resistant. In the future, we might need these genetic materials to breed new crops that can better adapt to the changing climate.I am constantly amazed by how diverse and beautiful seeds are. Some of them are brightly coloured and others are star-shaped. I feel proud when I see the unfrozen seeds germinate in test tubes and gradually grow into large plants. We have three plants in the seed-bank lobby that we cultivated from preserved tissues, and they are all now taller than me. More

  • in

    Jet stream position explains regional anomalies in European beech forest productivity and tree growth

    Woollings, T., Hannachi, A. & Hoskins, B. Variability of the North Atlantic eddy-driven jet stream. Q J. R. Meteorol. Soc. 136, 856–868 (2010).ADS 
    Article 

    Google Scholar 
    Coumou, D., Capua, D. I., Vavrus, G., Wang, L. S. & Wang, S. The influence of Arctic amplification on mid-latitude summer circulation. Nat. Commun. 9, 2959 (2018).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Belmecheri, S., Babst, F., Hudson, A. R., Betancourt, J. & Trouet, V. Northern Hemisphere jet stream position indices as diagnostic tools for climate and ecosystem dynamics. Earth Interact. 21, 1–23 (2017).Article 

    Google Scholar 
    Trouet, V., Babst, F. & Meko, M. Recent enhanced high-summer North Atlantic Jet variability emerges from three-century context. Nat. Commun. 9, 180 (2018).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lehmann, J. & Coumou, D. The influence of mid-latitude storm tracks on hot, cold, dry and wet extremes. Sci. Rep. 5, 17491 (2015).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Mahlstein, I., Martius, O., Chevalier, C. & Ginsbourger, D. Changes in the odds of extreme events in the Atlantic basin depending on the position of the extratropical jet. Geophys. Res. Lett. 39, 1–6 (2012).
    Google Scholar 
    Röthlisberger, M., Pfahl, S. & Martius, O. Regional-scale jet waviness modulates the occurrence of midlatitude weather extremes. Geophys. Res. Lett. 43, 10,910–989,997 (2016).Article 

    Google Scholar 
    Brunner, L., Schaller, N., Anstey, J., Sillmann, J. & Steiner, A. K. Dependence of present and future European temperature extremes on the location of atmospheric blocking. Geophys. Res. Lett. 45, 6311–6320 (2018).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Dong, B., Sutton, R. T., Woollings, T. & Hodges, K. Variability of the North Atlantic summer storm track: mechanisms and impacts on European climate. Environ. Res. Lett. 8, 34037 (2013).Article 

    Google Scholar 
    Mann, M. E. et al. Influence of anthropogenic climate change on planetary wave resonance and extreme weather events. Sci. Rep. 7, 45242 (2017).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Coumou, D. & Rahmstorf, S. A decade of weather extremes. Nat. Clim. Change 2, 491–496 (2012).ADS 
    Article 

    Google Scholar 
    Schumacher, D. L. et al. Amplification of mega-heatwaves through heat torrents fuelled by upwind drought. Nat. Geosci. 12, 712–717 (2019).ADS 
    CAS 
    Article 

    Google Scholar 
    Zscheischler, J. et al. Future climate risk from compound events. Nat. Clim. Change 8, 469–477 (2018).ADS 
    Article 

    Google Scholar 
    Lobell, D. B., Schlenker, W. & Costa-Roberts, J. Climate trends and global crop production since 1980. Science 333, 616–620 (2011).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Buras, A., Rammig, A. & Zang, C. S. Quantifying impacts of the 2018 drought on European ecosystems in comparison to 2003. Biogeosciences 17, 1655–1672 (2020).ADS 
    Article 

    Google Scholar 
    Reichstein, M. et al. Climate extremes and the carbon cycle. Nature 500, 287–295 (2013).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Frank, D. et al. Effects of climate extremes on the terrestrial carbon cycle: concepts, processes and potential future impacts. Glob. Change Biol. 21, 2861–2880 (2015).ADS 
    Article 

    Google Scholar 
    Sillmann, J. et al. Understanding, modeling and predicting weather and climate extremes: challenges and opportunities. Weather Clim. Extrem. 18, 65–74 (2017).Article 

    Google Scholar 
    Barriopedro, D., Fischer, E. M., Luterbacher, J., Trigo, R. M. & García-Herrera, R. The hot summer of 2010: redrawing the temperature record map of Europe. Science 332, 220–224 (2011).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Ciais, P. et al. Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 437, 529–533 (2005).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Bastos, A., Gouveia, C. M., Trigo, R. M. & Running, S. W. Analysing the spatio-temporal impacts of the 2003 and 2010 extreme heatwaves on plant productivity in Europe. Biogeosciences 11, 3421–3435 (2014).ADS 
    Article 

    Google Scholar 
    Fischer, E. M., Seneviratne, S. I., Vidale, P. L., Lüthi, D. & Schär, C. Soil moisture–atmosphere interactions during the 2003 european summer heat wave. J. Clim. 20, 5081–5099 (2007).ADS 
    Article 

    Google Scholar 
    Perkins-Kirkpatrick, S. E. & Lewis, S. C. Increasing trends in regional heatwaves. Nat. Commun. 11, 3357 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Friedlingstein, P. et al. Global carbon budget 2020. Earth Syst. Sci. Data 12, 3269–3340 (2020).ADS 
    Article 

    Google Scholar 
    Kalnay, E. et al. The NCEP/NCAR 40-year reanalysis project. Bull. Am. Meteorol. Soc. 77, 437–472 (1996).ADS 
    Article 

    Google Scholar 
    Rammig, A. et al. Coincidences of climate extremes and anomalous vegetation responses: comparing tree ring patterns to simulated productivity. Biogeosciences 12, 373–385 (2015).ADS 
    Article 

    Google Scholar 
    Spinoni, J., Naumann, G., Vogt, J. V. & Barbosa, P. The biggest drought events in Europe from 1950 to 2012. J. Hydrol. Reg. Stud. 3, 509–524 (2015).Article 

    Google Scholar 
    Madonna, E., Li, C., Grams, C. M. & Woollings, T. The link between eddy-driven jet variability and weather regimes in the North Atlantic-European sector. Q J. R. Meteorol. Soc. 143, 2960–2972 (2017).ADS 
    Article 

    Google Scholar 
    Grams, C. M., Beerli, R., Pfenninger, S., Staffell, I. & Wernli, H. Balancing Europe’s wind-power output through spatial deployment informed by weather regimes. Nat. Clim. Change 7, 557–562 (2017).Article 

    Google Scholar 
    Seftigen, K., Frank, D. C., Björklund, J., Babst, F. & Poulter, B. The climatic drivers of normalized difference vegetation index and tree-ring-based estimates of forest productivity are spatially coherent but temporally decoupled in Northern Hemispheric forests. Glob. Ecol. Biogeogr. 27, 1352–1365 (2018).Article 

    Google Scholar 
    Babst, F. et al. Above-ground woody carbon sequestration measured from tree rings is coherent with net ecosystem productivity at five eddy-covariance sites. N. Phytol. 201, 1289–1303 (2014).CAS 
    Article 

    Google Scholar 
    Zweifel, R. & Sterck, F. A conceptual tree model explaining legacy effects on stem growth. Front. Glob. Change 1, 9 (2018).Article 

    Google Scholar 
    Fatichi, S., Pappas, C., Zscheischler, J. & Leuzinger, S. Modelling carbon sources and sinks in terrestrial vegetation. N. Phytol. 221, 652–668 (2019).CAS 
    Article 

    Google Scholar 
    Wu, X. et al. Differentiating drought legacy effects on vegetation growth over the temperate Northern Hemisphere. Glob. Chang. Biol. 24, 504–516 (2018).ADS 
    PubMed 
    Article 

    Google Scholar 
    Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol. Evol. 4, 133–142 (2013).Article 

    Google Scholar 
    Davini, P. & Cagnazzo, C. On the misinterpretation of the North Atlantic Oscillation in CMIP5 models. Clim. Dyn. 43, 1497–1511 (2014).Article 

    Google Scholar 
    Pfahl, S. & Wernli, H. Quantifying the relevance of atmospheric blocking for co-located temperature extremes in the Northern Hemisphere on (sub-)daily time scales. Geophys. Res. Lett. 39 (2012).Drouard, M. & Woollings, T. Contrasting mechanisms of summer blocking over western Eurasia. Geophys. Res. Lett. 45, 12,040–12,048 (2018).Article 

    Google Scholar 
    Bastos, A. et al. European land CO2 sink influenced by NAO and East-Atlantic pattern coupling. Nat. Commun. 7, 10315 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Ascoli, D. et al. Inter-annual and decadal changes in teleconnections drive continental-scale synchronization of tree reproduction. Nat. Commun. 8, 1–9 (2017).CAS 
    Article 

    Google Scholar 
    Sousa, P. M. et al. Responses of European precipitation distributions and regimes to different blocking locations. Clim. Dyn. 48, 1141–1160 (2017).Article 

    Google Scholar 
    Hacket-Pain, A. J., Cavin, L., Friend, A. D. & Jump, A. S. Consistent limitation of growth by high temperature and low precipitation from range core to southern edge of European beech indicates widespread vulnerability to changing climate. Eur. J. Res. 135, 897–909 (2016).Article 

    Google Scholar 
    Cavin, L. & Jump, A. S. Highest drought sensitivity and lowest resistance to growth suppression are found in the range core of the tree Fagus sylvatica L. not the equatorial range edge. Glob. Chang. Biol. 23, 362–379 (2017).ADS 
    PubMed 
    Article 

    Google Scholar 
    Leuschner, C. Drought response of European beech (Fagus sylvatica L.): A review. Perspect. Plant Ecol. Evol. Syst. 47, 125576 (2020).Article 

    Google Scholar 
    Muffler, L. et al. Lowest drought sensitivity and decreasing growth synchrony towards the dry distribution margin of European beech. J. Biogeogr. 47, 1910–1921 (2020).Article 

    Google Scholar 
    Wang, F. et al. Seedlings from marginal and core populations of European beech (Fagus sylvatica L.) respond differently to imposed drought and shade. Trees 35, 53–67 (2021).CAS 
    Article 

    Google Scholar 
    Hall, R. J., Jones, J. M., Hanna, E., Scaife, A. A. & Erdélyi, R. Drivers and potential predictability of summertime North Atlantic polar front jet variability. Clim. Dyn. 48, 3869–3887 (2017).Article 

    Google Scholar 
    Screen, J. A. & Simmonds, I. Amplified mid-latitude planetary waves favour particular regional weather extremes. Nat. Clim. Change 4, 704–709 (2014).ADS 
    Article 

    Google Scholar 
    Kornhuber, K. et al. Extreme weather events in early summer 2018 connected by a recurrent hemispheric wave-7 pattern. Environ. Res. Lett. 14, 54002 (2019).Article 

    Google Scholar 
    Shepherd, T. G. Atmospheric circulation as a source of uncertainty in climate change projections. Nat. Geosci. 7, 703–708 (2014).ADS 
    CAS 
    Article 

    Google Scholar 
    Peings, Y., Cattiaux, J., Vavrus, S. J. & Magnusdottir, G. Projected squeezing of the wintertime North-Atlantic jet. Environ. Res. Lett. 13, 74016 (2018).Article 

    Google Scholar 
    Matsueda, M. & Endo, H. The robustness of future changes in Northern Hemisphere blocking: a large ensemble projection with multiple sea surface temperature patterns. Geophys. Res. Lett. 44, 5158–5166 (2017).ADS 
    Article 

    Google Scholar 
    Kwon, Y. O., Camacho, A., Martinez, C. & Seo, H. North Atlantic winter eddy-driven jet and atmospheric blocking variability in the Community Earth System Model version 1 Large Ensemble simulations. Clim. Dyn. 51, 3275–3289 (2018).Article 

    Google Scholar 
    Cohen, J. et al. Divergent consensuses on Arctic amplification influence on midlatitude severe winter weather. Nat. Clim. Change 10, 20–29 (2020).ADS 
    Article 

    Google Scholar 
    de Vries, H., Woollings, T., Anstey, J., Haarsma, R. J. & Hazeleger, W. Atmospheric blocking and its relation to jet changes in a future climate. Clim. Dyn. 41, 2643–2654 (2013).Article 

    Google Scholar 
    Woollings, T. et al. Blocking and its response to climate change. Curr. Clim. Chang. Rep. 4, 287–300 (2018).Article 

    Google Scholar 
    Anderegg, W. R. L. et al. Pervasive drought legacies in forest ecosystems and their implications for carbon cycle models. Science 349, 528–532 (2015).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Sousa-Silva, R. et al. Tree diversity mitigates defoliation after a drought-induced tipping point. Glob. Chang. Biol. 24, 4304–4315 (2018).ADS 
    PubMed 
    Article 

    Google Scholar 
    Magri, D. Patterns of post-glacial spread and the extent of glacial refugia of European beech (Fagus sylvatica). J. Biogeogr. 35, 450–463 (2008).Article 

    Google Scholar 
    Cailleret, M. et al. A synthesis of radial growth patterns preceding tree mortality. Glob. Chang. Biol. 23, 1675–1690 (2017).ADS 
    PubMed 
    Article 

    Google Scholar 
    Dorado-Liñán, I. et al. Geographical adaptation prevails over species-specific determinism in trees’ vulnerability to climate change at Mediterranean rear-edge forests. Glob. Chan. Biol. 25, 1296–1314 (2019).ADS 
    Article 

    Google Scholar 
    DeSoto, L. et al. Low growth resilience to drought is related to future mortality risk in trees. Nat. Commun. 11, 545 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Hacket-Pain, A. J., Friend, A. D., Lageard, J. G. A. & Thomas, P. A. The influence of masting phenomenon on growth–climate relationships in trees: explaining the influence of previous summers’ climate on ring width. Tree Physiol. 35, 319–330 (2015).PubMed 
    Article 

    Google Scholar 
    Bréda, N., Huc, R., Granier, A. & Dreyer, E. Temperate forest trees and stands under severe drought: a review of ecophysiological responses, adaptation processes and long-term consequences. Ann. Sci. 63, 625–644 (2006).Article 

    Google Scholar 
    Hacket-Pain, A. J. et al. Climatically controlled reproduction drives interannual growth variability in a temperate tree species. Ecol. Lett. 21, 1833–1844 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Popkin, G. How much can forests fight climate change? Nature 565, 280–282 (2019).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Davini, P. & D’Andrea, F. Northern Hemisphere atmospheric blocking representation in global climate models: twenty years of improvements? J. Clim. 29, 8823–8840 (2016).ADS 
    Article 

    Google Scholar 
    Barton, N. P. & Ellis, A. W. Variability in wintertime position and strength of the North Pacific jet stream as represented by re-analysis data. Int. J. Climatol. 29, 851–862 (2009).Article 

    Google Scholar 
    Doblas-Reyes, F. J., Casado, M. J. & Pastor, M. A. Sensitivity of the Northern Hemisphere blocking frequency to the detection index. J. Geophys. Res. Atmos. 107, D2 (2002).Article 

    Google Scholar 
    Cook, E. R. & Peters, K. The smoothing spline: a new approach to standardizing forest interior tree-ring width series for dendroclimatic studies. Tree-Ring Bull. 41, 45–53 (1981).
    Google Scholar 
    Sitch, S. et al. Recent trends and drivers of regional sources and sinks of carbon dioxide. Biogeosciences 12, 653–679 (2015).ADS 
    Article 

    Google Scholar 
    Team, R. Core (2020). R A Lang. Environ. Stat. Comput. R Found. Stat. Comput. Vienna, Austria. URL https://www.R-project.org (2020).Bunn, A. G. A dendrochronology program library in R (dplR). Dendrochronologia 26, 115–124 (2008).Article 

    Google Scholar 
    Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, (2015).Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: Tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).Barton, K. Mu-MIn: Multi-model inference. R Package Version 0.12.2/r18, (2009) http://R-Forge.R-project.org/projects/mumin/ More

  • in

    Feces DNA analyses track the rehabilitation of a free-ranging beluga whale

    Mann, J. Behavioral sampling methods for cetaceans: A review and critique. Mar. Mammal Sci. 15, 102–122 (1999).Article 

    Google Scholar 
    Pompanon, F. et al. Who is eating what: Diet assessment using next generation sequencing. Mol. Ecol. https://doi.org/10.1111/j.1365-294X.2011.05403.x (2012).Article 

    Google Scholar 
    Deagle, B. E. et al. Counting with DNA in metabarcoding studies: How should we convert sequence reads to dietary data?. Mol. Ecol. 28, 391–406 (2019).Article 

    Google Scholar 
    Berry, T. E. et al. DNA metabarcoding for diet analysis and biodiversity: A case study using the endangered Australian sea lion (Neophoca cinerea). Ecol. Evol. 7, 5435–5453 (2017).Article 

    Google Scholar 
    Brassea-Pérez, E., Schramm, Y., Heckel, G., Chong-Robles, J. & Lago-Lestón, A. Metabarcoding analysis of the Pacific harbor seal diet in Mexico. Mar. Biol. 166, 1–14 (2019).Article 

    Google Scholar 
    Ford, M. J. et al. Estimation of a killer whale (Orcinus orca) population’s diet using sequencing analysis of DNA from feces. PLoS ONE 11, e0144956 (2016).Article 

    Google Scholar 
    Thomas, A. C., Deagle, B. E., Eveson, J. P., Harsch, C. H. & Trites, A. W. Quantitative DNA metabarcoding: Improved estimates of species proportional biomass using correction factors derived from control material. Mol. Ecol. Resour. 16, 714–726 (2016).CAS 
    Article 

    Google Scholar 
    Deagle, B. E., Chiaradia, A., Mcinnes, J. & Jarman, S. N. Pyrosequencing faecal DNA to determine diet of little penguins: is what goes in what comes out? https://doi.org/10.1007/s10592-010-0096-6.Ando, H. et al. Methodological trends and perspectives of animal dietary studies by noninvasive fecal DNA metabarcoding. Environ. DNA 2, 391–406 (2020).Article 

    Google Scholar 
    Günther, B., Fromentin, J., Metral, L. & Arnaud-haond, S. Metabarcoding confirms the opportunistic foraging behaviour of Atlantic bluefin tuna and reveals the importance of gelatinous prey. PeerJ 9, e11757. https://doi.org/10.7717/peerj.11757 (2021).Article 

    Google Scholar 
    Simon, M., Hanson, M. B., Murrey, L., Tougaard, J. & Ugarte, F. From captivity to the wild and back: An attempt to release keiko the killer whale. Mar. Mammal Sci. 25, 693–705 (2009).Article 

    Google Scholar 
    Moore, M. et al. Rehabilitation and release of marine mammals in the United States: Risks and benefits. Mar. Mammal Sci. 23, 731–750 (2007).Article 

    Google Scholar 
    Leray, M. et al. A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: Application for characterizing coral reef fish gut contents. Front. Zool. 10, 1–14 (2013).Article 

    Google Scholar 
    Geller, J., Meyer, C. & Parker, M. Redesign of PCR primers for mitochondrial cytochrome c oxidase subunit I for marine invertebrates and application in all-taxa biotic surveys. Mol. Ecol. Resour. 13(5), 851–861. https://doi.org/10.1111/1755-0998.12138 (2013).CAS 
    Article 

    Google Scholar 
    Blaxter, M. L. et al. A molecular evolutionary framework for the phylum Nematoda. Nature https://doi.org/10.1038/32160 (1998).Article 

    Google Scholar 
    Sinniger, F. et al. Worldwide analysis of sedimentary DNA reveals major gaps in taxonomic knowledge of deep-sea benthos. Front. Mar. Sci. 3, 1–14 (2016).Article 

    Google Scholar 
    Brandt, M. I. et al. Bioinformatic pipelines combining denoising and clustering tools allow for more comprehensive prokaryotic and eukaryotic metabarcoding. Mol. Ecol. Resour. 21(6), 1904–1921 (2021).Article 

    Google Scholar 
    Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal https://doi.org/10.14806/ej.17.1.200 (2011).Article 

    Google Scholar 
    Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
    Article 

    Google Scholar 
    Antich, A., Palacin, C., Wangensteen, O. S. & Turon, X. To denoise or to cluster, that is not the question: Optimizing pipelines for COI metabarcoding and metaphylogeography. BMC Bioinform. 22, 1–25 (2021).Article 

    Google Scholar 
    Mahé, F., Rognes, T., Quince, C., de Vargas, C. & Dunthorn, M. Swarmv2: Highly-scalable and high-resolution amplicon clustering. PeerJ 2015, 1–12 (2015).
    Google Scholar 
    Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. https://doi.org/10.1093/nar/gks1219 (2013).Article 

    Google Scholar 
    Machida, R. J., Leray, M., Ho, S.-L. & Knowlton, N. Metazoan mitochondrial gene sequence reference datasets for taxonomic assignment of environmental samples. Sci. Data 4, 170027 (2017).CAS 
    Article 

    Google Scholar 
    Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naıve Bayesian classifier for rapid assignment of rRNA sequences.pdf. Appl. Environ. Microbiol. 73, 5261–5267 (2007).ADS 
    CAS 
    Article 

    Google Scholar 
    Davis, N. M., Di Proctor, M., Holmes, S. P., Relman, D. A. & Callahan, B. J. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome https://doi.org/10.1186/s40168-018-0605-2 (2018).Article 

    Google Scholar 
    Wangensteen, O. S., Palacín, C., Guardiola, M. & Turon, X. DNA metabarcoding of littoral hardbottom communities: High diversity and database gaps revealed by two molecular markers. PeerJ 2018, 1–30 (2018).
    Google Scholar 
    Schnell, I. B., Bohmann, K. & Gilbert, M. T. P. Tag jumps illuminated – reducing sequence-to-sample misidentifications in metabarcoding studies. Mol. Ecol. Resour. 15, 1289–1303 (2015).CAS 
    Article 

    Google Scholar 
    Song, X. et al. A new deep-sea hydroid (Cnidaria:Hydrozoa ) from the Bering Sea Basin reveals high genetic relevance to Arctic and adjacent shallow-water species. Polar Biol. 39, 461–471 (2016).Article 

    Google Scholar 
    Frøslev, T. G. et al. Algorithm for post-clustering curation of DNA amplicon data yields reliable biodiversity estimates. Nat. Commun. 8, 1–11 (2017).Article 

    Google Scholar 
    Vacquié-Garcia, J., Lydersen, C., Ims, R. A. & Kovacs, K. M. Habitats and movement patterns of white whales Delphinapterus leucas in Svalbard, Norway in a changing climate. Mov. Ecol. 6, 1–12 (2018).Article 

    Google Scholar 
    Kastelein, R. A., Nieuwstraten, S. H. & Verstegen, M. W. A. Passage time of carmine red dye through the digestion tract . In The Biology of the Harbour Porpoise 235–245 (1997).Lesage, V., Lair, S., Turgeon, S. & Beland, P. Diet of St. Lawrence Estuary Beluga (Delphinapterus leucas) in a changing ecosystem. Can. Field-Nat. 134, 21–35 (2020).Article 

    Google Scholar 
    Bluhm, B. A. & Gradinger, R. Regional variability in food availability for arctic marine mammals. Ecol. Appl. 18, S77–S96 (2008).Article 

    Google Scholar 
    Quakenbush, L. T. et al. Diet of beluga whales, Delphinapterus leucas, in Alaska from stomach contents, March-November. Mar. Fish. Rev. 77, 70–84 (2015).Article 

    Google Scholar 
    Choy, E. S. et al. Variation in the diet of beluga whales in response to changes in prey availability: Insights on changes in the Beaufort Sea ecosystem. Mar. Ecol. Prog. Ser. 647, 195–210 (2020).ADS 
    CAS 
    Article 

    Google Scholar 
    Mychek-Londer, J. G., Chaganti, S. R. & Heath, D. D. Metabarcoding of native and invasive species in stomach contents of Great Lakes fishes. PLoS ONE 15, 1–22 (2020).Article 

    Google Scholar 
    Nedreaas, K. Food and feeding habits of young saithe, Pollachius virens (L.), on the coast of Western Norway. Fisk. Skr. Ser. Havundersokelser 18, 263–301 (1987).
    Google Scholar 
    Højgaard, D. P. Food and parasitic nematodes of saithe, Pollachius virens (L.), from the Faroe Islands. Sarsia 84, 473–478 (1999).Article 

    Google Scholar 
    Ekbaum, E. Notes on the occurrence of Acanthocephala in Pacific fishes: I. Echinorhynchus gadi (Zoega) Müller in salmon and E. lageniformis sp. nov. and Corynosoma strumosum (Rudolphi) in two species of flounder. Parasitology 30, 267–274 (1938).Article 

    Google Scholar 
    Baptista-Fernandes, T. et al. Human gastric hyperinfection by Anisakis simplex: A severe and unusual presentation and a brief review. Int. J. Infect. Dis. 64, 38–41 (2017).Article 

    Google Scholar 
    Hubert, B., Bacou, J. & Belveze, H. Epidemiology of human anisakiasis: Incidence and sources in France. Am. J. Trop. Med. Hyg. 40, 301–303 (1989).CAS 
    Article 

    Google Scholar 
    Hays, R., Measures, L. N. & Huot, J. Capelin (Mallotus villosus) and herring (Clupea harengus) as paratenic hosts of Anisakis simplex, a parasite of beluga (Delphinapterus leucas) in the St. Lawrence estuary. Can. J. Zool. 78, 1411–1417 (1998).Article 

    Google Scholar 
    Yanong, R. P. E. Nematode (Roundworm) Infections in Fish Vol. 1, 1–9 (2002).Jauniaux, T. et al. Post-mortem findings and causes of death of harbour porpoises (Phocoena phocoena) stranded from 1990 to 2000 along the coastlines of Belgium and Northern France. J. Compar. Pathol. 126, 243–253 (2002).CAS 
    Article 

    Google Scholar  More

  • in

    Monitoring of radioactive cesium in wild boars captured inside the difficult-to-return zone in Fukushima Prefecture over a 5-year period

    Ministry of the Environment Government of Japan. Designation of Evacuation Zone (accessed 07 April 2021); https://www.env.go.jp/chemi/rhm/h29kisoshiryo/h29kiso-09-04-01.html. (in Japanese).Fukushima Prefectural Government, Japan. About the Transition of Evacuation Zone (accessed 07 April 2021); https://www.pref.fukushima.lg.jp/site/portal/cat01-more.html. (in Japanese).Chino, M. et al. Preliminary estimation of release amounts of 131I and 137Cs accidentally discharged from the Fukushima Daiichi Nuclear Power Plant into the atmosphere. J. Nucl. Sci. Technol. 48, 1129–1134 (2011).CAS 
    Article 

    Google Scholar 
    Koarashi, J., Atarashi-Andoh, M., Takeuchi, E. & Nishimura, S. Topographic heterogeneity effect on the accumulation of Fukushima-derived radiocaesium on forest floor driven by biologically mediated processes. Sci. Rep. 4, 6853 (2014).ADS 
    CAS 
    Article 

    Google Scholar 
    Saito, R., Nemoto, Y. & Tsukada, H. Relationship between radiocaesium in muscle and physicochemical fractions of radiocaesium in the stomach of wild boar. Sci. Rep. 10, 6796. https://doi.org/10.1038/s41598-020-63507-5 (2020).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Tsukada, H. From soil to agricultural-plants-transfer and distribution of radiocaesium. Kagaku (Chemistry). 67, 20–23 (2012) (in Japanese).CAS 

    Google Scholar 
    Saito, R. & Tsukada, H. Chapter 23: Physicochemical fractions of radiocaesium in the stomach contents of wild boar and its transfer to muscle tissue. In Behavior of Radionuclides in the Environment III (eds Nanba, K. et al.) 495–505 (Springer, 2022).Chapter 

    Google Scholar 
    Ishii, Y., Hayashi, S. & Takamura, T. Radiocaesium transfer in forest insect communities after the Fukushima Dai-ichi Nuclear Power Plant accident. PLoS ONE 12, e0171133 (2017).Article 

    Google Scholar 
    Matsushima, N., Ihara, S., Takase, M. & Horiguchi, T. Assessment of radiocaesium contamination in frogs 18 months after the Fukushima Daiichi nuclear disaster. Sci. Rep. 5, 9712 (2015).ADS 
    CAS 
    Article 

    Google Scholar 
    Ishii, Y., Matsuzaki, S. S. & Hayashi, S. Different factors determine 137Cs concentration factors of freshwater fish and aquatic organisms in lake and river ecosystems. J. Environ. Radioact. 213, 106102 (2020).CAS 
    Article 

    Google Scholar 
    Wada, T. et al. Strong contrast of cesium radioactivity between marine and freshwater fish in Fukushima. J. Environ. Radioact. 204, 132–142 (2019).CAS 
    Article 

    Google Scholar 
    Morishita, D. et al. Spatial and seasonal variations of radiocaesium concentrations in an algae-grazing annual fish, ayu Plecoglossus altivelis collected from Fukushima Prefecture in 2014. Fish. Sci. 85, 561–569 (2019).CAS 
    Article 

    Google Scholar 
    Saito, R., Kabeya, M., Nemoto, Y. & Oomachi, H. Monitoring 137Cs concentrations in bird species occupying different ecological niches; game birds and raptors in Fukushima Prefecture. J. Environ. Radioact. 197, 67–73 (2019).CAS 
    Article 

    Google Scholar 
    Merz, S., Shozugawa, K. & Steinhauser, G. Analysis of Japanese radionuclide monitoring data of food before and after the Fukushima nuclear accident. Environ. Sci. Technol. 49, 2875–2885 (2015).ADS 
    CAS 
    Article 

    Google Scholar 
    Steinhauser, G. & Saey, P. R. J. 137Cs in the meat of wild boars: A comparison of the impacts of Chernobyl and Fukushima. J. Radioanal. Nucl. Chem. 307, 1801–1806 (2016).CAS 
    Article 

    Google Scholar 
    Nemoto, Y., Saito, R. & Oomachi, H. Seasonal variation of caesium-137 concentration in Asian black bear (Ursus thibetanus) and wild boar (Sus scrofa) in Fukushima Prefecture, Japan. PLoS ONE 13, e0200797. https://doi.org/10.1371/journal.pone.0200797 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Nemoto, Y. et al. Effects of 137Cs contamination after the TEPCO Fukushima Dai-ichi Nuclear Power Station accident on food and habitat of wild boar in Fukushima Prefecture. J. Environ. Radioact. 225, 106342 (2020).CAS 
    Article 

    Google Scholar 
    Saito, R., Oomachi, H., Nemoto, Y. & Osako, M. Estimation of the total amount of the radiocaesium in the wild boar in their body – each organs survey and incineration residue survey. J. Soc. Rem. Radioact. Contam. Environ. 7, 165–173 (2019) (in Japanese).
    Google Scholar 
    Cui, L. et al. Radiocaesium concentrations in wild boars captured within 20 km of the Fukushima Daiichi Nuclear Power Plant. Sci. Rep. 10, 9272. https://doi.org/10.1038/s41598-020-66362-6 (2020).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Tagami, K., Howard, B. J. & Uchida, S. The time-dependent transfer factor of radiocaesium from soil to game animals in Japan after the Fukushima Dai-ichi nuclear accident. Environ. Sci. Technol. 50, 9424–9431. https://doi.org/10.1021/acs.est.6b03011 (2016).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Fuma, S. et al. Radiocaesium contamination of wild boars in Fukushima and surrounding regions after the Fukushima nuclear accident. Environ. Radioact. 164, 60–64 (2016).CAS 
    Article 

    Google Scholar 
    Fukushima Prefectural Government, Japan. Monitoring of Wild Animals. Accessed 7 Apr 2021. https://www.pref.fukushima.lg.jp/site/portal/wildlife-radiationmonitoring1.html. (in Japanese).Strebl, F. & Tataruch, F. Time trends (1986–2003) of radiocaesium transfer to roe deer and wild boar in two Austrian forest regions. J. Environ. Radioactiv. 98, 137–152 (2007).CAS 
    Article 

    Google Scholar 
    Ohtsuka-Ito, E. & Kanzaki, N. Population trends of the Japanese wild boar during the Showa era. Wildl. Cons. Jpn. 3, 95–105 (1998).Article 

    Google Scholar 
    Ueda, H. & Jiang, Z. The use of Orchards and Abandoned Orchard by wild boars in Yamanashi. Mamm. Sci. 44, 23–33 (2004) (in Japanese).
    Google Scholar 
    Fukushima Prefectural Government, Japan. Fukushima Prefecture Wild Boar Management Plan (Phase 3) (accessed 07 April 2021); https://www.pref.fukushima.lg.jp/uploaded/life/497785_1296285_misc.pdf (in Japanese).Anderson, D. et al. A comparison of methods to derive aggregated transfer factors using wild boar data from the Fukushima Prefecture. J. Environ. Radioact. 197, 101–108 (2019).CAS 
    Article 

    Google Scholar 
    Pröhl, G. et al. Ecological half-lives of 90Sr and 137Cs in terrestrial and aquatic ecosystems. J. Environ. Radioactiv. 91, 41–72 (2006).Article 

    Google Scholar 
    Palo, R. T., White, N. & Danell, K. Spatial and temporal variations of 137Cs in moose Alces alces and transfer to man in northern Sweden. Wildlife Biol. 9, 207–212 (2003).Article 

    Google Scholar 
    Kodera, Y., Kanzaki, N., Ishikawa, N. & Minagawa, A. Food habits of wild boar (Sus scrofa) inhabiting Iwami District, Shimane Prefecture, western Japan. J. Mammal. Soc. Jpn. 53, 279–287 (2013) (in Japanese).
    Google Scholar 
    Kodera, Y. & Kanzaki, N. Food habits and nutritional condition of Japanese wild boar in Iwami district, Shimane Prefecture, western Japan. Wildl. Cons. Jpn. 6, 109–117 (2001) (in Japanese).
    Google Scholar 
    Arita, S. et al. Radioactive cesium accumulation during developmental stages of Largemouth Bass, Micropterus salmoides. Proc. JSCE. G. (Environment) 71, 267–276 (2015).Article 

    Google Scholar 
    Kodera, Y. C. S. F. prevention of epidemics from a point of view of the ecology of wild boar. J. Vet. Epidemiol. 23, 4–8 (2019) (in Japanese).Article 

    Google Scholar 
    Calenge, C., Maillard, D., Vassant, J. & Brandt, S. Summer and hunting season home ranges of wild boar (Sus scrofa) in two habitats in France. Game Wildl. Sci. 19, 281–301 (2002).
    Google Scholar 
    Massei, G., Genov, P. V., Staines, B. W. & Gorman, M. L. Factors influencing home range and activity of wild boar (Sus scrofa) in a Mediterranean coastal area. J. Zool. 242, 411–423 (1997).Article 

    Google Scholar 
    Morelle, K. et al. Towards understanding wild boar Sus scrofa movement: A synthetic movement ecology approach. Mammal Rev. 45, 15–29 (2015).Article 

    Google Scholar 
    Kapata, J., Mnich, K., Mnich, S., Karpińska, M. & Bielawska, A. Time-dependence of 137Cs activity concentration in wild game meat in Knyszyn Primeval Forest (Poland). J. Environ. Radioactiv. 141, 76–81 (2015).Article 

    Google Scholar 
    Gulakov, A. V. Accumulation and distribution of 137Cs and 90Sr in the body of the wild boar (Sus scrofa) found on the territory with radioactive. J. Environ. Radioactiv. 127, 171–175 (2014).CAS 
    Article 

    Google Scholar 
    Hohmann, U. & Huckschlag, D. Investigations on the radiocaesium contamination of wild boar (Sus scrofa) meat in Rhineland-Palatinate: A stomach content analysis. Eur. J. Wildl. Res. 51, 263–270 (2005).Article 

    Google Scholar 
    Škrkal, J., Rulík, P., Fantínová, K., Mihalík, J. & Timková, J. Radiocaesium levels in game in the Czech Republic. J. Environ. Radioactiv. 139, 18–23 (2015).Article 

    Google Scholar 
    Japan Atomic Energy Agency (JAEA). 5th airborne monitoring survey (accessed 07 April 2021); https://emdb.jaea.go.jp/emdb/en/portals/b1020201/Steinhauser, G. Monitoring and radioecological characteristics of radiocaesium in Japanese beef after the Fukushima nuclear accident. J. Radioanal. Nucl. Chem. 311, 1367–1373 (2017).CAS 
    Article 

    Google Scholar 
    Merz, S., Shozugawa, K. & Steinhauser, G. Effective and ecological half-lives of 90Sr and 137Cs observed in wheat and rice in Japan. J. Radioanal. Nucl. Chem. 307, 1807–1810 (2016).CAS 
    Article 

    Google Scholar  More

  • in

    Catestatin selects for colonization of antimicrobial-resistant gut bacterial communities

    Kåhrström CT, Pariente N, Weiss U. Intestinal microbiota in health and disease. Nature 2016;535:47–47.Article 

    Google Scholar 
    El Aidy S, van Baarlen P, Derrien M, Lindenbergh-Kortleve DJ, Hooiveld G, Levenez F, et al. Temporal and spatial interplay of microbiota and intestinal mucosa drive establishment of immune homeostasis in conventionalized mice. Mucosal Immunol. 2012;5:567–79.CAS 
    Article 

    Google Scholar 
    Okumura R, Takeda K. Roles of intestinal epithelial cells in the maintenance of gut homeostasis. Exp Mol Med. 2017;49:e338–e338.CAS 
    Article 

    Google Scholar 
    Mahata SK, O’Connor DT, Mahata M, Yoo SH, Taupenot L, Wu H, et al. Novel autocrine feedback control of catecholamine release. A discrete chromogranin a fragment is a noncompetitive nicotinic cholinergic antagonist. J Clin Invest. 1997;100:1623–33.CAS 
    Article 

    Google Scholar 
    Briolat J, Wu SD, Mahata SK, Gonthier B, Bagnard D, Chasserot-Golaz S, et al. New antimicrobial activity for the catecholamine release-inhibitory peptide from chromogranin A. Cell Mol Life Sci. 2005;62:377–85.CAS 
    Article 

    Google Scholar 
    Lugardon K, Raffner R, Goumon Y, Corti A, Delmas A, Bulet P, et al. Antibacterial and antifungal activities of vasostatin-1, the N-terminal fragment of chromogranin A. J Biol Chem. 2000;275:10745–53.CAS 
    Article 

    Google Scholar 
    Aslam R, Atindehou M, Lavaux T, Haïkel Y, Schneider F, Metz-Boutigue M-H. Chromogranin A-derived peptides are involved in innate immunity. Curr Med Chem. 2012;19:4115–23.CAS 
    Article 

    Google Scholar 
    El-Salhy M, Patcharatrakul T, Hatlebakk JG, Hausken T, Gilja OH, Gonlachanvit S. Chromogranin A cell density in the large intestine of Asian and European patients with irritable bowel syndrome. Scand J Gastroenterol. 2017;52:691–7.CAS 
    Article 

    Google Scholar 
    Bartolomucci A, Possenti R, Mahata SK, Fischer-Colbrie R, Loh YP, Salton SR. The extended granin family: structure, function, and biomedical implications. Endocr Rev. 2011;32:755–97.CAS 
    Article 

    Google Scholar 
    Mahata SK, Corti A. Chromogranin A and its fragments in cardiovascular, immunometabolic, and cancer regulation. Ann N Y Acad Sci. 2019;1455:34–58.CAS 
    Article 

    Google Scholar 
    Corti A, Marcucci F, Bachetti T. Circulating chromogranin A and its fragments as diagnostic and prognostic disease markers. Pflugers Archiv Eur J Physiol. 2018;470:199–210.Mahata SK, Mahata M, Fung MM, O’Connor DT. Catestatin: a multifunctional peptide from chromogranin A. Regul Pept. 2010;162:33–43.CAS 
    Article 

    Google Scholar 
    Ying W, Mahata S, Bandyopadhyay GK, Zhou Z, Wollam J, Vu J, et al. Catestatin inhibits obesity-induced macrophage infiltration and inflammation in the liver and suppresses hepatic glucose production, leading to improved insulin sensitivity. Diabetes. 2018;67:841–8.CAS 
    Article 

    Google Scholar 
    Mahata SK, Kiranmayi M, Mahapatra NR. Catestatin: a master regulator of cardiovascular functions. Curr Med Chem. 2018;25:1352–74.CAS 
    Article 

    Google Scholar 
    Muntjewerff EM, Tang K, Lutter L, Christoffersson G, Nicolasen MJT, Gao H, et al. Chromogranin A regulates gut permeability via the antagonistic actions of its proteolytic peptides. Acta Physiol. 2021;232:e13655.Rabbi MF, Munyaka PM, Eissa N, Metz-Boutigue MH, Khafipour E, Ghia JE. Human catestatin alters gut microbiota composition in mice. Front Microbiol. 2017;7:1–12.Article 

    Google Scholar 
    Radek KA, Lopez-Garcia B, Hupe M, Niesman IR, Elias PM, Taupenot L, et al. The neuroendocrine peptide catestatin is a cutaneous antimicrobial and induced in the skin after injury. J Invest Dermatol. 2008;128:1525–34.CAS 
    Article 

    Google Scholar 
    Bevins CL, Salzman NH. Paneth cells, antimicrobial peptides and maintenance of intestinal homeostasis. Nat Rev Microbiol. 2011;9:356–68.CAS 
    Article 

    Google Scholar 
    Dupont A, Heinbockel L, Brandenburg K, Hornef MW. Antimicrobial peptides and the enteric mucus layer act in concert to protect the intestinal mucosa. Gut Microbes. 2014;5:761–5.Article 

    Google Scholar 
    Tsukuda N, Yahagi K, Hara T, Watanabe Y, Matsumoto H, Mori H, et al. Key bacterial taxa and metabolic pathways affecting gut short-chain fatty acid profiles in early life. ISME J. 2021;15:2574–90.CAS 
    Article 

    Google Scholar 
    Nuri R, Shprung T, Shai Y. Defensive remodeling: How bacterial surface properties and biofilm formation promote resistance to antimicrobial peptides. Biochim Biophys Acta Biomembr. 2015;1848:3089–100.CAS 
    Article 

    Google Scholar 
    Jakobsson HE, Rodríguez‐Piñeiro AM, Schütte A, Ermund A, Boysen P, Bemark M, et al. The composition of the gut microbiota shapes the colon mucus barrier. EMBO Rep. 2015;16:164–77.CAS 
    Article 

    Google Scholar 
    Samantha A, Vrielink A. Lipid A Phosphoethanolamine Transferase: regulation, structure and immune response. J Mol Biol. 2020;432:5184–96.CAS 
    Article 

    Google Scholar 
    Gottesman S. Proteases and their targets in Escherichia coli. Annu Rev Genet. 1996;30:465–506.CAS 
    Article 

    Google Scholar 
    Mirsepasi-Lauridsen HC, Vallance BA, Krogfelt KA, Petersen AM. Escherichia coli pathobionts associated with inflammatory bowel disease. Clin Microbiol Rev. 2019;32:1–16.Article 

    Google Scholar 
    Nayfach S, Fischbach MA, Pollard KS. MetaQuery: a web server for rapid annotation and quantitative analysis of specific genes in the human gut microbiome. Bioinformatics. 2015;31:3368–70.CAS 
    Article 

    Google Scholar 
    Ying W, Tang K, Avolio E, Schilling JM, Pasqua T, Liu MA, et al. Immunosuppression of macrophages underlies the cardioprotective effects of CST (Catestatin). Hypertension. 2021;77:1670–82.CAS 
    Article 

    Google Scholar 
    Stojanov S, Berlec A, Štrukelj B. The influence of probiotics on the firmicutes/bacteroidetes ratio in the treatment of obesity and inflammatory bowel disease. Microorganisms. 2020;8:1–16.Article 

    Google Scholar 
    Indiani CMDSP, Rizzardi KF, Castelo PM, Ferraz LFC, Darrieux M, Parisotto TM. Childhood obesity and firmicutes/bacteroidetes ratio in the gut microbiota: a systematic review. Child Obes. 2018;14:501–9.Article 

    Google Scholar 
    Lam YY, Ha CWY, Campbell CR, Mitchell AJ, Dinudom A, Oscarsson J, et al. Increased gut permeability and microbiota change associate with mesenteric fat inflammation and metabolic dysfunction in diet-induced obese mice. PLoS One. 2012;7:1–10.
    Google Scholar 
    Herp S, Durai Raj AC, Salvado Silva M, Woelfel S, Stecher B. The human symbiont Mucispirillum schaedleri: causality in health and disease. Med Microbiol Immunol. 2021;210:173–9.Article 

    Google Scholar 
    Parker BJ, Wearsch PA, Veloo ACM, Rodriguez-Palacios A. The genus alistipes: gut bacteria with emerging implications to inflammation, cancer, and mental health. Front Immunol. 2020;11:1–15.Article 

    Google Scholar 
    Hiippala K, Barreto G, Burrello C, Diaz-Basabe A, Suutarinen M, Kainulainen V, et al. Novel Odoribacter splanchnicus strain and its outer membrane vesicles exert immunoregulatory effects in vitro. Front Microbiol. 2020;11:1–14.Article 

    Google Scholar 
    McPhee JB, Small CL, Reid-Yu SA, Brannon JR, Moual H LE, Coombes BK. Host defense peptide resistance contributes to colonization and maximal intestinal pathology by Crohn’s disease-associated adherent-invasive Escherichia coli. Infect Immun. 2014;82:3383–93.Article 

    Google Scholar 
    Xu Y, Wei W, Lei S, Lin J, Srinivas S, Feng Y. An evolutionarily conserved mechanism for intrinsic and transferable polymyxin resistance. MBio. 2018;9:1–18.Article 

    Google Scholar 
    Thomassin JL, Brannon JR, Gibbs BF, Gruenheid S, Le Moual H. OmpT outer membrane proteases of enterohemorrhagic and enteropathogenic Escherichia coli contribute differently to the degradation of human LL-37. Infect Immun. 2012;80:483–92.CAS 
    Article 

    Google Scholar 
    Desloges I, Taylor JA, Leclerc JM, Brannon JR, Portt A, Spencer JD, et al. Identification and characterization of OmpT-like proteases in uropathogenic Escherichia coli clinical isolates. Microbiologyopen. 2019;8:1–36.Article 

    Google Scholar 
    McCarter JD, Stephens D, Shoemaker K, Rosenberg S, Kirsch JF, Georgiou G. Substrate specificity of the Escherichia coli outer membrane protease OmpT. J Bacteriol. 2004;186:5919–25.CAS 
    Article 

    Google Scholar 
    Kulkarni HM, Nagaraj R, Jagannadham MV. Protective role of E. coli outer membrane vesicles against antibiotics. Microbiol Res. 2015;181:1–7.CAS 
    Article 

    Google Scholar 
    Muntjewerff EM, Dunkel G, Nicolasen MJT, Mahata SK, van den Bogaart G. Catestatin as a Target for Treatment of Inflammatory Diseases. Front Immunol. 2018;9:2199.Santella RM. Approaches to DNA/RNA extraction and whole genome amplification: table 1. Cancer Epidemiol Biomark Prev. 2006;15:1585–7.CAS 
    Article 

    Google Scholar 
    Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 
    Article 

    Google Scholar 
    R Core Team. R: a language and environment for statistical computing. Vienna, Austria; 2019. https://www.r-project.org/.Lahti L, Shetty S. microbiome R package. http://microbiome.github.io.McMurdie PJ, Holmes S. Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One. 2013;8:e61217.Paulson JN, Colin Stine O, Bravo HC, Pop M. Differential abundance analysis for microbial marker-gene surveys. Nat Methods. 2013;10:1200–2.CAS 
    Article 

    Google Scholar 
    Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12:R60.Article 

    Google Scholar 
    Douglas GM, Maffei VJ, Zaneveld JR, Yurgel SN, Brown JR, Taylor CM, et al. PICRUSt2 for prediction of metagenome functions. Nat Biotechnol. 2020;38:685–8.CAS 
    Article 

    Google Scholar 
    Parks DH, Tyson GW, Hugenholtz P, Beiko RG. STAMP: Statistical analysis of taxonomic and functional profiles. Bioinformatics. 2014;30:3123–4.CAS 
    Article 

    Google Scholar 
    Beresford-Jones BS, Forster SC, Stares MD, Notley G, Viciani E, Browne HP, et al. The Mouse Gastrointestinal Bacteria Catalogue enables translation between the mouse and human gut microbiotas via functional mapping. Cell Host Microbe. 2022;30:124–138.e8.CAS 
    Article 

    Google Scholar 
    Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol Biol Evol. 2013;30:772–80.CAS 
    Article 

    Google Scholar 
    Price MN, Dehal PS, Arkin AP. FastTree 2 – approximately maximum-likelihood trees for large alignments. PLoS One. 2010;5:e9490.Article 

    Google Scholar 
    Menardo F, Loiseau C, Brites D, Coscolla M, Gygli SM, Rutaihwa LK, et al. Treemmer: a tool to reduce large phylogenetic datasets with minimal loss of diversity. BMC Bioinforma. 2018;19:1–8.Article 

    Google Scholar 
    Haider SR, Reid HJ, Sharp BL. Tricine-SDS-PAGE. In: Kurien B., Scofield R. editors. Protein electrophoresis. Methods in Molecular Biology (Methods and Protocols). Totowa, NJ: Humana Press; 2012. p. 81–91.Schägger H. Tricine-SDS-PAGE. Nat Protoc. 2006;1:16–22.Article 

    Google Scholar 
    Zoetendal EG, Booijink CCGM, Klaassens ES, Heilig HGHJ, Kleerebezem M, Smidt H, et al. Isolation of RNA from bacterial samples of the human gastrointestinal tract. Nat Protoc. 2006;1:954–9.CAS 
    Article 

    Google Scholar 
    Zhou K, Zhou L, Lim Q, Zou R, Stephanopoulos G, Too HP. Novel reference genes for quantifying transcriptional responses of Escherichia coli to protein overexpression by quantitative PCR. BMC Mol Biol. 2011;12:18.CAS 
    Article 

    Google Scholar 
    Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2-ΔΔCT method. Methods. 2001;25:402–8.CAS 
    Article 

    Google Scholar  More

  • in

    Publisher Correction: Field experiments underestimate aboveground biomass response to drought

    These authors contributed equally: György Kröel-Dulay, Andrea Mojzes.Institute of Ecology and Botany, Centre for Ecological Research, Vácrátót, HungaryGyörgy Kröel-Dulay & Andrea Mojzes‘Lendület’ Landscape and Conservation Ecology, Institute of Ecology and Botany, Centre for Ecological Research, Vácrátót, HungaryKatalin Szitár & Péter BatáryDepartment of Ecology, University of Innsbruck, Innsbruck, AustriaMichael BahnDepartment of Geosciences and Natural Resource Management, University of Copenhagen, Frederiksberg, DenmarkClaus Beier, Inger Kappel Schmidt & Klaus Steenberg LarsenNamibia University of Science and Technology, Windhoek, NamibiaMark BiltonPlants and Ecosystems (PLECO), Department of Biology, University of Antwerp, Wilrijk, BelgiumHans J. De Boeck & Sara ViccaDepartment of Forestry and Natural Resources, Purdue University, West Lafayette, IN, USAJeffrey S. DukesDepartment of Biological Sciences, Purdue University, West Lafayette, IN, USAJeffrey S. DukesCSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, SpainMarc Estiarte & Josep PeñuelasCREAF, Cerdanyola del Vallès, SpainMarc Estiarte & Josep PeñuelasGlobal Change Research Institute of the Czech Academy of Sciences, Brno, Czech RepublicPetr HolubDisturbance Ecology, Bayreuth Center of Ecology and Environmental Research, University of Bayreuth, Bayreuth, GermanyAnke JentschExperimental Plant Ecology, University of Greifswald, Greifswald, GermanyJuergen KreylingUK Centre for Ecology & Hydrology, Bangor, UKSabine ReinschSchool of Plant Sciences and Food Security, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, IsraelMarcelo SternbergPlant Ecology Group, University of Tübingen, Tübingen, GermanyKatja TielbörgerInstitute for Biodiversity and Ecosystem Dynamics (IBED), Ecosystem and Landscape Dynamics (ELD), University of Amsterdam, Amsterdam, the NetherlandsAlbert Tietema More

  • in

    Lipid composition of the Amazonian ‘Mountain Sacha Inchis’ including Plukenetia carolis-vegae Bussmann, Paniagua & C.Téllez

    Fatty acid profilePlukenetia volubilisThe fatty acid composition of P. volubilis is the most well studied in the genus, and the results from the two P. volubilis accessions from Ecuador and Peru in the current study are similar to previous results. The most abundant fatty acid in the seed oil of P. volubilis from Ecuador and Peru, respectively, is α-linolenic acid (C18:3 n-3, ω-3, ALA; 51.5 ± 3.3 and 46.6 ± 1.2%), followed by linoleic acid (C18:2 n-6, ω-6, LA; 32.5 ± 3.9 and 36.5 ± 0.8%), oleic acid (C18:1, OA; 8.5 ± 1,2 and 8.3 ± 0,4%) and smaller amounts ( More

  • in

    Saccharibacteria harness light energy using type-1 rhodopsins that may rely on retinal sourced from microbial hosts

    Phylogenetic placement of Saccharibacteria rhodopsins (SacRs) shows that these sequences form a sibling clade to characterized light-driven inward and outward H+ pumps (Fig. 1a). We selected three phylogenetically diverse SacRs from freshwater lakes (Table S1) and two related, previously uncharacterized sequences from the Gammaproteobacteria (Kushneria aurantia and Halomonas sp.) for synthesis and functional characterization (highlighted in Fig. 1a). All sequences have Asp–Thr–Ser (DTS) residues at the positions of D85–T96–D96 of bacteriorhodopsin (BR) in the third transmembrane helix (Fig. S1). These residues are known as the triplet DTD motif and represent key residues for proton pumping function in BR [6].Fig. 1: Characteristics of Saccharibacteria rhodopsins (SacRs).a Rhodopsin protein tree indicating that SacRs from freshwater lakes form a broad clade of proton pumps. b The ion-pumping activity of SacRs. Blue and green lines indicate the pH change with and without 10 μM CCCP, respectively. Yellow bars indicate the period of light illumination. c Time evolution of transient absorption changes of SacRNC335 in 100 mM NaCl, 20 mM HEPES–NaOH, pH 7.0, and POPE/POPG (molar ratio 3:1) vesicles with a lipid to protein molar ratio = 50. Time evolution at 406 nm (blue, representing the M accumulation), 561 nm (green, representing the bleaching of the initial state and the L accumulation), and 638 nm (red, representing the K and O accumulations). Yellow lines indicate fitting curves by a multi-exponential function. Inset: The photocycle of SacRNC335 based on the fitting in (c) and a kinetic model assuming a sequential photocycle. The lifetime (τ) of each intermediate is indicated by numbers as follow (mean ± S.D., fraction of the intermediate decayed with each lifetime in its double exponential decay is indicated in parentheses): I: τ = 1.7 ± 0.3 μs (42%), τ = 13 ± 1.8 μs (58%), II: τ = 118 ± 2 μs, III: τ = 1.6 ± 0.1 ms, IV: τ = 23.5 ± 1.0 ms, V: τ = 98.4 ± 6.4 ms (56%), τ = 384 ± 18 ms (44%). d Genomic context of SacRNC335. Neighboring genes with above-threshold KEGG annotations are indicated in gray with the highest-scoring HMM model. Genes without KEGG annotations are indicated in white.Full size imageProton transport assays for the SacRs and Gammaproteobacteria proteins expressed in Escherichia coli showed marked decrease of external pH upon light illumination (Fig. 1b and Fig. S2), indicating that these proteins are light-driven outward H+ pumps. The pH decrease was almost eliminated after adding the protonophore carbonyl cyanide m-chlorophenyl hydrazone (CCCP), which dissipates the H+ gradient, confirming that it was indeed formed upon illumination (Fig. 1b and Fig. S2). We also characterized the absorption spectra and the photocycle of the SacRs, showing that the three rhodopsins have an absorption peak around 550 nm (Fig. S3). The photocycle of the SacRs, determined by measuring the transient absorption change after nanosecond laser pulse illumination (Fig. 1c and Fig. S4), displays a blue-shifted M intermediate that represents the deprotonated state of the retinal chromophore. This has been observed for other H+ pumping rhodopsins [7, 8] and indicates that the proton bound to retinal is translocated during pumping.Given that SacRs function as outward proton pumps, we searched Saccharibacteria genomes for the F1Fo ATP synthase that would be required to harness the generated proton motive force for ATP synthesis. HMM searches showed that all genomes encoded the complete ATP synthase gene cluster and, furthermore, had c subunits with motifs consistent with H+ binding, instead of Na+ binding (Table S1 and Fig. S5). Together, our experimental and genomic analyses strongly suggest that some Saccharibacteria utilize rhodopsins for auxiliary energy generation in addition to their core fermentative capacities [6].Retinal is the rhodopsin chromophore that enables function of the complex upon illumination [9]. We found no evidence for the presence of β-carotene 15,15’-dioxygenase (blh), which produces all-trans-retinal (ATR) from β-carotene, in Saccharibacteria genomes encoding rhodopsin. This absence was likely not due to genome incompleteness, as genomic bins were generally of high quality (79–98% completeness, Table S1) and rhodopsin genomic loci were well-sampled. Additionally, no conserved hypothetical proteins were present in these regions, where blh is often found [10] (Fig. 1d, Fig. S6 and Table S2). As SacRs do contain the conserved lysine for retinal binding [4], we instead hypothesized that Saccharibacteria may uptake retinal from the environment, as has been previously observed for other microorganisms encoding rhodopsin but also lacking blh [11, 12].We tested the ability of SacR proteins to bind ATR from an external source by performing a retinal reconstitution assay. In contrast to the proton transport assays, where rhodopsin was expressed in the presence of ATR, here ATR was dissociated from the purified complex and the visible absorbance of rhodopsin was measured upon re-addition of ATR [13]. Both Gloeobacter rhodopsin (GR), a typical Type-1 outward H+ pump, and SacRs showed an increase in absorption in the visible region with time after the addition of ATR (Fig. 2a and Fig. S7). For all SacRs, the binding of ATR by their apoprotein was saturated within 30 sec after retinal addition (Fig. 2b), indicating that SacR is able to be efficiently functionalized using externally derived ATR. The observed reconstitution rate is substantially faster than that of GR (  > 20 min) and comparable to that of heliorhodopsin, which is used by other microorganisms also lacking a retinal synthetic pathway and rapidly binds ATR through a small opening in the apoprotein [12]. In the structure of SacRNC335 modeled by Alphafold2 [14, 15], a similar hole is visible in the protein moiety constructing the retinal binding pocket (Fig. S8). Hence, SacRs may also bind retinal through this hole in a similar manner to TaHeR (heliorhodopsin).Fig. 2: Binding of retinal by Saccharibacteria rhodopsins and context for biosynthesis.a UV-visible absorption spectra showing the regeneration of retinal binding to SacRNC335 and GR in 20 mM HEPES–NaOH, pH 7.0, 100 mM NaCl and 0.05% n-dodecyl-β-D-maltoside (DDM). In SacRNC335, a peak around 470 nm was transiently observed in the spectrum 30 s after the addition of ATR, suggesting that an intermediate species appears during the retinal incorporation process that involves formation of the Schiff base linkage. b Time evolution of visible absorption representing retinal binding to apo-protein. Numbers in parentheses in the legend indicate the absorption maxima of each rhodopsin. c Genetic potential for β-carotene 15,15’-dioxygenase (blh) production in freshwater lake metagenomes where SacRs are found. Fractions indicate the number of blh-encoding scaffolds taxonomically affiliated with the Actinobacteria in each sample. d Conceptual diagram illustrating potential retinal exchange between Saccharibacteria and host cells. ATR all-trans-retinal, GR Gloeobacter rhodopsin, AM Alinen Mustajärvi, Ki Kiruna, rhod. rhodopsin.Full size imageSaccharibacteria with rhodopsin must obtain retinal from other organisms. To evaluate possible sources of ATR, we investigated the genetic potential for retinal biosynthesis in 15 subarctic and boreal lakes [16] where Saccharibacteria with rhodopsin were present (Fig. S9). Blh-encoding scaffolds were found in 14 of the 15 metagenomes profiled (~93%) and, in nearly all cases, these scaffolds derived from Actinobacteria (Fig. 2c and Table S3). This is intriguing because Actinobacteria are known to be hosts of Saccharibacteria in the human microbiome [17, 18] and potentially more generally [4, 19]. BLAST searches against genome bins from the same samples indicated that these Actinobacteria were members of the order Nanopelagicales (Table S3) and often encode a rhodopsin (phylogenetically distinct from SacRs) in close genomic proximity to blh genes (Table S4). HMM searches revealed that these genomes also harbor homologs of the crtI, crtE, crtB, and crtY genes necessary for β-carotene production [20]. More