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    Whales in the way

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    From under the ice

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    Multiple bacterial partners in symbiosis with the nudibranch mollusk Rostanga alisae

    Symbiont diversity and distributionThe present study provides the first evidence of symbiosis in R. alisae, a species of nudibranchs. This is the most multiple symbiosis that have ever been recorded for marine invertebrates. While many organisms establish an exclusively one-on-one relationship with a single microbial species or microbes belonging to the same functional group5,12, there are also organisms that harbor multiple microbial species, in which symbiont–symbiont and host–symbiont interactions occur. Six phylotypes of chemoautotrophic bacteria were reported for mussel Idas sp. from a cold seep area11 and five extracellular symbionts for the gutless oligochaete worm Olavius algarvensis34. However, in these cases, symbioses involving bacteria and marine invertebrates are either endosymbiotic microbes co-occurring inside the host bacteriocytes5,11 or ectosymbiotic microbes associated with the external surfaces of the animals3,4,9,15,34, with the exception of scaly-foot snail from hydrothermal vents having partnerships simultaneously with epi- and endosymbiontic microbes35.Bacterial symbionts in R. alisae have appeared to be more diverse than was previously known for marine invertebrates. It is evident that the detected symbiont phylotypes differ greatly from all other known symbionts found in marine invertebrates. Labrenzia (Rodobacteriales) and Maritalea (Rhizobiales) have not been recorded as forming symbiotic associations with invertebrates or plants so far, although other members of the families Rodobacteriales and Rhizobiales are well known symbionts14. Strains of Bradyrhizobium, Burkholderia, Achromobacter, and Stenotrophomonas are reported as symbionts of plants, interacting with a vast majority of nodulating legume species and efficient in biological nitrogen fixation36. This may be important when considering the nature of these symbionts in the nudibranch. Symbioses between cyanobacteria and marine organisms are commonly found among marine plants, fungi, sponges, ascidians, corals, and protists37,38. Synechococcus, identified as dominant symbiont clones of R. alisae (Table S2), is a unicellular cyanobacterium common in the marine environment, providing a range of beneficial functions including photosynthesis, nitrogen fixation, UV protection, and production of defensive toxins8,9,37. Symbiotic interactions between actinobacteria and their host have been observed in insects, human, animals, and plants, where the bacteria provide the host with protection against pathogens and produce essential nutrients39. However, none of the members of the clade Actinobacteria recorded in R. alisae are known to live symbiotically.Arrangement of symbiotic associationDespite the high diversity of bacteria, they are well organized in the host. Dense clusters of rod-shaped bacteria, Labrenzia, Maritalea, Bradyrhizobium, Burcholderia, Achromobacter, and Stenotrophomonas, were found within host-derived vacuoles, referred to as bacteriocytes, inside epithelial cells of R. alisae (Fig. 3). Although such arrangement differs from that typical of bacteriocytes, which are usually considered as specialized cells of the hosts for harboring bacteria, it resembles that reported for scaly-food snail from hydrothermal vents, which harbor symbionts in the esophageal gland35. Bacteriocytes in the gastropod Lurifax vitreus found near hydrothermal vents also constitute a portion of the mantle epithelium; they have large vacuoles containing many live and dividing bacteria40. Each bacteriocyte was densely packed with certain symbionts, and the bacteriocytes were randomly distributed within the epithelium cells. A distinctly regular distribution pattern was observed in the gill epithelium of the mussel Bathymodiolus sp.: the thiotrophic symbionts occupy the apical region, and the methanotrophic symbionts are more abundant in the basal region of bacteriocytes4. In the mussel Idas sp., however, there is no spatial pattern of the six distinct bacterial phylotypes, and the symbionts are mixed within bacteriocytes11.Synechococcus dominated the cytoplasm of intestinal epithelium and, more rarely, epidermis cells, mainly as specialized cell type referred to as nitrogen-fixing heterocysts. They are visually similar to cyanobacteria from corals and sponges8,37.The phylogenetic diversity and the spatial organization of the symbiotic community in R. alisae were determined by the 16S rRNA analysis, which was consistent with the results of FISH and TEM. Unlike most symbioses of marine invertebrates when bacteria house specialized host cells5,11 or cover epidermis7,15, symbiotic association of R. alisae exhibited spatial partitioning between symbionts, which were unevenly distributed between the tissues (Table S2). It has been established that different members of the microbial community can complement each other in acquisition of various restrictive nutrients, confirming the importance of the functional diversity of symbionts41. Thus, Stenotrophomonas rhizophila and Bradyrhizobium build a beneficial association in the rhizosphere and can act synergistically on promoting growth and nutrient uptake of soybean36. Cyanobacteria can interact synergistically with beneficial members from the endophytic microbiome of rice seedlings42. The location of bacterium in the organism of R. alisae may, in fact, depend on the specific metabolic and ecological roles that the symbionts play, and also on the interaction with bacterium belonging to different physiological groups.Nature of symbiosisSymbiotic associations between microbes and invertebrates are acquired mainly in a nutrient-depleted environment where symbionts usually provide their hosts with essential nutrients and high-energy compounds1. In contrast to known symbioses between microbes and gutless invertebrates, which obtain nutrients exclusively from the bacteria, R. alisae, like most nudibranch species, is a sponge-eating predator. However, due to the lack of adipose tissue, sponges are distinguished by a low lipid content (0.4 to 1.5% of wet weight)43 and also by specific proteinaceous spongin fibers and chitin, a polysaccharide similar to cellulose that can be indigestible for some predators, which together indicate their low nutritional value. Furthermore, R. alisae feeds exclusively on the sponge O. pennata; therefore, in habitats with low prey availability, this nudibranch has to survive starvation while searching for sponge assemblages. We suppose that symbiotic bacteria of R. alisae contribute to the utilization of low-quality food, similarly to symbiotic bacteria from the genera Rhodobacter, Burkholderia, and Aeromonas associated with the detritivorous isopod Asellus aquaticus44.A fatty acid analysis, as a useful approach to clarifying the nature of symbiosis5,20,32, has confirmed the trophic interaction between symbionts and the nudibranch host (Table S2). Among the fatty acids of symbiotic bacteria in R. alisae, OBFA are a major acyl constituent of membranes in Stenotrophomonas45 and also in Actinobacteria, Arthrobacter, Iamia, Ilumatobacter, and Kocuria46. Cis-vaccenic acid is a major component of Maritalea30. Omega-cyclohexyl tridecanoic acid (cyclo19:0) is specific to Bradyrhizobium47, Burkholderia, and Achromobacter48. Linoleic acid is produced by cyanobacteria including marine species of Synecoccocus33; in nudibranch, it obviously serves as a precursor in the synthesis of arachidonic acid (20:4n-6), thus, providing additional evidence for the transfer of fatty acids from symbionts to the host. Mollusks are capable of converting linoleic acid to arachidonic acid, since they have enzymes required for its synthesis21. The presence of these bacteria-specific markers and the abundance of arachidonic acid confirm the metabolic role of symbionts in the nudibranch host.Among nutrients, biologically available nitrogen can be considered a restrictive nutrient for the sponge-eating R. alisae, which can be acquired with the help of nitrogen-fixing symbionts, also referred to as diazotrophs. R. alisae harbors Bradyrhizobium, Burkholderia, Achromobacter, and Stenotrophomonas that are efficient in biological nitrogen fixation previously found to be associated with nodulating legume species36. Symbiotic nitrogen fixers are known to be associated with a variety of marine invertebrates such as wood-boring bivalves, corals, sponges, sea urchins, tunicates, and polychaetes7,8,37. Moreover, the protection of the enzyme nitrogenase that catalyzes N2 fixation against oxygen is an important physiological requirement in bacteria such as symbiotic Bradyrhizobium, Burkholderia, Achromobacter, and Stenotrophomonas that are located in bacteriocytes and provide this protection. Synechococcus is known as a nitrogen-fixer37,49. It performs N2 fixation in heterocysts where nitrogenase is restricted under oxic conditions. Indeed, heterocysts of Synechococcus are abundant in the intestine cells of R. alisae (Fig. 5B–D).Nitrate assimilation is one of the major processes of nitrogen acquisition by many heterotrophic bacteria and cyanobacteria50,51. Symbionts of R. alisae can play an important role in the process of nitrate utilization through denitrification, dissimilatory nitrate reduction, and assimilatory nitrate reduction as a nitrogen source and synthesize it into organic nitrogen. The nitrate reducers, Labrenzia52, Stenotrophomonas53, Maritalea30, and Rhodobacteraceae29 are widely represented in R. alisae. Synechococcus also utilizes nitrate, nitrite, or ammonium for growth50. Thus, symbiotic bacteria may play a significant role in the N-budget of the nudibranch mollusk.The symbiotic bacteria of R. alisae, including Bradyrhizobium, Maritalea, Labrenzia, Burkholderia, Achromobacter, Stenotrophomonas, Arthrobacter, Iamia, Ilumatobacter, and Kocuria, are known as carboxydotrophic or carbon monoxide (CO) oxidizers54,55. Despite the toxicity of CO for multicellular organisms, numerous aerobic and anaerobic microorganisms can use CO as a source of energy and/or carbon for cell growth56. The marine worm Olavius algarvensis establishes symbiosis with chemosynthetic bacteria using CO, a substrate previously not known to play a role in symbiotic associations with marine invertebrates, as an energy source57. We do not rule out that the R. alisae symbionts also might exploit CO as carbon and energy source. Despite this, assumption may seem impossible taking in account the CO toxicity, but, since many invertebrates (mollusks, tube worm, etc.) use toxic sulfate, thiosulfate, and methane as an energy source1,15, this hypothesis is worth to be addressed.An important component of skeleton in marine sponges of the family Microcionidae, including O. pennata, is the structural polysaccharide chitin58. Some bacteria are capable of hydrolyzing chitin via the activity of chitinolytic enzymes and can utilize chitin as a source of carbon, nitrogen, and/or energy59. Chitinase activity was documented for strains of Labrenzia60, Burkholderia61, Arthrobacter62, Achromobacter63, Stenotrophomonas64, Alcaligenes65, and actinobacteria59 associated with R. alisae. Thus, these bacteria can work synergistically to digest chitin and spongin, contributing to feeding success of the host nudibranch which depends solely on low-quality, nitrogen- and carbon-deficient food available.Furthermore, direct evidence has confirmed that many bioactive compounds from invertebrates are produced by symbiotic microorganisms66,67. Many biologically active compounds including toxic and deterrent secretions have been identified in nudibranchs of the family Discodorididae68. Symbiotic bacteria may exhibit toxic activity to provide the host nudibranch with chemical defense against predators and environment. Bacteria, especially actinobacteria, living in a symbiotic relationship with R. alisae may help the host in defense, since nudibranch lack a shell, and secondary metabolites of bacteria can provide them with chemical defense against predators and environment, as has been reported for some marine invertebrates2,9,10.In complex associations, the integration and coexistence of symbionts depend on supplementary partnerships and mutual contribution to the host’s metabolism41. The most intensively studied cases are highly specialized associations, where both partners can only exist in close relationship with one another. The relatively high diversity of microbes in R. alisae complicates understanding the complex pattern of molecular and cellular interactions between the host and its symbionts. More

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    Resilience of countries to COVID-19 correlated with trust

    Up to 1 December 2020, 156 countries had exhibited at least one peak and then decay of cases/capita (of which 36 had experienced a second peak and decay), 151 countries had exhibited at least one peak and then decay of deaths/capita (of which 32 had experienced a second peak and decay), and 93 countries had sufficient testing data to determine at least one peak and then decay of cases/tests (of which 23 had experienced a second peak and decay). Time-series for all countries and the three metrics are shown in Supplementary Fig. 1. For resilience, having filtered cases of reasonably exponential decay for further analysis (r2 ≥ 0.8) and included multiple instances of well-fitted recovery occurring in one country in the dataset, we obtain n = 177 decays for cases/capita, n = 159 for deaths/capita, n = 105 for cases/tests. In a few countries a minimum had not yet been reached by 1 December 2020, so the reduction dataset is smaller (cases/capita n = 165, deaths/capita n = 150, cases/tests n = 101).Comparable resilience and reduction of cases and deathsThe relative measures of resilience (rate of decay) and (proportional) reduction of cases should be more reliably estimated than absolute case numbers but could still be biased by variations in testing intensity across time and space. Encouragingly, we find across countries and waves, resilience of cases/capita and cases/tests are strongly positively rank correlated (n = 100, (rho) =0.86, p  More

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    Network traits predict ecological strategies in fungi

    1.Fischer MS, Glass NL. Communicate and fuse: How filamentous fungi establish and maintain an interconnected mycelial network. Front Microbiol. 2019;10:619.2.Fricker MD, Heaton LLM, Jones NS, Boddy L. The mycelium as a network. Microbiol Spectr. 2017;5:335–67.3.Heaton LLM, Jones NS, Fricker MD. A mechanistic explanation of the transition to simple multicellularity in fungi. Nat Commun. 2020;11:2594.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    4.Kiss E, Hegedus B, Viragh M, Varga T, Merenyi Z, Koszo T, et al. Comparative genomics reveals the origin of fungal hyphae and multicellularity. Nat Commun. 2019;10:4080.PubMed 
    PubMed Central 

    Google Scholar 
    5.Nagy LG, Varga T, Csernetics Á, Virágh M. Fungi took a unique evolutionary route to multicellularity: Seven key challenges for fungal multicellular life. Fungal Biol Rev. 2020;34:151–69.
    Google Scholar 
    6.Naranjo-Ortiz MA, Gabaldon T. Fungal evolution: major ecological adaptations and evolutionary transitions. Biol Rev Camb Philos Soc. 2019;94:1443–76.PubMed 
    PubMed Central 

    Google Scholar 
    7.Stajich JE, Berbee ML, Blackwell M, Hibbett DS, James TY, Spatafora JW, et al. The fungi. Curr Biol. 2009;19:R840–845.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    8.Treseder KK, Lennon JT. Fungal traits that drive ecosystem dynamics on land. Microbiol Mol Biol Rev. 2015;79:243–62.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    9.Adler PB, Salguero-Gómez R, Compagnoni A, Hsu JS, Ray-Mukherjee J, Mbeau-Ache C, et al. Functional traits explain variation in plant life history strategies. Proc. Natl Acad Sci USA. 2014;111:740–5.CAS 
    PubMed 

    Google Scholar 
    10.Pérez-Harguindeguy N, Díaz S, Garnier E, Lavorel S, Poorter H, Jaureguiberry P, et al. New handbook for standardised measurement of plant functional traits worldwide. Aust J Bot. 2013;61:167.
    Google Scholar 
    11.Dawson SK, Boddy L, Halbwachs H, Bässler C, Andrew C, Crowther TW, et al. Handbook for the measurement of macrofungal functional traits: A start with basidiomycete wood fungi. Funct Ecol. 2018;33:372–87.
    Google Scholar 
    12.Aguilar-Trigueros CA, Hempel S, Powell JR, Anderson IC, Antonovics J, Bergmann J, et al. Branching out: Towards a trait-based understanding of fungal ecology. Fungal Biol Rev. 2015;29:34–41.
    Google Scholar 
    13.Pringle A, Taylor JW. The fitness of filamentous fungi. Trends Microbiol. 2002;10:474–81.CAS 
    PubMed 

    Google Scholar 
    14.Zanne AE, Abarenkov K, Afkhami ME, Aguilar-Trigueros CA, Bates S, Bhatnagar JM, et al. Fungal functional ecology: Bringing a trait-based approach to plant-associated fungi. Biol Rev. 2020;95:409–33.PubMed 

    Google Scholar 
    15.Boddy L. Saprotrophic cord-forming fungi: Meeting the challenge of heterogeneous environments. Mycologia. 1999;91:13–32.
    Google Scholar 
    16.Boddy L, Donnelly DP. Fractal geometry and microorganisms in the environment. Biophys Chem Fractal Struct Processes Environ Syst. 2008;11:239–72.17.Lehmann A, Zheng W, Soutschek K, Roy J, Yurkov AM, Rillig MC. Tradeoffs in hyphal traits determine mycelium architecture in saprobic fungi. Sci Rep. 2019;9:14152.PubMed 
    PubMed Central 

    Google Scholar 
    18.Serghi EU, Kokkoris V, Cornell C, Dettman J, Stefani F, Corradi N. Homo- and dikaryons of the arbuscular mycorrhizal fungus rhizophagus irregularis differ in life history strategy. Front Plant Sci. 2021;12:1544.
    Google Scholar 
    19.Held M, Edwards C, Nicolau DV. Probing the growth dynamics of Neurospora crassa with microfluidic structures. Fungal Biol. 2011;115:493–505.PubMed 

    Google Scholar 
    20.Aleklett K, Ohlsson P, Bengtsson M, Hammer EC. Fungal foraging behaviour and hyphal space exploration in micro-structured Soil Chips. ISME J. 2021;15:1782–1793.21.De Ligne L, Vidal-Diez de Ulzurrun G, Baetens JM, Van den Bulcke J, Van Acker J, De Baets B. Analysis of spatio-temporal fungal growth dynamics under different environmental conditions. IMA Fungus. 2019;10:7.PubMed 
    PubMed Central 

    Google Scholar 
    22.Dikec J, Olivier A, Bobee C, D’Angelo Y, Catellier R, David P, et al. Hyphal network whole field imaging allows for accurate estimation of anastomosis rates and branching dynamics of the filamentous fungus Podospora anserina. Sci Rep. 2020;10:3131.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    23.Du H, Lv P, Ayouz M, Besserer A, Perré P. Morphological characterization and quantification of the mycelial growth of the Brown-Rot fungus Postia placenta for modeling purposes. PLoS One. 2016;11:e0162469.PubMed 
    PubMed Central 

    Google Scholar 
    24.Vidal-Diez de Ulzurrun G, Baetens JM, Van den Bulcke J, Lopez-Molina C, De Windt I, De Baets B. Automated image-based analysis of spatio-temporal fungal dynamics. Fungal Genet Biol. 2015;84:12–25.CAS 
    PubMed 

    Google Scholar 
    25.Boddy L, Wood J, Redman E, Hynes J, Fricker MD. Fungal network responses to grazing. Fungal Genet Biol. 2010;47:522–30.PubMed 

    Google Scholar 
    26.Rotheray TD, Jones TH, Fricker MD, Boddy L. Grazing alters network architecture during interspecific mycelial interactions. Fungal Ecol. 2008;1:124–32.
    Google Scholar 
    27.Bebber DP, Hynes J, Darrah PR, Boddy L, Fricker MD. Biological solutions to transport network design. Proc Biol Sci/R Soc. 2007;274:2307–15.
    Google Scholar 
    28.Fricker MD, Akita D, Heaton LLM, Jones N, Obara B, Nakagaki T. Automated analysis of Physarumnetwork structure and dynamics. J Phys D: Appl Phys. 2017;50:254005.
    Google Scholar 
    29.Lee SH, Fricker MD, Porter MA. Mesoscale analyses of fungal networks as an approach for quantifying phenotypic traits. J Complex Netw. 2017;5:145–59.
    Google Scholar 
    30.Obara B, Grau V, Fricker MD. A bioimage informatics approach to automatically extract complex fungal networks. Bioinformatics. 2012;28:2374–81.CAS 
    PubMed 

    Google Scholar 
    31.Bebber DP, Tlalka M, Hynes J, Darrah PR, Ashford A, Watkinson SC et al. Fungi and the environment. Cambridge: Cambridge University Press; 2007. p. 1−21.32.Fricker MD, Lee JA, Bebber DP, Tlalka M, Hynes J, Darrah PR, et al. Imaging complex nutrient dynamics in mycelial networks. J. Microsc. 2008;231:317–31.CAS 
    PubMed 

    Google Scholar 
    33.Vidal-Diez de Ulzurrun G, Huang T-Y, Chang C-W, Lin H-C, Hsueh Y-P. Fungal feature tracker (FFT): A tool for quantitatively characterizing the morphology and growth of filamentous fungi. PLoS Comp Biol. 2019;15:e1007428.CAS 

    Google Scholar 
    34.Heaton LLM, López E, Maini PK, Fricker MD, Jones NS. Growth-induced mass flows in fungal networks. Proc R Soc B: Biol Sci. 2010;277:3265–74.
    Google Scholar 
    35.Heaton LLM, López E, Maini PK, Fricker MD, Jones NS. Advection, diffusion, and delivery over a network. Phys Rev E. 2012;86:021905.
    Google Scholar 
    36.Fricker MD, Boddy L, Nakagaki T, Bebber DP (2009). Adaptive biological networks. In: Gross T, Sayama H, editors. Adaptive networks: theory, models, and applications. Berlin: Springer; 2009. p. 51−70.37.Boddy L, Jones TH. Mycelial responses in heterogeneous environments: parallels with macroorganisms. Fungi Environ. 2007;25:112–58.
    Google Scholar 
    38.Crowther TW, Boddy L, Hefin Jones T. Functional and ecological consequences of saprotrophic fungus–grazer interactions. ISME J. 2012;6:1992–2001.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    39.Crowther TW, Jones TH, Boddy L. Interactions between saprotrophic basidiomycete mycelia and mycophagous soil fauna. Mycology. 2012;3:77–86.
    Google Scholar 
    40.Tordoff GM, Boddy L, Jones TH. Grazing by Folsomia candida (Collembola) differentially affects mycelial morphology of the cord-forming basidiomycetes Hypholoma fasciculare, Phanerochaete uelutina, and Resinicium bicolor. Mycol Res. 2006;110:335–45.PubMed 

    Google Scholar 
    41.Heaton L, Obara B, Grau V, Jones N, Nakagaki T, Boddy L, et al. Analysis of fungal networks. Fungal Biol Rev. 2012;26:12–29.
    Google Scholar 
    42.Barthelemy M. Morphogenesis of spatial networks. Cham, Switzerland: Springer International Publishing; 2018.43.Fricker MD, Bebber D, Boddy L. Chapter 1 Mycelial networks: structure and dynamics. In: Boddy L, Frankland JC, van West P, editors. British mycological society symposia series. London, UK, Academic Press; 2008. p. 3−18.44.Fricker M, Boddy L, Bebber D. Biology of the fungal cell. Berlin Heidelberg: Springer Verlag; 2007. p. 309−30.45.Fricker MD, Lee JA, Boddy L, Bebber DP. The Interplay between structure and function in fungal networks. Topologica 2008;1:004.46.Moore D, Robson GD, Trinci AP. 21st century guidebook to fungi. Cambridge, UK: Cambridge University Press; 2011.47.Bielčik M, Aguilar-Trigueros CA, Lakovic M, Jeltsch F, Rillig MC. The role of active movement in fungal ecology and community assembly. Movement Ecol. 2019;7:36.
    Google Scholar 
    48.Wright IJ, Reich PB, Westoby M, Ackerly DD, Baruch Z, Bongers F, et al. The worldwide leaf economics spectrum. Nature. 2004;428:821–7.CAS 

    Google Scholar 
    49.Hart Y, Sheftel H, Hausser J, Szekely P, Ben-Moshe NB, Korem Y, et al. Inferring biological tasks using Pareto analysis of high-dimensional data. Nat Methods. 2015;12:233–5.CAS 
    PubMed 

    Google Scholar 
    50.Shoval O, Sheftel H, Shinar G, Hart Y, Ramote O, Mayo A, et al. Evolutionary trade-offs, Pareto optimality, and the geometry of phenotype space. Science. 2012;336:1157–60.CAS 
    PubMed 

    Google Scholar 
    51.Andrade-Linares DR, Veresoglou SD, Rillig MC. Temperature priming and memory in soil filamentous fungi. Fungal Ecol. 2016;21:10–15.
    Google Scholar 
    52.A’Bear AD, Boddy L, Hefin Jones T. Impacts of elevated temperature on the growth and functioning of decomposer fungi are influenced by grazing collembola. Global Change Biol. 2012;18:1823–32.
    Google Scholar 
    53.Boddy L, Wells JM, Culshaw C, Donnelly DP. Fractal analysis in studies of mycelium in soil. Geoderma. 1999;88:301–28.
    Google Scholar 
    54.Pain C, Kriechbaumer V, Kittelmann M, Hawes C, Fricker M. Quantitative analysis of plant ER architecture and dynamics. Nat Commun. 2019;10:984.PubMed 
    PubMed Central 

    Google Scholar 
    55.Xu H, Blonder B, Jodra M, Malhi Y, Fricker M. Automated and accurate segmentation of leaf venation networks via deep learning. New Phytol. 2021;229:631–48.PubMed 

    Google Scholar 
    56.Wickham H, Bryan J. Read Excel Files. R package version 1.3.1. 2019. https://CRAN.R-project.org/package=readxl.57.Csardi G, Nepusz T. The igraph software package for complex network research. InterJ, Complex Syst. 2006;1695:1–9.
    Google Scholar 
    58.R Development Core Team. A language and environment for statistical computing. Vienna, Austria: R Foundation for statistical computing; 2017.59.Oksanen J, Guillaume Blanchet F, Kindt R, Legendre P, Minchin PR, O’Hara RB et al. vegan: Community ecology package. 2012. https://CRAN.R-project.org/package=vegan.60.A’Bear AD, Jones TH, Boddy L. Size matters: What have we learnt from microcosm studies of decomposer fungus–invertebrate interactions? Soil Biol Biochem. 2014;78:274–83.
    Google Scholar 
    61.Trinci APJ. A kinetic study of the growth of Aspergillus nidulans and other fungi. Microbiology. 1969;57:11–24.CAS 

    Google Scholar 
    62.Morin-Sardin S, Nodet P, Coton E, Jany J-L. Mucor: A Janus-faced fungal genus with human health impact and industrial applications. Fungal Biol Rev. 2017;31:12–32.
    Google Scholar 
    63.Grigoriev IV, Nikitin R, Haridas S, Kuo A, Ohm R, Otillar R, et al. MycoCosm portal: gearing up for 1000 fungal genomes. Nucleic Acids Res. 2014;42:D699–704.CAS 
    PubMed 

    Google Scholar 
    64.Naranjo-Ortiz MA, Gabaldon T. Fungal evolution: diversity, taxonomy, and phylogeny of the Fungi. Biol Rev Camb Philos Soc. 2019;94:2101–37.PubMed 
    PubMed Central 

    Google Scholar 
    65.Domsch K, Gams W, Anderson T-H. Compendium of soil fungi. 2nd ed. Eching: IHW-Verlag; 2007.66.Thomma BP. Alternaria spp.: from general saprophyte to specific parasite. Mol Plant Pathol. 2003;4:225–36.CAS 
    PubMed 

    Google Scholar 
    67.Bacon C, Yates I. Endophytic root colonization by fusarium species: histology, plant interactions, and toxicity. In: Schulz BE, Boyle CC, Sieber T, editors. Microbial root endophytes. Berlin: Springer; 2006. p. 133−52.68.Nguyen TA, Le S, Lee M, Fan J-S, Yang D, Yan J, et al. Fungal wound healing through instantaneous protoplasmic gelation. Curr Biol. 2021;31:271–82. e275CAS 
    PubMed 

    Google Scholar 
    69.Scheu S, Simmerling F. Growth and reproduction of fungal feeding Collembola as affected by fungal species, melanin, and mixed diets. Oecologia. 2004;139:347–53.PubMed 

    Google Scholar 
    70.Rayner ADM, Boddy L. Fungal decomposition of wood. Its biology and ecology. Chichester, Sussex: John Wiley & Sons Ltd.; 1988.71.Connolly JH, Shortle WC, Jellison J. Translocation and incorporation of strontium carbonate derived strontium into calcium oxalate crystals by the wood decay fungus Resinicium bicolor. Can J Botany. 1999;77:179–87.CAS 

    Google Scholar 
    72.A’Bear AD, Jones TH, Boddy L. Potential impacts of climate change on interactions among saprotrophic cord-forming fungal mycelia and grazing soil invertebrates. Fungal Ecol. 2014;10:34–43.
    Google Scholar 
    73.Fukasawa Y, Savoury M, Boddy L. Ecological memory and relocation decisions in fungal mycelial networks: responses to quantity and location of new resources. ISME J. 2020;14:380–8.PubMed 

    Google Scholar 
    74.Crowther TW, Maynard DS, Crowther TR, Peccia J, Smith JR, Bradford MA. Untangling the fungal niche: the trait-based approach. Front Microbiol. 2014;5:579. More

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    Soils and sediments host Thermoplasmata archaea encoding novel copper membrane monooxygenases (CuMMOs)

    Divergent CuMMOs identified in MAGs recovered from soil and sediment ecosystemsIn previous work we identified putative divergent amoA/pmoA homologues in 7 Thermoplasmatota genomes recovered from Mediterranean grassland soil [25]. This was intriguing, given that amo/pmo homologues had not been previously observed in archaea outside of the Nitrososphaerales. Here we searched for additional genomes encoding related (divergent) amo/pmo’s using a series of readily available, and custom built, hidden markov models (HMMs) across all archaeal genomes in the Genome Taxonomy Database (GTDB), and in all archaeal MAGs in our unpublished datasets from ongoing studies (Supplementary Fig. 1 and Supplementary Data 1). We found additional amoA/pmoA genes in genomes recovered from soils at the South Meadow and Rivendell sites of the Angelo Coast Range Reserve (CA) [25, 26], the nearby Sagehorn site [26], a hillslope of the East River watershed (CO) [27], and in sediments from the Rifle aquifer (CO) [28] and the deep ocean [29]. In total we identified 201 archaeal MAGs taxonomically placed using phylogenetically informative single copy marker genes outside of Nitrososphaerales containing divergent amo/pmo proteins (Supplementary Table 1 and Supplementary Data 1). Genome de-replication resulted in 34 species-level genome clusters, 20 of which encoded an amo/pmo homologue (Supplementary Table 2). Of these genomes, 11 are species not previously available in public databases. In all cases where assembled sequences were of sufficient length, the amoA/pmoA, B, and C protein coding genes were found co-located with each other and with a hypothetical protein here called amoX/pmoX in the order C-A-X-B (Fig. 1A, Supplementary Table 2, and Supplementary Fig. 2). The mean sequence identity of the novel amoA/pmoA, B, and C proteins to known bacterial sequences were 16.7, 8.0, and 14.2% and 13.8, 9.5, and 20.8% to known archaeal sequences. This level of divergent amino acid identity is typical for CuMMOs, as known bacterial and known archaeal amoA/pmoA, B, and C proteins share mean identities of 16.1, 9.7, and 16.5% respectively. As might be expected considering the large sequence divergence between the recovered sequences and known amo/pmo proteins, we found that no pair of typical primers used for bacterial and archaeal amoA/pmoA environmental surveys [30] matched any novel amoA/pmoA gene with More

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    Emergence of methicillin resistance predates the clinical use of antibiotics

    1.Davies, J. & Davies, D. Origins and evolution of antibiotic resistance. Microbiol. Mol. Biol. Rev. 74, 417–433 (2010).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    2.European Centre for Disease Prevention and Control, European Medicines Agencies. The Bacterial Challenge: Time to React. A Call to Narrow the Gap Between Multidrug-Resistant Bacteria in the EU and the Development of New Antibacterial Agents https://ecdc.europa.eu/sites/portal/files/media/en/publications/Publications/0909_TER_The_Bacterial_Challenge_Time_to_React.pdf (2009).3.Jevons, M. P. “Celbenin”—resistant Staphylococci. Br. Med. J. 1, 124–125 (1961).PubMed Central 

    Google Scholar 
    4.Harkins, C. P. et al. Methicillin-resistant Staphylococcus aureus emerged long before the introduction of methicillin into clinical practice. Genome Biol. 18, 130 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    5.Chambers, H. F. & DeLeo, F. R. Waves of resistance: Staphylococcus aureus in the antibiotic era. Nat. Rev. Microbiol. 7, 629–641 (2009).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    6.Price, L. B. et al. Staphylococcus aureus CC398: host adaptation and emergence of methicillin resistance in livestock. mBio 3, e00305-11 (2012).PubMed 
    PubMed Central 

    Google Scholar 
    7.Global Priority List of Antibiotic-Resistant Bacteria to Guide Research, Discovery, and Development of New Antibiotics http://www.who.int/medicines/publications/WHO-PPL-Short_Summary_25Feb-ET_NM_WHO.pdf?ua=1 (WHO, 2017).8.Rasmussen, S. L. et al. European hedgehogs (Erinaceus europaeus) as a natural reservoir of methicillin-resistant Staphylococcus aureus carrying mecC in Denmark. PLoS ONE 14, e0222031 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    9.Bengtsson, B. et al. High occurrence of mecC-MRSA in wild hedgehogs (Erinaceus europaeus) in Sweden. Vet. Microbiol. 207, 103–107 (2017).PubMed 

    Google Scholar 
    10.García-Álvarez, L. et al. Methicillin-resistant Staphylococcus aureus with a novel mecA homologue in human and bovine populations in the UK and Denmark: a descriptive study. Lancet Infect. Dis. 11, 595–603 (2011).PubMed 
    PubMed Central 

    Google Scholar 
    11.Paterson, G. K., Harrison, E. M. & Holmes, M. A. The emergence of mecC methicillin-resistant Staphylococcus aureus. Trends Microbiol. 22, 42–47 (2014).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    12.Marples, M. J. & Smith, J. M. B. The hedgehog as a source of human ringworm. Nature 188, 867–868 (1960).ADS 
    CAS 
    PubMed 

    Google Scholar 
    13.English, M. P., Evans, C. D., Hewitt, M. & Warin, R. P. “Hedgehog ringworm”. Br. Med. J. 1, 149–151 (1962).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    14.Smith, J. M. B. & Marples, M. J. A natural reservoir of penicillin-resistant strains of Staphylococcus aureus. Nature 201, 844 (1964).ADS 
    CAS 
    PubMed 

    Google Scholar 
    15.Smith, J. M. B. & Marples, M. J. Dermatophyte lesions in the hedgehog as a reservoir of penicillin-resistant staphylococci. J. Hyg. 63, 293–303 (1965).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    16.Smith, J. M. B. Staphylococcus aureus strains associated with the hedgehog Erinaceus europaeus. J. Hyg. Camb. 63, 293–303 (1965).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    17.Morris, P. & English, M. P. Trichophyton mentagrophytes var. erinacei in British hedgehogs. Sabouraudia 7, 122–128 (1969).CAS 
    PubMed 

    Google Scholar 
    18.Le Barzic, C. et al. Detection and control of dermatophytosis in wild European hedgehogs (Erinaceus europaeus) admitted to a French wildlife rehabilitation centre. J. Fungi 7, 74 (2021).
    Google Scholar 
    19.Dube, F., Söderlund, R., Salomonsson, M. L., Troell, K. & Börjesson, S. Benzylpenicillin-producing Trichophyton erinacei and methicillin resistant Staphylococcus aureus carrying the mecC gene on European hedgehogs: a pilot-study. BMC Microbiol. 21, 212 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    20.Hewitt, G. The genetic legacy of the Quaternary ice ages. Nature 405, 907–913 (2000).ADS 
    CAS 
    PubMed 

    Google Scholar 
    21.Brockie, R. E. Distribution and abundance of the hedgehog (Erinaceus europaeus) L. in New Zealand, 1869–1973. N. Z. J. Zool. 2, 445–462 (1975).
    Google Scholar 
    22.van den Berg, M. A. et al. Genome sequencing and analysis of the filamentous fungus Penicillium chrysogenum. Nat. Biotechnol. 26, 1161–1168 (2008).CAS 
    PubMed 

    Google Scholar 
    23.Ullán, R. V., Campoy, S., Casqueiro, J., Fernández, F. J. & Martín, J. F. Deacetylcephalosporin C production in Penicillium chrysogenum by expression of the isopenicillin N epimerization, ring expansion, and acetylation genes. Chem. Biol. 14, 329–339 (2007).PubMed 

    Google Scholar 
    24.Kitano, K. et al. A novel penicillin produced by strains of the genus Paecilomyces. J. Ferment. Technol. 54, 705–711 (1976).CAS 

    Google Scholar 
    25.Petersen, A. et al. Epidemiology of methicillin-resistant Staphylococcus aureus carrying the novel mecC gene in Denmark corroborates a zoonotic reservoir with transmission to humans. Clin. Microbiol. Infect. 19, E16–E22 (2013).CAS 
    PubMed 

    Google Scholar 
    26.Richardson, E. J. et al. Gene exchange drives the ecological success of a multi-host bacterial pathogen. Nat. Ecol. Evol. 2, 1468–1478 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    27.Holden, M. T. G. et al. A genomic portrait of the emergence, evolution, and global spread of a methicillin-resistant Staphylococcus aureus pandemic. Genome Res. 23, 653–664 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    28.Strauß, L. et al. Origin, evolution, and global transmission of community-acquired Staphylococcus aureus ST8. Proc. Natl Acad. Sci. USA 114, E10596–E10604 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    29.Nübel, U. et al. Frequent emergence and limited geographic dispersal of methicillin-resistant Staphylococcus aureus. Proc. Natl Acad. Sci. USA 105, 14130–14135 (2008).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    30.Rasmussen, S. L., Nielsen, J. L., Jones, O. R., Berg, T. B. & Pertoldi, C. Genetic structure of the European hedgehog (Erinaceus europaeus) in Denmark. PLoS ONE 15, e0227205 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    31.Hansen, J. E. et al. LA-MRSA CC398 in dairy cattle and veal calf farms indicates spillover from pig production. Front. Microbiol. 10, 2733 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    32.Eriksson, J. Espinosa-Gongora, C., Stamphøj, I., Larsen, A. R. & Guardabassi, L. Carriage frequency, diversity and methicillin resistance of in Danish small ruminants. Vet. Microbiol. 163, 110–115 (2013).CAS 
    PubMed 

    Google Scholar 
    33.Danish Integrated Antimicrobial Resistance Monitoring and Research Programme. DANMAP 2019: Use of Antimicrobial Agents and Occurrence of Antimicrobial Resistance in Bacteria From Food Animals, Food, and Humans in DENMARK https://www.danmap.org/-/media/Sites/danmap/Downloads/Reports/2019/DANMAP_2019.ashx?la=da&hash=AA1939EB449203EF0684440AC1477FFCE2156BA5 (2020).34.Veterinary Medicines Directorate. UK Veterinary Antibiotic Resistance and Sales Surveillance Reporthttps://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/950126/UK-VARSS_2019_Report__2020-TPaccessible.pdf (2020).35.Harrison, E. M. et al. Whole genome sequencing identifies zoonotic transmission of MRSA isolates with the novel mecA homologue mecC. EMBO Mol. Med. 5, 509–515 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    36.Loncaric, I. et al. Characterization of mecC gene-carrying coagulase-negative Staphylococcus spp. isolated from various animals. Vet. Microbiol. 230, 138–144 (2019).CAS 
    PubMed 

    Google Scholar 
    37.Gómez, P. et al. Detection of MRSA ST3061-t843-mecC and ST398-t011-mecA in white stork nestlings exposed to human residues. J. Antimicrob. Chemother. 71, 53–57 (2016).PubMed 

    Google Scholar 
    38.Kim, C. et al. Properties of a novel PBP2A protein homolog from Staphylococcus aureus strain LGA251 and its contribution to the β-lactam-resistant phenotype. J. Biol. Chem. 287, 36854–36863 (2012).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    39.Tahlan, K. & Jensen, S. E. Origins of the β-lactam rings in natural products. J. Antibiot. 66, 401–419 (2013).CAS 

    Google Scholar 
    40.Pantůček, R. et al. Staphylococcus edaphicus sp. nov. isolated in Antarctica harbors the mecC gene and genomic islands with a suspected role in adaptation to extreme environment. Appl. Environ. Microbiol. 84, e01746-17 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    41.D’Costa, V. M., et al. Antibiotic resistance is ancient. Nature 477, 457–461 (2011).ADS 
    PubMed 

    Google Scholar 
    42.Allen, H. K., Moe, L. A., Rodbumrer, J., Gaarder, A. & Handelsman, J. Functional metagenomics reveals diverse beta-lactamases in a remote Alaskan soil. ISME J. 3, 243–251 (2009).CAS 

    Google Scholar 
    43.Forsberg, K. J. et al. The shared antibiotic resistome of soil bacteria and human pathogens. Science 337, 1107–1111 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    44.Forsberg, K. J. et al. Bacterial phylogeny structures soil resistomes across habitats. Nature 509, 612–616 (2014).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    45.Coll, F. et al. Definition of a genetic relatedness cutoff to exclude recent transmission of meticillin-resistant Staphylococcus aureus: a genomic epidemiology analysis. Lancet Microbe 1, e328–e335 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    46.Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its application to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012).MathSciNet 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    47.Enright, M. C., Day, N. P., Davies, C. E., Peacock, S. J., Spratt, B. G. Multilocus sequence typing for characterization of methicillin-resistant and methicillin-susceptible clones of Staphylococcus aureus. J. Clin. Microbiol. 38, 1008–1015 (2000).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    48.Van Wamel, W. J., Rooijakkers, S. H., Ruyken, M. van Kessel, K. P. & Strijp, J. A. The innate immune modulators staphylococcal complement inhibitor and chemotaxis inhibitory protein of Staphylococcus aureus are located on beta-hemolysin-converting bacteriophages. J. Bacteriol. 188, 1310–1315 (2006).PubMed 
    PubMed Central 

    Google Scholar 
    49.Viana, D. et al. Adaptation of Staphylococcus aureus to ruminant and equine hosts involved SaPI-carried variants of von Willebrand factor-binding protein. Mol. Microbiol. 77, 1583–1594 (2010).50.Rooijakkers, S. H. M. et al. Staphylococcal complement inhibitor: structure and active sites. J. Immunol. 179, 2989–2998 (2007).CAS 
    PubMed 

    Google Scholar 
    51.Arndt, D. et al. PHASTER: a better, faster version of the PHAST phage search tool. Nucleic Acids Res. 44, W16–W21 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    52.Bortolaia, V. et al. ResFinder 4.0 for predictions of phenotypes from genotypes. J. Antimicrob. Chemother. 75, 3491–3500 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    53.Clausen, P. T. L. C., Aarestrup, F. M. & Lund, O. Rapid and precise alignment of raw reads against redundant database with KMA. BMC Bioinform. 19, 397 (2018).
    Google Scholar 
    54.Sahl, J. W. et al. NASP: an accurate, rapid method for the identification of SNPs in WGS datasets that supports flexible input and output formats. Microb. Genom. 2, e000074 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    55.Li, H. & Durbin, R. Fast and accurate short read alignment with Burrow-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    56.McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    57.DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation sequencing data. Nat. Genet. 43, 491–498 (2011).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    58.Delcher, A. L., Phillippy, A., Carlton, J. & Salzberg, S. L. Fast algorithms for large-scale genome alignment and comparison. Nucleic Acids Res. 30, 2478–2483 (2002).PubMed 
    PubMed Central 

    Google Scholar 
    59.Kurz, S. et al. Versatile and open software for comparing large genomes. Genome Biol. 5, R12 (2004).
    Google Scholar 
    60.Guindon, S. & Gasquel, O. A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst. Biol. 52, 696–704 (2003).PubMed 

    Google Scholar 
    61.Guindon, S. et al. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst. Biol. 59, 307–321 (2010).CAS 

    Google Scholar 
    62.Didelot, X. & Wilson, D. J. ClonalFrameML: efficient inference of recombination in whole bacterial genome. PLoS Comput. Biol. 11, e1004041 (2015).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    63.Didelot, X. et al. Bayesian inference of ancestral dates on bacterial phylogenetic trees. Nucleic Acids Res. 46, e134 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    64.Didelot, X., Siveroni, I. & Volz, E. M. Additive uncorrelated relaxed clock models for the dating of genomic epidemiology phylogenies. Mol. Biol. Evol. 38, 307–317 (2021).CAS 
    PubMed 

    Google Scholar 
    65.Plummer, M., Best, N., Cowles, K. & Vines, K. CODA: convergence diagnosis and output analysis for MCMC. R News 6, 7–11 (2006).
    Google Scholar 
    66.Volz, E. M. & Frost, S. D. Scalable relaxed clock phylogenetic dating. Virus Evol. 3, vex025 (2017).
    Google Scholar 
    67.Wang, M. et al. Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking. Nat. Biotechnol. 34, 828–837 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    68.Adusumilli, R. & Mallick, P. Data conversion with ProteoWizard msConvert. Methods Mol. Biol. 1550, 339–368 (2017).CAS 
    PubMed 

    Google Scholar  More

  • in

    Linkage disequilibrium under polysomic inheritance

    Brown AHD, Feldman MW, Nevo E (1980) Multilocus structure of natural populations of Hordeum spontaneum. Genetics 96:523–536CAS 
    Article 

    Google Scholar 
    Burow MD, Simpson CE, Starr JL, Paterson AH (2001) Transmission genetics of chromatin from a synthetic amphidiploid to cultivated peanut (Arachis hypogaea L.): broadening the gene pool of a monophyletic polyploid species. Genetics 159:823CAS 
    Article 

    Google Scholar 
    Butruille DV, Boiteux LS (2000) Selection–mutation balance in polysomic tetraploids: Impact of double reduction and gametophytic selection on the frequency and subchromosomal localization of deleterious mutations. Proc Natl Acad Sci USA 97:6608–6613CAS 
    Article 

    Google Scholar 
    Cockerham CC, Weir BS (1977) Digenic descent measures for finite populations. Genet Res 30:121–147Article 

    Google Scholar 
    Devlin B, Risch N (1995) A comparison of linkage disequilibrium measures for fine-scale mapping. Genomics 29:311–322CAS 
    Article 

    Google Scholar 
    Do C, Waples RS, Peel D, Macbeth G, Tillett BJ, Ovenden JR (2014) NeEstimator v2: re‐implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Mol Ecol Resour 14:209–214CAS 
    Article 

    Google Scholar 
    England PR, Cornuet J-M, Berthier P, Tallmon DA, Luikart G (2006) Estimating effective population size from linkage disequilibrium: severe bias in small samples. Conserv Genet 7:303Article 

    Google Scholar 
    Fisher RA (1947) The theory of linkage in polysomic inheritance. Philos Trans R Soc Lond Ser B Biol Sci 233:55–87
    Google Scholar 
    Gao XY, Starmer J, Martin ER (2008) A multiple testing correction method for genetic association studies using correlated single nucleotide polymorphisms. Genet Epidemiol 32:361–369Article 

    Google Scholar 
    Hästbacka J, de la Chapelle A, Kaitila I, Sistonen P, Weaver A, Lander E (1992) Linkage disequilibrium mapping in isolated founder populations: diastrophic dysplasia in Finland. Nat Genet 2:204–211Article 

    Google Scholar 
    Hayes BJ, Visscher PM, McPartlan HC, Goddard ME (2003) Novel multilocus measure of linkage disequilibrium to estimate past effective population size. Genome Res 13:635–643CAS 
    Article 

    Google Scholar 
    Hill WG (1974) Disequilibrium among several linked neutral genes in finite population I. Mean changes in disequilibrium. Theor Popul Biol 5:366–392CAS 
    Article 

    Google Scholar 
    Hill WG (1975) Linkage disequilibrium among multiple neutral alleles produced by mutation in finite population. Theor Popul Biol 8:117–126CAS 
    Article 

    Google Scholar 
    Hill WG (1981) Estimation of effective population size from data on linkage disequilibrium. Genet Res 38:209–216Article 

    Google Scholar 
    Hill WG, Robertson A (1968) Linkage disequilibrium in finite populations. Theor Appl Genet 38:226–231CAS 
    Article 

    Google Scholar 
    Hill WG, Weir BS (1994) Maximum-likelihood estimation of gene location by linkage disequilibrium. Am J Hum Genet 54:705CAS 

    Google Scholar 
    Hollenbeck C, Portnoy D, Gold J (2016) A method for detecting recent changes in contemporary effective population size from linkage disequilibrium at linked and unlinked loci. Heredity 117:207–216CAS 
    Article 

    Google Scholar 
    Hosking LK, Boyd PR, Xu CF, Nissum M, Cantone K, Purvis IJ, Khakhar R, Barnes MR, Liberwirth U, Hagen-Mann K (2002) Linkage disequilibrium mapping identifies a 390 kb region associated with CYP2D6 poor drug metabolising activity. Pharmacogenomics J 2:165CAS 
    Article 

    Google Scholar 
    Huang K, Dunn DW, Ritland K, Li BG (2020) polygene: Population genetics analyses for autopolyploids based on allelic phenotypes. Methods Ecol Evol 11:448–456Article 

    Google Scholar 
    Jorde LB (1995) Linkage disequilibrium as a gene-mapping tool. Am J Hum Genet 56:11CAS 

    Google Scholar 
    Lewontin RC (1964) The interaction of selection and linkage. I. General considerations; heterotic models. Genetics 49:49CAS 
    Article 

    Google Scholar 
    Maruyama T (1982) Stochastic integrals and their application to population genetics. In: Kimura M (ed) Molecular evolution, protein polymorphism and the neutral theory. Japan Scientific Societies Press, Tokyo, p 151–166
    Google Scholar 
    Nei M (1987) Molecular evolutionary genetics. Columbia university press, New YorkOhta T (1980) Linkage disequilibrium between amino acid sites in immunoglobulin genes and other multigene families. Genet Res 36:181–197CAS 
    Article 

    Google Scholar 
    Ohta T, Kimura M (1969) Linkage disequilibrium at steady state determined by random genetic drift and recurrent mutation. Genetics 63:229CAS 
    Article 

    Google Scholar 
    Otto SP (2007) The evolutionary consequences of polyploidy. Cell 131:452–462CAS 
    Article 

    Google Scholar 
    Rieger R, Michaelis A, Green MM (1968) A glossary of genetics and cytogenetics: classical and molecular. Springer-Verlag, New York, NYBook 

    Google Scholar 
    Robert C (1991) Generalized inverse normal distributions. Stat Probabil Lett 11:37–41Article 

    Google Scholar 
    Santiago E, Novo I, Pardiñas AF, Saura M, Wang J, Caballero A (2020) Recent demographic history inferred by high-resolution analysis of linkage disequilibrium. Mol Biol Evol 37:3642–3653CAS 
    Article 

    Google Scholar 
    Sattler MC, Carvalho CR, Clarindo WR (2016) The polyploidy and its key role in plant breeding. Planta 243:281–296CAS 
    Article 

    Google Scholar 
    Slatkin M (2008) Linkage disequilibrium—understanding the evolutionary past and mapping the medical future. Nat Rev Genet 9:477CAS 
    Article 

    Google Scholar 
    Stift M, Berenos C, Kuperus P, van Tienderen PH (2008) Segregation models for disomic, tetrasomic and intermediate inheritance in tetraploids: a general procedure applied to Rorippa (yellow cress) microsatellite data. Genetics 179:2113–2123Article 

    Google Scholar 
    Sved JA (1964) The relationship between diploid and tetraploid recombination frequencies. Heredity 19:585–596CAS 
    Article 

    Google Scholar 
    Sved JA, Cameron EC, Gilchrist AS (2013) Estimating effective population size from linkage disequilibrium between unlinked loci: theory and application to fruit fly outbreak populations. PLoS ONE 8:e69078CAS 
    Article 

    Google Scholar 
    Sved JA, Feldman MW (1973) Correlation and probability methods for one and two loci. Theor Popul Biol 4:129–132CAS 
    Article 

    Google Scholar 
    Tenesa A, Navarro P, Hayes BJ, Duffy DL, Clarke GM, Goddard ME, Visscher PM (2007) Recent human effective population size estimated from linkage disequilibrium. Genome Res 17:520–526CAS 
    Article 

    Google Scholar 
    Udall JA, Wendel JF (2006) Polyploidy and crop improvement. Crop Sci 46:S-3–S-14Article 

    Google Scholar 
    Waples RS (2006) A bias correction for estimates of effective population size based on linkage disequilibrium at unlinked gene loci. Conserv Genet 7:167–184. https://doi.org/10.1007/s10592-005-9100-yArticle 

    Google Scholar 
    Waples RS, Antao T, Luikart G (2014) Effects of overlapping generations on linkage disequilibrium estimates of effective population size. Genetics 197:769–780Article 

    Google Scholar 
    Waples RK, Larson WA, Waples RS (2016) Estimating contemporary effective population size in non-model species using linkage disequilibrium across thousands of loci. Heredity 117:233–240. https://doi.org/10.1038/hdy.2016.60CAS 
    Article 

    Google Scholar 
    Weir BS (1979) Inferences about linkage disequilibrium. Biometrics 35:235–254Weir BS, Cockerham CC (1979) Estimation of linkage disequilibrium in randomly mating populations. Heredity 42:105Article 

    Google Scholar 
    Weir BS, Hill WG (1980) Effect of mating structure on variation in linkage disequilibrium. Genetics 95:477–488CAS 
    Article 

    Google Scholar  More