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    Variable effects of vegetation characteristics on a recreation service depending on natural and social environment

    Study areaWe focused on hiking activity in the four main islands of Japan (Honshu, Hokkaido, Kyushu, and Shikoku) and nearby small islands connected to the main islands by a bridge (Fig. 1a). These islands lie between latitudes 31.0° and 45.5°N, and the total area is 361,000 km2. The islands are generally mountainous and tallest mountains in central Honshu exceed 3000 m a.s.l. (Fig. 1c). In Tokyo, mean monthly temperatures range between 5.2 °C in January and 26.4 °C in August, while they range between − 18.4 °C in January and 6.2 °C in August at the summit of the highest mountain, Mt. Fuji (3776 m a.s.l., Japan Meteorological Agency). In northern Honshu and Hokkaido, snow depth can exceed 1 m even at low elevations and high mountains are covered with snow even in southern Japan.Vegetation excluding farmland and pasture covers 70.9% of the study area and the 93.9% is forest. Plantations of mostly evergreen conifers such as Japanese cedar (Cryptomeria japonica) occupy 37.6% of the vegetation area (National Surveys on the Natural Environment by the Biodiversity Center of Japan 2nd–7th; http://www.biodic.go.jp/trialSystem/top_en.html). Secondary vegetation after past human disturbances occupies 39.4% of the total vegetation and the remaining 23.0% is primary vegetation. The typical primary vegetation types are, from north to south, boreal mixed forest, deciduous broad leaved forest, and evergreen broad leaved forest.Grid squaresRecords of hiking activity were summarized for 4244 secondary grid squares based on Standard Grid Square System, which was defined by the Minister’s Order of Administrative Management Agency in 1973. In the system, the secondary grid was defined as a grid of 5′ in latitude and 7′ 30″ in longitude, which roughly corresponds to a 10 km grid in the study area. This is the standard grid system of the government and we adopted the system for convenience in future application uses and communication with practitioners. The grids, which are defined by latitude and longitude, are different in the area up to 22% between the north and south ends. Therefore, area of each grid was included in a model as an offset term.Hiking activityAccording to a government survey in 2016, (the Survey on Time Use and Leisure Activities by the Statistics Bureau of Japan, http://www.stat.go.jp/english/data/shakai/index.htm), 10.0% (about 10.7 million people) of Japan’s population age 15 or over enjoyed hiking/mountaineering in the last year. The census showed also that hiking is more popular among urban residents in the metropolitan areas. Both multi-day expedition to high mountains and day trek to low mountains in suburban areas are popular. Because of the severe winter climate, unskilled hikers use the high mountains in summer and early autumn only. During a summer vacation, whose peak time in Japan is August, many hikers enjoy multi-day trips to distant mountains. Spring and autumn are also popular seasons because of the mild weather and the scenic beauty of the fresh green or autumn colors.Data collectionIn this study, we used number of hiking records accumulated on the most popular social networking service for hikers in Japan (Yamareco; https://www.yamareco.com) as a surrogate for flow of recreation service. For all the registered destinations in the study area, the number of hiking records for each month and the latitude and longitude of the destination were collected from the service in September 2016 with the rvest28 package in R software29. This service launched in October 2005 hosts records of the hiking route, photos, participants, and impressions of a hiking trip and facilitates communication among users. Although monthly number of records for each destination is always available on the site, the exact date of each hiking record is not always public information for privacy reasons; therefore, all of the records from the almost 11 years since the start of the service were lumped together in our analysis. Hikers may record multiple places in a single trip, so the total number of records must be larger than the number of unique trips. Users of the service sometime record a place that is not a destination, e.g. start points and stations of trails, parking areas, stations of transports, and bus stops. Such records were excluded before analyses as far as it can be judged from the name of the place. As a result, the total number of hiking records was 4,708,229 records for 16,179 destinations. Finally, these records were assigned to the 4244 grids based on the latitude and longitude of each destination and then total number of records for each grid was used as a surrogate of the recreation service flow in our analysis. Not only total number but also monthly number was used in our analysis to examine seasonal changes in associations between the service and vegetation. Total record number of the grids was strongly right-skewed; no record (handled as 0 in our analysis) was found in 2036 grids while mean and maximum record number were 1109 and 350,384, respectively.Explanation variablesFifty ecological, environmental, and social/infrastructural variables (Table S1) were prepared for each grid by using ArcGIS version 10.5 (ESRI, Redlands, CA, USA). For vegetation and land-use attributes, National Surveys on the Natural Environment by the Biodiversity Center of Japan (2nd–7th; http://www.biodic.go.jp/trialSystem/top_en.html) and National Land Numerical Information (http://nlftp.mlit.go.jp/ksj-e/index.html) were used. The proportion of sea, that of total vegetation cover (excluding agricultural land and pasture) to land area, that of agricultural land (including pasture) to land area, that of natural vegetation (vegetation excluding plantations) to total vegetated area, and that of primary vegetation (vegetation with no record or evidence of a disturbance) to natural vegetation were summarized at four spatial scales: a radius of 10 km, 20 km, 50 km, and 100 km from the center of each grid. Spatial patterns of the three vegetation variables in 10 km radius were summarized in Fig. 1d–f.Maximum elevation, minimum elevation, and ruggedness (index of topographic heterogeneity30) were summarized at the four spatial scales based on a digital elevation model (10-m resolution) provided by the Geospatial Information Authority of Japan (https://fgd.gsi.go.jp/download/menu.php). For climatic variables (annual and monthly mean temperature, annual and monthly precipitation, annual and monthly hours of sunshine, and annual maximum snow depth), the National Land Numeric Information provided by the Ministry of Land, Infrastructure, Transport and Tourism of Japan (http://nlftp.mlit.go.jp/ksj-e/index.html) was referenced. Densities of population and roads at the four spatial scales were prepared from population census data from the Statistics Bureau of Japan (http://e-stat.go.jp/SG2/eStatGIS/page/download.html) and the National Land Numeric Information. For calculation of these densities, the sea surface was excluded. In addition, latitude and longitude of center of each grid were also used as explanatory variables to average effects of spatial coordinates.Statistical analysisIn this study, we employed BRT, a machine-learning method based on regression trees31 for modeling the complex relationship between a CES flow and landscape attributes12. BRT is an ensemble learning method where multiple regression trees are sequentially combined to minimize the loss function by means of gradient descent. This technique has advantage in the development of a model with a high predictive performance, in which high-dimensional interactions among explanatory variables and nonlinear responses are fully accounted for. In ecology, BRT has been frequently used for modeling of a species distribution32.Total and monthly numbers of hiking records were modeled as a function of the 50 variables described above under the assumption of a Poisson response. For temperature, precipitation, and hours of sunshine, annual and monthly average were used for the analysis of total and monthly records, respectively. In modeling by BRT, parameters for building of each learner and assembly of the learners must be carefully chosen to maximize generalization ability of a model31. In our case, candidate parameters were 2, 5, and 10 for the maximum depth of variable interactions for each learner; 2, 5, 10, and 20 for the minimum number of observations in the terminal nodes for each learner; 0.5 and 0.75 for the proportion of training data used for building each learner; and 1000, 2000, 4000, 6000, 8000 and 10,000 for the total number of learners (Table S2). In the model assembling process, the value of 0.01 was used as a shrinkage parameter. Ten-fold cross validation was used to obtain the best suites of parameters. R2 based on sum of squares:$${R}^{2}=1-frac{{sum ({y}_{i}-widehat{{y}_{i}})}^{2}}{{sum ({y}_{i}-overline{{y }_{i}})}^{2}}$$
    was used for evaluation of the model’s prediction performance. The importance of explanatory variables was evaluated as an increase of mean absolute error after 100-times permutation of a variable33.Effects of each explanatory variable (a landscape attribute) on the response variable (record number) and the context dependence were visually inspected by individual conditional expectation (ICE) plot34. ICE plot visualizes the effect of a given explanatory variable for each observation by connecting outcome of a model for shifting values of the focal explanatory variable throughout the range while keeping other explanatory variables as the original value. Predictions were performed in log-scale and each line was centered to be zero at the left end of the x-axis to show relative effects of explanatory variables (c-ICE plot sensu Goltstein et al.34). Each line in ICE plot can be colored based on value of the second explanatory variable to assist assessment of the interactive effects of the two predictors. Friedman’s H statistic35 was used to detect explanatory variables whose interaction with the vegetation variables are important and therefore should be used for color-coding of an ICE plot. Friedman’s H is defined as a proportion of variance of partial dependence estimates explained by interactive effects for arbitrary suites of explanatory variables.Then, expected impacts of 0.1 decrease in the three local vegetation variables were assessed by the trained model and mapped. Although vegetation variables were sometimes more important at larger spatial scales (see “Results”), we focused on vegetation at a local (10 km radius) scale because most changes in vegetation occur at the scale in Japan (National Surveys on the Natural Environment by the Biodiversity Center of Japan, https://www.biodic.go.jp/kiso/fnd_list_h.html).All statistical analyses were performed using the R software packag29. The gbm36 package was used for BRT, the iml37 package was used for calculation of Friedman’s H statistic, and the cv.models (Oguro, https://github.com/Marchen/cv.models) packages was used for cross validation and parameter tuning. More

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    Gene loss and symbiont switching during adaptation to the deep sea in a globally distributed symbiosis

    Cavanaugh CM, McKiness ZP, Newton ILG, Stewart FJ. Marine chemosynthetic symbioses. Prokaryotes. 2006;1:475–507.Article 

    Google Scholar 
    Beinart RA, Luo C, Konstantinidis KT, Stewart FJ, Girguis PR. The bacterial symbionts of closely related hydrothermal vent snails with distinct geochemical habitats show broad similarity in chemoautotrophic gene content. Front Microbiol. 2019;10:1818.Article 

    Google Scholar 
    Robidart JC, Bench SR, Feldman RA, Novoradovsky A, Podell SB, Gaasterland T, et al. Metabolic versatility of the Riftia pachyptila endosymbiont revealed through metagenomics. Environ Microbiol. 2008;10:727–37.Article 
    CAS 

    Google Scholar 
    Ponnudurai R, Sayavedra L, Kleiner M, Heiden SE, Thürmer A, Felbeck H, et al. Genome sequence of the sulfur-oxidizing Bathymodiolus thermophilus gill endosymbiont. Stand Genom Sci. 2017;12:50.Article 

    Google Scholar 
    Duperron S, Bergin C, Zielinski F, Blazejak A, Pernthaler A, McKiness ZP, et al. A dual symbiosis shared by two mussel species, Bathymodiolus azoricus and Bathymodiolus puteoserpentis (Bivalvia: Mytilidae), from hydrothermal vents along the northern Mid-Atlantic Ridge. Environ Microbiol. 2006;8:1441–7.Article 
    CAS 

    Google Scholar 
    Dubilier N, Bergin C, Lott C. Symbiotic diversity in marine animals: the art of harnessing chemosynthesis. Nat Rev Microbiol. 2008;6:725–40.Article 
    CAS 

    Google Scholar 
    Sogin EM, Leisch N, Dubilier N. Chemosynthetic symbioses. Curr Biol. 2020;30:R1137–R1142.Article 
    CAS 

    Google Scholar 
    Roeselers G, Newton ILG. On the evolutionary ecology of symbioses between chemosynthetic bacteria and bivalves. Appl Microbiol Biotechnol. 2012;94:1–10.Article 
    CAS 

    Google Scholar 
    Moran NA. Symbiosis as an adaptive process and source of phenotypic complexity. Proc Natl Acad Sci USA. 2007;104 Suppl 1:8627–33.Article 
    CAS 

    Google Scholar 
    McMullen JG, Peterson BF, Forst S, Blair HG, Patricia Stock S. Fitness costs of symbiont switching using entomopathogenic nematodes as a model. BMC Evol Biol. 2017;17. https://doi.org/10.1186/s12862-017-0939-6.Taylor JD, Glover E. Biology, evolution and generic review of the chemosymbiotic bivalve family Lucinidae. London, UK: Ray Society; 2021.Osvatic JT, Wilkins LGE, Leibrecht L, Leray M, Zauner S, Polzin J, et al. Global biogeography of chemosynthetic symbionts reveals both localized and globally distributed symbiont groups. Proc Natl Acad Sci USA. 2021;118. https://doi.org/10.1073/pnas.2104378118.Petersen JM, Kemper A, Gruber-Vodicka H, Cardini U, van der Geest M, Kleiner M, et al. Chemosynthetic symbionts of marine invertebrate animals are capable of nitrogen fixation. Nat Microbiol. 2016;2:16195.Article 
    CAS 

    Google Scholar 
    Lim SJ, Davis B, Gill D, Swetenburg J, Anderson LC, Engel AS, et al. Gill microbiome structure and function in the chemosymbiotic coastal lucinid Stewartia floridana. FEMS Microbiol Ecol. 2021;97. https://doi.org/10.1093/femsec/fiab042.Lim SJ, Davis BG, Gill DE, Walton J, Nachman E, Engel AS, et al. Taxonomic and functional heterogeneity of the gill microbiome in a symbiotic coastal mangrove lucinid species. ISME J. 2019;13:902–20.Article 
    CAS 

    Google Scholar 
    Gros O, Liberge M, Felbeck H. Interspecific infection of aposymbiotic juveniles of Codakia orbicularis by various tropical lucinid gill-endosymbionts. Mar Biol. 2003;142:57–66.Article 

    Google Scholar 
    Gros O, Elisabeth NH, Gustave SDD, Caro A, Dubilier N. Plasticity of symbiont acquisition throughout the life cycle of the shallow-water tropical lucinid Codakia orbiculata (Mollusca: Bivalvia). Environ Microbiol. 2012;14:1584–95.Article 
    CAS 

    Google Scholar 
    Gros O, Frenkiel L, Mouëza M. Embryonic, larval, and post-larval development in the symbiotic clam Codakia orbicularis (Bivalvia: Lucinidae). Invertebr Biol. 1997;116:86–101.Article 

    Google Scholar 
    König S, Gros O, Heiden SE, Hinzke T, Thürmer A, Poehlein A, et al. Nitrogen fixation in a chemoautotrophic lucinid symbiosis. Nat Microbiol. 2016;2:16193.Article 

    Google Scholar 
    Fiore CL, Jarett JK, Olson ND, Lesser MP. Nitrogen fixation and nitrogen transformations in marine symbioses. Trends Microbiol. 2010;18:455–63.Article 
    CAS 

    Google Scholar 
    Cardini U, Bednarz VN, Foster RA, Wild C. Benthic N2 fixation in coral reefs and the potential effects of human-induced environmental change. Ecol Evol. 2014;4:1706–27.Article 

    Google Scholar 
    Glover EA, Taylor JD. Lucinidae of the Philippines: highest known diversity and ubiquity of chemosymbiotic bivalves from intertidal to bathyal depths (Mollusca: Bivalvia). mém Mus Natl Hist Nat. 2016;208:65–234.
    Google Scholar 
    Taylor JD, Glover EA, Williams ST. Diversification of chemosymbiotic bivalves: origins and relationships of deeper water Lucinidae. Biol J Linn Soc Lond. 2014;111:401–20.Article 

    Google Scholar 
    von Cosel R. Taxonomy of tropical West African bivalves. VI. Remarks on Lucinidae (Mollusca, Bivalvia), with description of six new genera and eight new species. Zoosystema. 2006;28:805.
    Google Scholar 
    Glover EA, Taylor JD, Rowden AA. Bathyaustriella thionipta, a new lucinid bivalve from a hydrothermal vent on the Kermadec Ridge, New Zealand and its relationship to shallow-water taxa (Bivalvia: Lucinidae). J Mollusca Stud. 2004;70:283–95.Article 

    Google Scholar 
    Paulus E Shedding light on deep-sea biodiversity—a highly vulnerable habitat in the face of anthropogenic change. Front Mar Sci. 2021;8. https://doi.org/10.3389/fmars.2021.667048.Brown A, Thatje S. Explaining bathymetric diversity patterns in marine benthic invertebrates and demersal fishes: physiological contributions to adaptation of life at depth. Biol Rev Camb Philos Soc. 2014;89:406–26.Article 

    Google Scholar 
    Smith CR, De Leo FC, Bernardino AF, Sweetman AK, Arbizu PM. Abyssal food limitation, ecosystem structure and climate change. Trends Ecol Evol. 2008;23:518–28.Article 

    Google Scholar 
    Gage JD, Tyler PA. Deep-sea biology: a natural history of organisms at the deep-sea floor. Cambridge, UK: Cambridge University Press; 1991.Iken K, Brey T, Wand U, Voigt J, Junghans P. Food web structure of the benthic community at the Porcupine Abyssal Plain (NE Atlantic): a stable isotope analysis. Prog Oceanogr. 2001;50:383–405.Article 

    Google Scholar 
    von Cosel R, Bouchet P. Tropical deep-water lucinids (Mollusca: Bivalvia) from the Indo-Pacific: essentially unknown, but diverse and occasionally gigantic. mém Mus Natl Hist Nat. 2008;196:115–213.
    Google Scholar 
    Stearns REC Scientific results of explorations by the US Fish Commission steamer Albatross. No. XVII. Descriptions of new West American land, fresh-water, and marine shells, with notes and comments. Proceedings of the United States National Museum. 1890. https://repository.si.edu/bitstream/handle/10088/13174/1/USNMP-13_813_1890.pdf.Taylor JD, Glover EA. The lucinid bivalve genus Cardiolucina (Mollusca, Bivalvia, Lucinidae): systematics, anatomy and relationships. Bull Br Mus Nat Hist Zoo. 1997;63:93–122.
    Google Scholar 
    Coan EV, Valentich-Scott P, Sadeghian PS. Bivalve seashells of tropical West America: marine bivalve mollusks from Baja California to Northern Peru. Santa Barbara, USA: Museum of Natural History; 2012.von Cosel R, Gofas S. Marine bivalves of tropical West Africa: from Rio de Oro to southern Angola. Marseille, France: Muséum national d’Histoire naturelle, Paris; 2019. p 1104.Atkinson L, Sink K. Field guide to the offshore marine invertebrates of South Africa. 2018. https://doi.org/10.15493/SAEON.PUB.10000001.Montagu G. Testacea Britannica, or natural history of British shells. London, UK: JS Hollis; 1803.Taylor J, Glover E. New lucinid bivalves from shallow and deeper water of the Indian and West Pacific Oceans (Mollusca, Bivalvia, Lucinidae). ZooKeys. 2013;326:69–90.Article 

    Google Scholar 
    Apprill A, McNally S, Parsons R, Weber L. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat Micro Ecol. 2015;75:129–37.Article 

    Google Scholar 
    Parada AE, Needham DM, Fuhrman JA. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ Microbiol. 2016;18:1403–14.Article 
    CAS 

    Google Scholar 
    Pjevac P, Hausmann B, Schwarz J, Kohl G, Herbold CW, Loy A, et al. An economical and flexible dual barcoding, two-step PCR approach for highly multiplexed amplicon sequencing. Front Microbiol. 2021;12:669776.Article 

    Google Scholar 
    McLaren MR, Callahan BJ. Silva 138.1 prokaryotic SSU taxonomic training data formatted for DADA2 [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4587955.Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.Article 
    CAS 

    Google Scholar 
    Andersen KS, Kirkegaard RH, Karst SM, Albertsen M. ampvis2: an R package to analyse and visualise 16S rRNA amplicon data. 2018. https://www.biorxiv.org/content/10.1101/299537v1.Bushnell B. BBMap: a fast, accurate, splice-aware aligner. Berkeley, CA, USA: Lawrence Berkeley National Lab. (LBNL); 2014.Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017;27:824–34.Article 
    CAS 

    Google Scholar 
    Nurk S, Bankevich A, Antipov D, Gurevich A, Korobeynikov A, Lapidus A, et al. Assembling genomes and mini-metagenomes from highly chimeric reads. In: Research in Computational Molecular Biology. Springer Berlin Heidelberg; 2013. p. 158–70.Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25:2078–9.Article 

    Google Scholar 
    Eren AM, Esen ÖC, Quince C, Vineis JH, Morrison HG, Sogin ML, et al. Anvi’o: an advanced analysis and visualization platform for ‘omics data. PeerJ. 2015;3:e1319.Article 

    Google Scholar 
    Alneberg J, Bjarnason BS, de Bruijn I, Schirmer M, Quick J, Ijaz UZ, et al. Binning metagenomic contigs by coverage and composition. Nat Methods. 2014;11:1144–6.Article 
    CAS 

    Google Scholar 
    Kang DD, Li F, Kirton E, Thomas A, Egan R, An H, et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ. 2019;7:e7359.Article 

    Google Scholar 
    Olm MR, Brown CT, Brooks B, Banfield JF. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 2017;11:2864–8.Article 
    CAS 

    Google Scholar 
    Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.Article 
    CAS 

    Google Scholar 
    Chaumeil P-A, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics. 2019. https://doi.org/10.1093/bioinformatics/btz848.Parks DH, Chuvochina M, Chaumeil P-A, Rinke C, Mussig AJ, Hugenholtz P. A complete domain-to-species taxonomy for Bacteria and Archaea. Nat Biotechnol. 2020;38:1079–86.Article 
    CAS 

    Google Scholar 
    Parks DH, Chuvochina M, Waite DW, Rinke C, Skarshewski A, Chaumeil P-A, et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol. 2018;36:996–1004.Article 
    CAS 

    Google Scholar 
    Matsen FA, Kodner RB, Armbrust EV. pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinform. 2010;11:538.Article 

    Google Scholar 
    Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun. 2018;9:5114.Article 

    Google Scholar 
    Hyatt D, Chen G-L, Locascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 2010;11:119.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 
    Eddy SR. Accelerated profile HMM searches. PLoS Comput Biol. 2011;7:e1002195.Article 
    CAS 

    Google Scholar 
    Ondov BD, Treangen TJ, Melsted P, Mallonee AB, Bergman NH, Koren S, et al. Mash: fast genome and metagenome distance estimation using MinHash. Genome Biol. 2016;17:132.Article 

    Google Scholar 
    Trifinopoulos J, Nguyen L-T, von Haeseler A, Minh BQ. W-IQ-TREE: a fast online phylogenetic tool for maximum likelihood analysis. Nucleic Acids Res. 2016;44:W232–5.Article 
    CAS 

    Google Scholar 
    Letunic I, Bork P. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 2021;49:W293–W296.Article 
    CAS 

    Google Scholar 
    Varghese NJ, Mukherjee S, Ivanova N, Konstantinidis KT, Mavrommatis K, Kyrpides NC, et al. Microbial species delineation using whole genome sequences. Nucleic Acids Res. 2015;43:6761–71.Article 
    CAS 

    Google Scholar 
    Qin Q-L, Xie B-B, Zhang X-Y, Chen X-L, Zhou B-C, Zhou J, et al. A proposed genus boundary for the prokaryotes based on genomic insights. J Bacteriol. 2014;196:2210–5.Article 

    Google Scholar 
    Huerta-Cepas J, Szklarczyk D, Heller D, Hernández-Plaza A, Forslund SK, Cook H, et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 2019;47:D309–D314.Article 
    CAS 

    Google Scholar 
    Huerta-Cepas J, Forslund K, Coelho LP, Szklarczyk D, Jensen LJ, von Mering C, et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-Mapper. Mol Biol Evol. 2017;34:2115–22.Article 
    CAS 

    Google Scholar 
    Brettin T, Davis JJ, Disz T, Edwards RA, Gerdes S, Olsen GJ, et al. RASTtk: a modular and extensible implementation of the RAST algorithm for building custom annotation pipelines and annotating batches of genomes. Sci Rep. 2015;5:8365.Article 

    Google Scholar 
    Mahram A, Herbordt MC. NCBI BLASTP on high-performance reconfigurable computing systems. ACM Trans Reconfigurable Technol Syst. 2015;7:1–20.Article 

    Google Scholar 
    Yang Z. PAML: a program package for phylogenetic analysis by maximum likelihood. Comput Appl Biosci. 1997;13:555–6.CAS 

    Google Scholar 
    Osvatic J, Wilkins L. Strength of selection scripts. FigShare. 2022;8. https://doi.org/10.6084/m9.figshare.20626746.v1.Amann RI, Binder BJ, Olson RJ, Chisholm SW, Devereux R, Stahl DA. Combination of 16S rRNA-targeted oligonucleotide probes with flow cytometry for analyzing mixed microbial populations. Appl Environ Microbiol. 1990;56:1919–25.Article 
    CAS 

    Google Scholar 
    Lan Y, Sun J, Chen C, Sun Y, Zhou Y, Yang Y, et al. Hologenome analysis reveals dual symbiosis in the deep-sea hydrothermal vent snail Gigantopelta aegis. Nat Commun. 2021;12:1165.Article 
    CAS 

    Google Scholar 
    Leray M, Wilkins LGE, Apprill A, Bik HM, Clever F, Connolly SR, et al. Natural experiments and long-term monitoring are critical to understand and predict marine host-microbe ecology and evolution. PLoS Biol. 2021;19:e3001322.Article 
    CAS 

    Google Scholar 
    Petersen Jillian M, Yuen B, Alexandre G. The symbiotic ‘all-rounders’: partnerships between marine animals and chemosynthetic nitrogen-fixing bacteria. Appl Environ Microbiol 2020;87:e02129–20.Johnson KS, Childress JJ, Hessler RR, Sakamoto-Arnold CM, Beehler CL. Chemical and biological interactions in the Rose Garden hydrothermal vent field, Galapagos spreading center. Deep Sea Res A. 1988;35:1723–44.Article 

    Google Scholar 
    Kennicutt ME II, Brooks JM, Burke RA Jr. Hydrocarbon seepage, gas hydrates, and authigenic carbonate in the northwestern Gulf of Mexico. Offshore Technology Conference; 1989. https://doi.org/10.4043/5952-ms.Lilley MD, Butterfield DA, Olson EJ, Lupton JE, Macko SA, McDuff RE. Anomalous CH4 and NH4+ concentrations at an unsedimented mid-ocean-ridge hydrothermal system. Nature. 1993;364:45–47.Article 
    CAS 

    Google Scholar 
    Von Damm KL, Edmond JM, Measures CI, Grant B. Chemistry of submarine hydrothermal solutions at Guaymas Basin, Gulf of California. Geochim Cosmochim Acta. 1985;49:2221–37.Article 

    Google Scholar 
    Lee RW, Thuesen EV, Childress JJ. Ammonium and free amino acids as nitrogen sources for the chemoautotrophic symbiosis Solemya reidi Bernard (Bivalvia: Protobranchia). J Exp Mar Bio Ecol. 1992;158:75–91.Article 
    CAS 

    Google Scholar 
    Sanders JG, Beinart RA, Stewart FJ, Delong EF, Girguis PR. Metatranscriptomics reveal differences in in situ energy and nitrogen metabolism among hydrothermal vent snail symbionts. ISME J. 2013;7:1556–67.Article 
    CAS 

    Google Scholar 
    Touchette BW, Burkholder JM. Review of nitrogen and phosphorus metabolism in seagrasses. J Exp Mar Bio Ecol. 2000;250:133–67.Article 
    CAS 

    Google Scholar 
    Fourqurean JW, Zieman JC, Powell GVN. Relationships between porewater nutrients and seagrasses in a subtropical carbonate environment. Mar Biol. 1992;114:57–65.Article 
    CAS 

    Google Scholar 
    Williams SL. Experimental studies of Caribbean seagrass bed development. Ecol Monogr. 1990;60:449–69.Article 

    Google Scholar 
    Herbert RA. Nitrogen cycling in coastal marine ecosystems. FEMS Microbiol Rev. 1999;23:563–90.Article 
    CAS 

    Google Scholar 
    Risgaard-Petersen N, Dalsgaard T, Rysgaard S, Christensen PB, Borum J, McGlathery K, et al. Nitrogen balance of a temperate eelgrass Zostera marina bed. Mar Ecol Prog Ser. 1998;174:281–91.Article 
    CAS 

    Google Scholar 
    Bristow LA, Dalsgaard T, Tiano L, Mills DB, Bertagnolli AD, Wright JJ, et al. Ammonium and nitrite oxidation at nanomolar oxygen concentrations in oxygen minimum zone waters. Proc Natl Acad Sci USA. 2016;113:10601–6.Article 
    CAS 

    Google Scholar 
    Karthäuser C, Ahmerkamp S, Marchant HK, Bristow LA, Hauss H, Iversen MH, et al. Small sinking particles control anammox rates in the Peruvian oxygen minimum zone. Nat Commun. 2021;12:3235.Article 

    Google Scholar 
    Kuypers MMM, Lavik G, Woebken D, Schmid M, Fuchs BM, Amann R, et al. Massive nitrogen loss from the Benguela upwelling system through anaerobic ammonium oxidation. Proc Natl Acad Sci USA. 2005;102:6478–83.Article 
    CAS 

    Google Scholar 
    Johnson KS, Beehler CL, Sakamoto-Arnold CM, Childress JJ. In situ measurements of chemical distributions in a deep-sea hydrothermal vent field. Science. 1986;231:1139–41.Article 
    CAS 

    Google Scholar 
    Childress JJ, Girguis PR. The metabolic demands of endosymbiotic chemoautotrophic metabolism on host physiological capacities. J Exp Biol. 2011;214:312–25.Article 
    CAS 

    Google Scholar 
    Hentschel U, Hand S, Felbeck H. The contribution of nitrate respiration to the energy budget of the symbiont-containing clam Lucinoma aequizonata: a calorimetric study. J Exp Biol. 1996;199:427–33.Article 
    CAS 

    Google Scholar 
    Breusing C, Mitchell J, Delaney J, Sylva SP, Seewald JS, Girguis PR, et al. Physiological dynamics of chemosynthetic symbionts in hydrothermal vent snails. ISME J. 2020;14:2568–79.Article 
    CAS 

    Google Scholar 
    Amorim K, Loick-Wilde N, Yuen B, Osvatic JT, Wäge-Recchioni J, Hausmann B, et al. Chemoautotrophy, symbiosis and sedimented diatoms support high biomass of benthic molluscs in the Namibian shelf. Sci Rep. 2022;12:9731.Article 
    CAS 

    Google Scholar 
    Breusing C, Johnson SB, Tunnicliffe V, Clague DA, Vrijenhoek RC, Beinart RA. Allopatric and sympatric drivers of speciation in Alviniconcha hydrothermal vent snails. Mol Biol Evol. 2020;37:3469–84.Article 
    CAS 

    Google Scholar 
    Giovannoni SJ, Cameron Thrash J, Temperton B. Implications of streamlining theory for microbial ecology. ISME J. 2014;8:1553–65.Article 

    Google Scholar 
    Brissac T, Gros O, Merçot H. Lack of endosymbiont release by two Lucinidae (Bivalvia) of the genus Codakia: consequences for symbiotic relationships. FEMS Microbiol Ecol. 2009;67:261–7.Article 
    CAS 

    Google Scholar 
    Werner GDA, Cornelissen JHC, Cornwell WK, Soudzilovskaia NA, Kattge J, West SA, et al. Symbiont switching and alternative resource acquisition strategies drive mutualism breakdown. Proc Natl Acad Sci USA. 2018;115:5229–34.Article 
    CAS 

    Google Scholar 
    Sudakaran S, Kost C, Kaltenpoth M. Symbiont acquisition and replacement as a source of ecological innovation. Trends Microbiol. 2017;25:375–90.Article 
    CAS 

    Google Scholar 
    Li Y, Liles MR, Halanych KM. Endosymbiont genomes yield clues of tubeworm success. ISME J. 2018;12:2785–95.Article 
    CAS 

    Google Scholar 
    Moran NA, Yun Y. Experimental replacement of an obligate insect symbiont. Proc Natl Acad Sci USA. 2015;112:2093–6.Article 
    CAS 

    Google Scholar 
    Sørensen MES, Wood AJ, Cameron DD, Brockhurst MA. Rapid compensatory evolution can rescue low fitness symbioses following partner switching. Curr Biol. 2021;31:3721–3728.e4.Article 

    Google Scholar 
    Taylor JD, Glover EA, Smith L, Ikebe C, Williams ST. New molecular phylogeny of Lucinidae: increased taxon base with focus on tropical Western Atlantic species (Mollusca: Bivalvia). Zootaxa. 2016;4196:zootaxa.4196.3.2.Article 

    Google Scholar 
    Osvatic J. Fig1 gtdb tree and alignment. figshare. 2021. https://doi.org/10.6084/m9.figshare.16837216.v1.Osvatic J. Figure 2: GTDB alignment and phylogeny. 2021. https://doi.org/10.6084/m9.figshare.16837237. More

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    A comparative study of fifteen cover crop species for orchard soil management: water uptake, root density traits and soil aggregate stability

    Evapotranspiration measurements and above-ground biomassFigure 1 shows daily evapotranspiration (ET, mm day−1) of each CC tested before mowing (DOY, day of the year, 184) and at 2, 8, 17 and 25 days after mowing (DOY 190, 196, 205 and 213); bare soil was also included as a reference. Before mowing, ET rates showed significant differences between and within the three groups. CR plants had a mean ET of 8.1 mm day−1, which was lower, compared to the other two groups (10.6 and 18.6 mm day−1 for GR and LE, respectively) and the bare soil control (8.5 mm day−1). On DOY 184, values as high as 9.4 (Glechoma hederacea L., GH) and 9.8 mm day−1 (Trifolium subterraneum L. cv. Denmark, TS) were found (Fig. 1), while ranging around 7 mm day-1, Dichondra repens J.R.Forst. & G.Forst. (DR), Hieracium pilosella L. (HP), and Sagina subulata (Swartz) C. Presl (SS) ET were lower than soil evaporation itself.Figure 1Vertical bars represent the daily water use as referred to unit of soil (ET, mm day−1) for the bare soil (yellow) and all the cover crop species as divided into creeping plants (shades of blue), legumes (shades of green) and grasses (shades of orange). Evapotranspiration was measured though a gravimetric method before (i.e. − 4) and at 2, 8, 17 and 25 days after mowing. ET data are mean values ± SE (n = 4).Full size imageOn the same day, a large ET variation was recorded within the GR group as Festuca arundinacea Schreb. cv. Thor (FA) scored the highest daily ET values (13.4 mm day−1), whereas in Festuca ovina L. cv. Ridu (FO), water loss was reduced by 45% (7.5 mm day−1). Within the 15 CCs, LE registered the highest pre-mowing ET with Trifolium michelianum Savi cv. Bolta (TM) peaking at 22.6 mm day−1. However, within LE, Medicago polymorpha L. cv. Scimitar (MP) showed ET values as low as 12.1 mm day−1 (Fig. 1).Two days after mowing, all tested CCs recorded ET values lower than 9 mm day−1 (Fig. 1). Moreover, water use reduction among LE ranged between 56% (M. polymorpha, MP) and 73% (T. michelianum, TM), such that T. michelianum (TM, 6.1 mm day−1), Medicago truncatula Gaertn. cv. Paraggio (MT, 5.6 mm day−1) and M. polymorpha (MP, 5.2 mm day−1) registered ET values lower than the bare soil (7.0 mm day−1). Even though registering a consistent ET reduction after mowing, GR retained ET rates slightly higher than bare soil, except for F. ovina (FO), which recorded the lowest at 6.3 mm day−1. Subsequent samplings showed that most of the CCs had a progressive recovery in water use (Fig. 1) and data taken 17 days after mowing confirmed that Lotus corniculatus L. cv. Leo (LC) and all GR fetched pre-mowing ET rates. Medicago lupulina L. cv. Virgo (ML) registered a partial recovery with similar rates (about 13 mm day−1) at 17 and 25 days after the mowing event. F. ovina and all remaining LE stayed below 10 mm day−1 with ET values close to the control until the end of the trial. At 17 days from grass cutting, under a quite high exceeding-the-pot biomass, both G. hederacea (GH) and T. subterraneum (TS) reached ET values as high as 12.0 and 11.4 mm day−1, respectively. On the other hand, D. repens (DR), H. pilosella (HP), and S. subulata (SS) even though with slightly higher ET values than those registered at the beginning of the trial (DOY 184), remained close to the soil evaporation rates until DOY 213.Aboveground dry clipped biomass at the first mowing date (ADW_MW1, DOY 188) showed large differences among groups, as represented in Table 1. ADW_MW1 within LE was quite variable, as values ranged between 274.3 g m−2 (M. polymorpha, MP) and 750.0 g m−2 (T. michelianum, TM). With a mean value of 565.9 g m−2, LE aboveground biomass was 80% higher than the mean GR ADW_MW1 (110.2 g m-2). F. ovina (FO) scored the lowest value at 48.4 g m−2 among grasses, while within the creeping group, G. hederacea (GH) and T. subterraneum (TS) had biomass development outside the pot edges totalling 89.6 g m−2 and 23.2 g m−2, respectively.Table 1 Aboveground dry biomass clipped at the first mowing event (ADW _MW1), the corresponding leaf area surface index (LAI) and water use per leaf area unit (ETLEAF) of all cover crops tested.Full size tableLeaf area index (LAI, m2 m−2) at mowing showed the highest values in LE with LAI peaking at 12.4 (Table 1). Among GR, LAI did not show significant differences, being around 1.2. Concerning CR, LAI was assessed at 0.2 and 0.8 for T. subterraneum (TS) and G. hederacea (GH) respectively, while LAI estimated through photo analysis ranged between 1.3 (D. repens, DR) and 3.6 (T. subterraneum TS).Evapotranspiration per leaf area unit (ETLEAF) was notably higher in GR, ranging between 7.75 (F. ovina, FO) and 9.22 (Lolium perenne L. cv. Playfast, LP) mm m−2 day−1 (Table 1). In descending order, ETLEAF was the highest in D. repens (DR, 5.46 mm m−2 day−1). Similar ETLEAF was found when comparing some LE and CR species such as M. truncatula (MT, 3.40 mm m−2 day−1), M. lupulina (ML, 4.05 mm m−2 day−1), G. hederacea (GH, 3.68 mm m−2 day−1), H. pilosella (HP, 3.86 mm m-2 day-1) and T. subterraneum (TS, 2.74 mm m−2 day−1). T. michelianum (TM), with 1.81 mm m-2 day-1 scored the lowest ETLEAF of all species (Table 1).Plotting LAI versus the before-mowing ET yielded a significant quadratic relationship (R2  > 0.76) (Fig. 2a) which helped to distinguish two different data clouds. Till LAI values of about 6, the model was linear, having at its lower end all GR and CR species with the inclusion of M. polymorpha (MP) as a legume, while, at the other end, M. truncatula (MT), L. corniculatus (LC) and M. lupulina (ML) were grouped together. T. michelianum (TM) was isolated from all CCs at 22.56 mm day−1.Figure 2Panel (a): quadratic regression of leaf area index (LAI, m2 m−2) vs cover crop evapotranspiration per unit of soil (ET, mm day−1). Each data point is mean value ± SE (n = 4). The quadratic model equation is y = − 0.128×2 + 2.9968x + 5.4716, R2 = 0.76. Panel (b): the quadratic regression between LAI corresponding to the clipped biomass (m2 m−2) and cover crop ET reduction (%). Each data point is mean value ± SE (n = 4). Quadratic model equation is y = − 0.8985×2 + 16.503x + 5.1491, R2 = 0.94.Full size imageWhen regressing the fraction of ET reduction, compared to pre-mowing values vs LAI (Fig. 2b), the same quadratic model achieved a very close fit (R2 = 0.94, p  1 mm) root diameters as affected by soil cover.Full size tableThe highest values of diameter class length (DCL, mm cm−3) for very fine roots (DCL_VF,  1.0 mm) roots although, most notably, L. corniculatus roots showed the highest abundance for both DCL_M (23.08 cm cm−3) and DCL_C (0.54 cm cm−3).At the 10–20 cm soil depth, GR confirmed the highest values for both very fine and fine roots, with F. arundinacea reaching maximum DCL of 2.269 and 5.215 cm cm-3, respectively (Table 2). L. corniculatus largely outscored any other species for both medium and coarse root diameter (6.173 and 0.037 cm cm−3, respectively), with F. arundinacea ranking second (3.157 and 0.016 cm cm−3, respectively).The highest root dry weight (RDW, mg cm-3) within the topsoil layer was reached by L. corniculatus (8.7 mg cm−3) and F. arundinacea (7.6 mg cm-3). Notably, such values were significantly higher than those recorded on the remaining species, except for the F. arundinacea vs F. rubra commutata comparison (Table 2). At 10–20 depth, scant variation was recorded in RDW measured in grasses, whereas L. corniculatus held its supremacy within legumes (4.5 mg cm−3). Within the creeping type, D. repens (DR) and G. hederacea (GH) scored RDW values as high as those determined for grass species (namely F. arundinacea , P. pratensis and F. rubra commutata), whereas S. subulata (SS) essentially had no root development.Soil aggregates and mean weight diameter (MWD)Table 3 reports the proportional aggregate weight (g kg−1) for both 0–10 and 10–20 cm soil depths. Compared to bare soil, the largest increase in large macroaggregates (LM,  > 2000 µm) in the top 10 cm of soil was achieved by L. corniculatus with 461 g kg−1. L. corniculatus differed from the rest of the LE group, whose grand mean (90 g kg−1) was the lowest of the three tested groups. As a legume, T. subterraneum (TS, 122 g kg−1) recorded the lowest values compared to fellow CR species, ranging between 211 (D. repens, DR) and 316 g kg−1 (G. hederacea, GH). GR recorded LM values slightly lower than those of CR, with a mean value of 217 vs 224 g kg-1.Table 3 Proportional aggregate weight (g kg−1) of sand-free aggregate-size fractions acquired from wet sieving as affected by soil cover and mean weight diameter (MWD). Aggregate-size fraction divided as macroaggregates with large size ( > 2 mm, LM) and small size (2 mm—250 μm, sM), microaggregates (250 μm—53 μm, m), and silt and clay ( More

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    Potential for mercury methylation by Asgard archaea in mangrove sediments

    Hsu-Kim H, Kucharzyk KH, Zhang T, Deshusses MA. Mechanisms regulating mercury bioavailability for methylating microorganisms in the aquatic environment: A critical review. Environ Sci Technol. 2013;47:2441–56.Article 
    CAS 

    Google Scholar 
    Podar M, Gilmour CC, Brandt CC, Soren A, Brown SD, Crable BR, et al. Global prevalence and distribution of genes and microorganisms involved in mercury methylation. Sci Adv. 2015;1:e1500675.Article 

    Google Scholar 
    Liu YR, Johs A, Bi L, Lu X, Hu HW, Sun D, et al. Unraveling microbial communities associated with methylmercury production in paddy soils. Environ Sci Technol. 2018;52:13110–8.Article 
    CAS 

    Google Scholar 
    Lee C-S, Fisher NS. Bioaccumulation of methylmercury in a marine copepod. Environ Toxicol Chem. 2017;36:1287–93.Article 
    CAS 

    Google Scholar 
    Parks JM, Johs A, Podar M, Bridou R, Hurt RAJ, Smith SD, et al. The genetic basis for bacterial mercury methylation. Science 2013;339:1332–5.Article 
    CAS 

    Google Scholar 
    McDaniel EA, Peterson BD, Stevens SLR, Tran PQ, Anantharaman K, McMahon KD. Expanded phylogenetic diversity and metabolic flexibility of mercury-methylating microorganisms. mSystems 2020;5:e00299–20.Article 
    CAS 

    Google Scholar 
    Cooper CJ, Zheng K, Rush KW, Johs A, Sanders BC, Pavlopoulos GA, et al. Structure determination of the HgcAB complex using metagenome sequence data: Insights into microbial mercury methylation. Commun Biol. 2020;3:320.Article 
    CAS 

    Google Scholar 
    Kerin EJ, Gilmour CC, Roden E, Suzuki MT, Coates JD, Mason RP. Mercury methylation by dissimilatory iron-reducing bacteria. Appl Environ Microbiol. 2006;72:7919–21.Article 
    CAS 

    Google Scholar 
    Gilmour CC, Podar M, Bullock AL, Graham AM, Brown SD, Somenahally AC, et al. Mercury methylation by novel microorganisms from new environments. Environ Sci Technol. 2013;47:11810–20.Article 
    CAS 

    Google Scholar 
    Capo E, Bravo AG, Soerensen AL, Bertilsson S, Pinhassi J, Feng C, et al. Deltaproteobacteria and Spirochaetes-like bacteria are abundant putative mercury methylators in oxygen-deficient water and marine particles in the Baltic Sea. Front Microbiol. 2020;11:574080.Article 

    Google Scholar 
    Gionfriddo CM, Tate MT, Wick RR, Schultz MB, Zemla A, Thelen MP, et al. Microbial mercury methylation in Antarctic sea ice. Nat Microbiol. 2016;1:16127.Article 
    CAS 

    Google Scholar 
    Jones DS, Walker GM, Johnson NW, Mitchell CPJ, Coleman Wasik JK, Bailey JV. Molecular evidence for novel mercury methylating microorganisms in sulfate-impacted lakes. ISME J. 2019;13:1659–75.Article 
    CAS 

    Google Scholar 
    Christensen GA, Gionfriddo CM, King AJ, Moberly JG, Miller CL, Somenahally AC, et al. Determining the reliability of measuring mercury cycling gene abundance with correlations with mercury and methylmercury concentrations. Environ Sci Technol. 2019;53:8649–63.Article 
    CAS 

    Google Scholar 
    Villar E, Cabrol L, Heimburger-Boavida LE. Widespread microbial mercury methylation genes in the global ocean. Environ Microbiol Rep. 2020;12:277–87.Article 
    CAS 

    Google Scholar 
    Lin H, Ascher DB, Myung Y, Lamborg CH, Hallam SJ, Gionfriddo CM, et al. Mercury methylation by metabolically versatile and cosmopolitan marine bacteria. ISME J. 2021;15:1810–25.Article 
    CAS 

    Google Scholar 
    King JK, Kostka JE, Frischer ME, Saunders FM, Jahnke RA. A quantitative relationship that demonstrates mercury methylation rates in marine sediments are based on the community composition and activity of sulfate-reducing bacteria. Environ Sci Technol. 2001;35:2491–6.Article 
    CAS 

    Google Scholar 
    Regnell O, Watras CJ. Microbial mercury methylation in aquatic environments: A critical review of published field and laboratory studies. Environ Sci Technol. 2019;53:4–19.Article 
    CAS 

    Google Scholar 
    Xie R, Wang Y, Huang D, Hou J, Li L, Hu H, et al. Expanding Asgard members in the domain of Archaea sheds new light on the origin of eukaryotes. Sci China Life Sci. 2022;65:818–29.Article 
    CAS 

    Google Scholar 
    Seitz KW, Dombrowski N, Eme L, Spang A, Lombard J, Sieber JR, et al. Asgard archaea capable of anaerobic hydrocarbon cycling. Nat Commun. 2019;10:1822.Article 

    Google Scholar 
    Zaremba-Niedzwiedzka K, Caceres EF, Saw JH, Backstrom D, Juzokaite L, Vancaester E, et al. Asgard archaea illuminate the origin of eukaryotic cellular complexity. Nature 2017;541:353–8.Article 
    CAS 

    Google Scholar 
    Liu Y, Makarova KS, Huang W-C, Wolf YI, Nikolskaya AN, Zhang X, et al. Expanded diversity of Asgard archaea and their relationships with eukaryotes. Nature 2021;593:553–7.Article 
    CAS 

    Google Scholar 
    Zhang JW, Dong HP, Hou LJ, Liu Y, Ou YF, Zheng YL, et al. Newly discovered Asgard archaea Hermodarchaeota potentially degrade alkanes and aromatics via alkyl/benzyl-succinate synthase and benzoyl-CoA pathway. ISME J. 2021;15:1826–43.Article 
    CAS 

    Google Scholar 
    Cai M, Liu Y, Yin X, Zhou Z, Friedrich MW, Richter-Heitmann T, et al. Diverse Asgard archaea including the novel phylum Gerdarchaeota participate in organic matter degradation. Sci China Life Sci. 2020;63:886–97.Article 
    CAS 

    Google Scholar 
    Baker BJ, De Anda V, Seitz KW, Dombrowski N, Santoro AE, Lloyd KG. Diversity, ecology and evolution of Archaea. Nat Microbiol. 2020;5:887–900.Article 
    CAS 

    Google Scholar 
    Farag Ibrahim F, Zhao R, Biddle Jennifer F, Atomi H. “Sifarchaeota,” a novel Asgard phylum from Costa Rican sediment capable of polysaccharide degradation and anaerobic methylotrophy. Appl Environ Micro. 2021;87:e02584–20.
    Google Scholar 
    Adam PS, Borrel G, Brochier-Armanet C, Gribaldo S. The growing tree of Archaea: new perspectives on their diversity, evolution and ecology. ISME J. 2017;11:2407–25.Article 

    Google Scholar 
    Cai M, Richter-Heitmann T, Yin X, Huang W-C, Yang Y, Zhang C, et al. Ecological features and global distribution of Asgard archaea. Sci Total Environ. 2021;758:143581.Article 
    CAS 

    Google Scholar 
    Zhang C-J, Chen Y-L, Sun Y-H, Pan J, Cai M-W, Li M. Diversity, metabolism and cultivation of archaea in mangrove ecosystems. Mar Life Sci Tech. 2020;3:252–62.Article 

    Google Scholar 
    Dai SS, Yang Z, Tong Y, Chen L, Liu SY, Pan R, et al. Global distribution and environmental drivers of methylmercury production in sediments. J Hazard Mater. 2021;407:124700.Article 
    CAS 

    Google Scholar 
    Tang WL, Liu YR, Guan WY, Zhong H, Qu XM, Zhang T. Understanding mercury methylation in the changing environment: Recent advances in assessing microbial methylators and mercury bioavailability. Sci Total Environ. 2020;714:136827.Article 
    CAS 

    Google Scholar 
    Tsui MTK, Finlay JC, Balogh SJ, Nollet YH. In situ production of methylmercury within a stream channel in northern California. Environ Sci Technol. 2010;44:6998–7004.Article 
    CAS 

    Google Scholar 
    Liu Y, Zhou Z, Pan J, Baker BJ, Gu JD, Li M. Comparative genomic inference suggests mixotrophic lifestyle for Thorarchaeota. ISME J. 2018;12:1021–31.Article 
    CAS 

    Google Scholar 
    Lei P, Zhong H, Duan D, Pan K. A review on mercury biogeochemistry in mangrove sediments: Hotspots of methylmercury production? Sci Total Environ. 2019;680:140–50.Article 
    CAS 

    Google Scholar 
    Beckers F, Rinklebe J. Cycling of mercury in the environment: Sources, fate, and human health implications: A review. Crit Rev Env Sci Tec. 2017;47:693–794.Article 
    CAS 

    Google Scholar 
    de Oliveira DC, Correia RR, Marinho CC, Guimaraes JR. Mercury methylation in sediments of a Brazilian mangrove under different vegetation covers and salinities. Chemosphere 2015;127:214–21.Article 

    Google Scholar 
    Li R, Xu H, Chai M, Qiu GY. Distribution and accumulation of mercury and copper in mangrove sediments in Shenzhen, the world’s most rapid urbanized city. Environ Moni Assess. 2016;188:87.Article 

    Google Scholar 
    O’Connor D, Hou D, Ok YS, Mulder J, Duan L, Wu Q, et al. Mercury speciation, transformation, and transportation in soils, atmospheric flux, and implications for risk management: A critical review. Environ Int. 2019;126:747–61.Article 

    Google Scholar 
    Obrist D, Kirk JL, Zhang L, Sunderland EM, Jiskra M, Selin NE. A review of global environmental mercury processes in response to human and natural perturbations: Changes of emissions, climate, and land use. Ambio 2018;47:116–40.Article 

    Google Scholar 
    Capo E, Peterson BD, Kim M, Jones DS, Acinas SG, Amyot M, et al. A consensus protocol for the recovery of mercury methylation genes from metagenomes. Mol Ecol Resour. 2022; https://doi.org/10.1111/1755-0998.13687.Gionfriddo CM, Wymore AM, Jones DS, Wilpiszeski RL, Lynes MM, Christensen GA, et al. An improved hgcAB primer set and direct high-throughput sequencing expand Hg-methylator diversity in nature. Front Microbiol. 2020;11:541554.Article 

    Google Scholar 
    Yu R-Q, Barkay T. Chapter two – microbial mercury transformations: Molecules, functions and organisms. Adv Appl Microbiol. 2022;118:31–90.Article 

    Google Scholar 
    Chételat J, Richardson MC, MacMillan GA, Amyot M, Poulain AJ. Ratio of methylmercury to dissolved organic carbon in water explains methylmercury bioaccumulation across a latitudinal gradient from north-temperate to arctic lakes. Environ Sci Technol. 2018;52:79–88.Article 

    Google Scholar 
    Liu Y-R, Dong J-X, Han L-L, Zheng Y-M, He J-Z. Influence of rice straw amendment on mercury methylation and nitrification in paddy soils. Environ Pollut. 2016;209:53–9.Article 
    CAS 

    Google Scholar 
    Moreau JW, Gionfriddo CM, Krabbenhoft DP, Ogorek JM, DeWild JF, Aiken GR, et al. The effect of natural organic matter on mercury methylation by Desulfobulbus propionicus 1pr3. Front Microbiol. 2015;6:1389.Article 

    Google Scholar 
    Chen C-F, Ju Y-R, Chen C-W, Dong C-D. The distribution of methylmercury in estuary and harbor sediments. Sci Total Environ. 2019;691:55–63.Article 
    CAS 

    Google Scholar 
    Bravo AG, Bouchet S, Guédron S, Amouroux D, Dominik J, Zopfi J. High methylmercury production under ferruginous conditions in sediments impacted by sewage treatment plant discharges. Water Res. 2015;80:245–55.Article 
    CAS 

    Google Scholar 
    Wang H, Su J, Zheng T, Yang X. Insights into the role of plant on ammonia-oxidizing bacteria and archaea in the mangrove ecosystem. J Soil Sediment. 2015;15:1212–23.Article 
    CAS 

    Google Scholar 
    Imachi H, Nobu MK, Nakahara N, Morono Y, Ogawara M, Takaki Y, et al. Isolation of an archaeon at the prokaryote–eukaryote interface. Nature 2020;577:519–25.Article 
    CAS 

    Google Scholar 
    Zhou J, Riccardi D, Beste A, Smith JC, Parks JM. Mercury methylation by HgcA: Theory supports carbanion transfer to Hg(II). Inorg Chem. 2014;53:772–7.Article 
    CAS 

    Google Scholar 
    Smith Steven D, Bridou R, Johs A, Parks Jerry M, Elias Dwayne A, Hurt Richard A, et al. Site-directed mutagenesis of HgcA and HgcB reveals amino acid residues important for mercury methylation. Appl Environ Micro. 2015;81:3205–17.Article 
    CAS 

    Google Scholar 
    Sousa FL, Neukirchen S, Allen JF, Lane N, Martin WF. Lokiarchaeon is hydrogen dependent. Nat Microbiol. 2016;1:16034.Article 
    CAS 

    Google Scholar 
    Schaefer JK, Rocks SS, Zheng W, Liang L, Gu B, Morel FMM. Active transport, substrate specificity, and methylation of Hg(II) in anaerobic bacteria. Proc Natl Acad Sci USA 2011;108:8714.Article 
    CAS 

    Google Scholar 
    Sakai S, Imachi H, Hanada S, Ohashi A, Harada H, Kamagata Y. Methanocella paludicola gen. nov., sp. nov., a methane-producing archaeon, the first isolate of the lineage ‘Rice Cluster I’, and proposal of the new archaeal order Methanocellales ord. nov. Int J Syst Evol Microbiol. 2008;58:929–36.Article 

    Google Scholar 
    Dridi B, Fardeau ML, Ollivier B, Raoult D, Drancourt M. Methanomassiliicoccus luminyensis gen. nov., sp. nov., a methanogenic archaeon isolated from human faeces. Int J Syst Evol Microbiol. 2012;62:1902–7.Article 
    CAS 

    Google Scholar 
    Dietz R, Sonne C, Basu N, Braune B, O’Hara T, Letcher RJ, et al. What are the toxicological effects of mercury in arctic biota? Sci Total Environ. 2013;443:775–90.Article 
    CAS 

    Google Scholar 
    Gilmour Cynthia C, Bullock Allyson L, McBurney A, Podar M, Elias Dwayne A, Lovley Derek R. Robust mercury methylation across diverse methanogenic archaea. mBio 2018;9:e02403–17.
    Google Scholar 
    Pan J, Chen Y, Wang Y, Zhou Z, Li M. Vertical distribution of Bathyarchaeotal communities in mangrove wetlands suggests distinct niche preference of Bathyarchaeota subgroup 6. Micro Ecol. 2019;77:417–28.Article 

    Google Scholar 
    Zhang C-J, Pan J, Duan C-H, Wang Y-M, Liu Y, Sun J, et al. Prokaryotic diversity in mangrove sediments across southeastern China fundamentally differs from that in other biomes. mSystems 2019;4:e00442–19.Article 
    CAS 

    Google Scholar 
    Peng Y, Leung HC, Yiu SM, Chin FY. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics 2012;28:1420–8.Article 
    CAS 

    Google Scholar 
    Li D, Liu C-M, Luo R, Sadakane K, Lam T-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 2015;31:1674–6.Article 
    CAS 

    Google Scholar 
    Hyatt D, Chen G-L, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinforma. 2010;11:119.Article 

    Google Scholar 
    Zhang C-J, Pan J, Liu Y, Duan C-H, Li M. Genomic and transcriptomic insights into methanogenesis potential of novel methanogens from mangrove sediments. Microbiome. 2020;8:94.Article 
    CAS 

    Google Scholar 
    Kang DD, Froula J, Egan R, Wang Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ 2015;3:e1165.Article 

    Google Scholar 
    Sieber CMK, Probst AJ, Sharrar A, Thomas BC, Hess M, Tringe SG, et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat Microbiol. 2018;3:836–43.Article 
    CAS 

    Google Scholar 
    Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.Article 
    CAS 

    Google Scholar 
    Olm MR, Brown CT, Brooks B, Banfield JF. dRep: A tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 2017;11:2864–8.Article 
    CAS 

    Google Scholar 
    Chaumeil PA, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 2019;36:1925–7.
    Google Scholar 
    Kanehisa M, Sato Y, Morishima K. BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J Mol Biol. 2016;428:726–31.Article 
    CAS 

    Google Scholar 
    Huerta-Cepas J, Forslund K, Szklarczyk D, Jensen LJ, von Mering C, Bork P. Fast genome-wide functional annotation through orthology assignment by eggNOG-mapper. Mol Biol Evol. 2017;34:2115–22.Article 
    CAS 

    Google Scholar 
    Finn RD, Clements J, Eddy SR. HMMER web server: interactive sequence similarity searching. Nucleic Acids Res. 2011;39:W29–W37.Article 
    CAS 

    Google Scholar 
    Eddy SR. Accelerated profile HMM searches. PLoS Comput Biol. 2011;7:e1002195.Article 
    CAS 

    Google Scholar 
    Edgar RC. MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–7.Article 
    CAS 

    Google Scholar 
    Capella-Gutiérrez S, Silla-Martínez JM, Gabaldón T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 2009;25:1972–3.Article 

    Google Scholar 
    Nguyen L-T, Schmidt HA, von Haeseler A, Minh BQ. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol. 2015;32:268–74.Article 
    CAS 

    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 
    Letunic I, Bork P. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res. 2016;44:W242–5.Article 
    CAS 

    Google Scholar 
    Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. Nature 2021;596:583–9.Article 
    CAS 

    Google Scholar 
    Trott O, Olson AJ. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010;31:455–61.CAS 

    Google Scholar 
    Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–9.Article 
    CAS 

    Google Scholar 
    Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinforma (Oxf, Engl). 2010;26:841–2.Article 
    CAS 

    Google Scholar 
    Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 2009;25:1754–60.Article 
    CAS 

    Google Scholar  More

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    Genetic basis of thiaminase I activity in a vertebrate, zebrafish Danio rerio

    Sequence analysisProtein sequence searches were conducted in the GenBank nr database with BLASTP42 using default parameters, including automatically adjusting parameters for short input sequences (Table S1). Conserved domain searches were run against the GenBank Conserved Domain Database (CDD)43. Sequence alignments were conducted in CLC Main Workbench 20.0.4 (Qiagen) with the fast alignment algorithm, gap open cost = 10, and gap extension cost = 1. Biochemical properties of the fish putative thiaminase I protein sequences were predicted with the Create Sequence Statistics function in CLC Main Workbench 20.0.4 (Qiagen, Hilden, Germany). The molecular weights were calculated from the sum of the amino acids in the sequence, and the isoelectric points (pIs) were calculated from the pKa values for the individual amino acids in the sequence.Bacteria culturePure cultures of P. thiaminolyticus strain 818822 were cultured at 37 °C in Terrific Broth (MO BIO Laboratories, Carlsbad, CA) in either a shaking incubator or in a beveled flask with a stir bar and were harvested after 48–80 h of culture. Upon harvest, cultures were processed immediately or frozen whole in 50 mL Falcon tubes at − 80 °C. Fresh or thawed cultures were spun at 14,000×g, and culture supernatant was concentrated using Amicon-ultra 10 kDa molecular weight cut-off (MWCO) filters (EMD Millipore, Billerica, MA).The zebrafish and alewife candidate thiaminase I genes were cloned and overexpressed in E. coli to determine whether they produced functional thiaminases. The recombinant thiaminase I gene from P. thiaminolyticus was overexpressed in E. coli as a positive control. Candidate and control genes were synthesized (Integrated DNA Technologies, Inc., Coralville, Iowa) and placed into the pET52b vector (EMD Millipore). Insert sequences are provided in Supplementary Figs. S10–S13. The empty pET52b vector was used as a negative control. The plasmid was transformed into E. coli (Rosetta 2(DE3)pLysS Singles Competent Cells, EMD Millipore) according to the manufacturer’s instructions, and expression of candidate genes was induced by the addition of IPTG. Cells were lysed in 1X BugBuster (Millipore) according to the manufacturer’s instructions in the presence of benzonase nuclease, and soluble and insoluble fractions were separated by centrifugation.Tissue collectionsAdult common carp were captured from Lake Erie using short-set gill nets. Adult alewife and quagga mussels (Dreissena bugensis) were collected from Sturgeon Bay, Lake Michigan using bottom trawls. Fish collections were completed during July 2007. Sex of sampled fish was not identified. Upon collection, unanesthetized animals were immediately euthanized by flash freezing between slabs of dry ice and stored at − 80 °C. Fish were harvested by the Great Lakes Science Center, U.S. Geological Survey (USGS). Laboratory use of frozen animal tissues and wild type and recombinant bacteria was in accordance with institutional guidelines and biosafety procedures at Oregon State University and USGS. Animal care and use procedures were approved by the Great Lakes Science Center, USGS. All USGS sampling and handling of fish during research are carried out in accordance with guidelines for the care and use of fishes by the American Fisheries Society44. All methods are reported in accordance with applicable ARRIVE guidelines (https://arriveguidelines.org). Zebrafish from OSU’s zebrafish facility were anesthetized and euthanized by overdose with waterborne 200 ppm ethyl 3-aminobenzoate methanesulfonate (MS-222, Sigma-Aldrich, St. Louis, MO) following protocols approved by the OSU Animal Institutional Care and Use Committee and were frozen at − 80 °C after euthanization. Gills, liver, spleen, and the intestinal tract were dissected, and gill tissue was homogenized separately from liver, spleen, and gut, which were homogenized together and designated “viscera.” Homogenization and protein preparation procedures were the same as that for alewife. Zebrafish from Columbia Environmental Research Center (CERC), USGS cultures were anesthetized and euthanized by overdose with 200 ppm ethyl 3-aminobenzoate methanesulfonate (MS-222, Sigma-Aldrich, St. Louis, MO) in water following protocols approved by CERC Institutional Animal Care and Use Committee (IACUC). Whole fish (0.2–0.6 g) were homogenized in 10 mL cold phosphate buffer, pH 6.5. Whole common carp and alewife were thawed until they could just be dissected. Preliminary trial extractions on alewife stomach and intestines, spleen, and gills revealed similar results and revealed that gills and spleen tissue produced the cleanest protein preparations. Therefore, subsequent extractions for common carp and alewife used gill tissue. Samples were pooled from 3 to 5 individual fish, haphazardly chosen from the sampled fish without exclusions. Quagga mussels were thawed just sufficiently to be husked from their shell and were used whole. Researchers were aware of the species and tissue designation of each sample throughout the experiments. Animal tissues were placed in ice-cold (4 °C) beakers containing cold extraction buffer (16 mM K3HPO4, 84 mM KH2PO4, 100 mM NaCl, pH 6.5 with 1 mM DTT, 2 mM EDTA, 3 mM Pepstatin, 1X Protease inhibitor cocktail (Sigma), and 1 mM AEBSF). All extractions were carried out at 4 °C in pre-chilled glassware. Samples were mechanically homogenized using a rotor–stator tissue grinder. Samples were stirred gently for several hours to overnight at 4 °C, centrifuged at 14,000×g to remove debris, and strained through cheesecloth to remove any insoluble lipids. Extracts were then subjected to 30–75% ammonium sulfate precipitation. Pellets from the precipitation were resuspended in buffer (83 mM KH2PO4, 17 mM K2HPO4, and 100 mM NaCl), centrifuged to remove any remaining debris, and stored in 30% glycerol at − 20 °C.Protein electrophoresisNative PAGE was run using either pre-cast TGX gels (BioRad, Hercules, California) of varying percentage (7.5% to 12% or 8–16% gradient gels) or on hand-cast gels (TGX FastCast, BioRad) made according to the manufacturer’s instructions.Blue-native PAGE was used to estimate the mass of thiaminases in their native conformation. Blue-native PAGE45 gels were run using the NativePage Novex Bis–Tris system (Life Technologies) or hand-cast equivalents46. Light blue cathode buffer was used to facilitate visualization of the activity stain.Standard denaturing SDS-PAGE was used to estimate the molecular mass of thiaminases after denaturation. Denaturing SDS-PAGE was run using one of three relatively equivalent methods: pre-cast TGX gels (BioRad) according to the manufacturer’s instructions, hand-cast Tris–HCl gels using standard Laemmli chemistry47 with an operating pH of approximately 9.5, or hand-cast Bis–Tris gels (MOPS buffer) with an operating pH of approximately 7. For all denaturing and non-denaturing SDS-PAGE applications, standard Laemmli sample buffer was used, and samples were heated to 75 °C for 15 min to facilitate denaturation followed by brief centrifugation to eliminate any precipitated debris.Non-denaturing PAGE was used as an alternative to denaturing PAGE for the common carp thiaminase that could not be renatured (i.e., activity could not be recovered) following a denaturing SDS-PAGE. Non-denaturing PAGE was conducted using any of the three aforementioned gel chemistries with SDS-containing running buffers including reductant (DTT), but samples were not heated prior to application to the gel. Samples for non-denaturing PAGE were allowed to incubate in sample buffer at room temperature for 30 min prior to gel loading. This preserves the charge-shift induced by SDS but does not result in protein denaturation, facilitating in-gel analysis of thiaminase I activity after separation.To visualize proteins following electrophoresis, gels were stained with Coomassie stain (CBR-250 at 1 g/L in methanol/acetic acid/water (4:5:1) and destained with methanol/acetic acid/water (1.7:1:11.5). Mini-gels were run on BioRad’s mini-protean gel rigs. Midi-gels (16 cm length) were run on Hoefer’s SE660, and large-format gels (32 cm length) were run on a BioRad’s Protean Slab Cell. Mini-gels were generally run at room temperature, and midi- and large-format gels were run at 4 °C. Blue-native PAGE was always run at 4 °C.Two-dimensional electrophoresis (2DE) separated proteins in the first dimension based on pI and in the second dimension based on mass (either native or denatured). 2DE was performed by combining in-gel IEF with either denaturing SDS-PAGE, non-denaturing SDS-PAGE, or native PAGE. IPG strips were incubated in TRIS-buffered equilibration solution48 either with 6 M urea, SDS, and iodacetamide (denaturing) or without urea, SDS, and iodacetamide (non-denaturing) for 20 min. Low melting point agarose was used to solidify IGP strips in place. Agarose was cooled to just above the gelling temperature, as hot agarose inactivated thiaminase I activity.Isoelectric focusingIsoelectric focusing (IEF) was conducted both in-gel and in-liquid. In-gel IEF was conducted in immobilized pH gradient (IPG) strips using a Multifor II (GE Healthcare Life Sciences). Prior to rehydration, all protein preparations were desalted in low-salt (~ 5 to 10 mM) sodium or potassium phosphate buffer (pH 6.5) using 10 kDA MWCO filters. All samples were applied using sample volumes and protein concentrations recommended by the manufacturer. For standard denaturing in-gel IEF, rehydration solution consisted of 8 M urea, 2% CHAPS, 2% IPG buffer of the appropriate pH-range, 1% bromophenol blue, and 18 mM DTT. The IEF was conducted at maximum of 2 mA total current and 5 W total power, with an EPS3500 XL power supply in gradient mode. Voltage gradients were based on standard protocols recommended by the manufacturer. In-gel IEF was also performed under native conditions to allow thiaminase I activity staining of IPG strips. Protocols were essentially the same as those for denaturing conditions, with the following exceptions: (1) urea was eliminated and the CHAPS concentration was reduced to 0.5% in the rehydration solution; (2) rehydration was conducted at 14 °C; and (3) the water in the cooling tray was cooled to 4 °C.In-liquid IEF was conducted using a Rotofor (BioRad) according to the manufacturer’s instructions. Non-denaturing in-liquid IEF was also conducted using a focusing solution including no urea, 2% pH 3–10 biolyte, 0.5% CHAPS, 20% glycerol, and 5 mM DTT. The addition of glycerol helped retain activity but also increased focusing times. The Rotofor was run at a constant 15 W with a maximum current of 20 mA and voltage set for a maximum of 2000 V. Samples containing 8 M urea were cooled to 14 °C during focusing to avoid urea precipitation, whereas samples lacking urea were cooled to 4 °C during focusing. Protein extracts in salt solutions greater than 10 mM were desalted directly in focusing solution using a 10 kDA MWCO filter. Focusing runs were allowed to proceed until the voltage stabilized and fractions were harvested with the needle array and vacuum pump. Ampholytes were removed by addition of NaCl to 1 M and then samples were desalted into phosphate buffer using a 10kD MWCO filter.Thiaminase I activity measurementsFor quantitative measurements of thiaminase I activity, we conducted a radiometric assay at CERC as previously described49. Zebrafish homogenates were diluted 1:8, 1:16, or 1:32 in cold phosphate buffer, pH 6.5. Two replicates per dilution were assayed. Activity was calculated from the greatest dilution that gave activity within the linear range of the assay and was reported as pmol thiamine consumed per g tissue (wet weight) per minute (pmol/g/min).Thiaminase I activity stainingAfter electrophoresis, gels were stained for thiaminase I activity using a previously described diazo-coupling reaction19,50. Briefly, gels were washed 3 times in water, twice in 25 mM sodium phosphate buffer with 1 mM DTT, and once in 25 mM sodium phosphate buffer without DTT. Gels were then incubated in 0.89 mM thiamine-HCl and co-substrate (1.45 mM pyridoxine, 24 mM nicotinic acid, or 20 mM pyridine) in 25 mM sodium phosphate buffer for 10 min. Gels were briefly rinsed in water and placed in a lidded container and incubated at 37 °C for 30 min to allow thiamine degradation by any thiaminases in the gel. The diazo stain19,50 was then applied to detect remaining thiamine in the gel for five minutes with gentle agitation. Stained gels were rinsed with water and photographed, and further stained with Coomassie to visualize proteins. More

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    Conservation setbacks? The secrets to lifting morale

    Conservationist Jim Groombridge in Hawaii (standing) performing a ‘heli-hook-up’, in which a net full of equipment is hooked up to the hovering helicopter, to save it needing to land.Credit: Jim Groombridge/Maui Forest Bird Recovery Project

    Since his undergraduate degree, Jim Groombridge has been part of several teams that work with critically endangered animals, including the Mauritius kestrel (Falco punctatus), which was brought back from the brink of extinction. But he has also experienced the devastation of some species being lost forever, despite all possible interventions. After receiving his PhD from Queen Mary University of London in 2000, he worked as a project coordinator at the Maui Forest Bird Recovery Project in Makawao, Hawaii. Conservation science spans many topics including climate change, working with local communities, epidemiology, genomics and designing protected areas. Projects can range from single-species conservation to ecosystem-level or landscape conservation, such as restoring whole islands. Now a professor in biodiversity conservation at the University of Kent’s Durrell Institute of Conservation and Ecology in Canterbury, UK, Groombridge teaches bachelor’s and master’s students about leadership of conservation teams and how to motivate them in the face of setbacks.What is special about leading conservation teams?Conservation field teams are slightly quirky, and those quirks can define what makes a team work well or not. One is that team leaders are rarely trained in management tasks, such as overseeing a budget, interacting with project partners and local governments, dealing with team members who feel passionate about what they do and facing the high stakes involved. Team members are enthusiastic, passionate and seldom motivated by money.Another quirk is that, in a small conservation team of four to six people, there is often a mix of skill sets and experience. You can have highly experienced specialists in a particular area, such as screening parrots for diseases, or reintroduction biology, and you might also have volunteers with only passion and enthusiasm to offer.How do you lead a team with such variable experience?Even with those different levels of expertise, you still need to meet high standards for specimen and data collection. At the moment, for example, I’m sequencing the genome of the pink pigeon (Nesoenas mayeri), using samples collected in the 1990s. There’s a sense of responsibility, especially if you’re working with species that are rare, because if you mess it up, they could go extinct. It’s not unusual to have volunteers with only two or three weeks’ worth of experience handling extremely rare samples or working with valuable data sets. Their learning curve is pretty steep. As a leader, you need to make sure that you understand the details — ranging from tasks such as collecting data and monitoring and recording invasive species to, for example, knowing how to trap a mongoose — so that you can make sure that everyone is collecting the data in the same way.

    Jim Groombridge (far left), who studies biodiversity conservation at the University of Kent, UK, with one of the field crews involved in an operation to translocate a bird called the po‘ouli in Hawaii.Credit: Jim Groombridge/Maui Forest Bird Recovery Project

    What do team members tend to have in common?They often share a passion for nature. They want to save the environment, they want to save a species from going extinct, they want to make a difference. That level of emotion is important. It creates an energy, which needs to be channelled proactively and positively into the project to make it a success.In 2002, for example, I was leading a team working to save a bird called the po‘ouli (Melamprosops phaeosoma) on the island of Maui, part of the Hawaiian archipelago. We were trying to translocate one of the last known birds into the range of another one to give them the opportunity to breed. There was huge excitement, but after four weeks of failing to catch the bird, there was also a lot of frustration.How do you manage a team with such strong emotions?Morale is really important. So is being able to deal with difficulties when they arise. That’s what gets small teams through tough times. With the po‘ouli, I had to make sure that the team had fun, and that people genuinely enjoyed themselves. That meant taking time out with the team in the evenings and ensuring that everyone had a bit of a laugh, so it wasn’t deadly serious all the time. Also, I made sure that team members got to perform the aspects of the job that they were good at, to increase their confidence and well-being. We eventually trapped the po‘ouli and moved it, but even though the birds were in the same territory, they didn’t breed.How do you manage expectations amid failure?I had to remind the team about the broader picture of what we had achieved. This was the first time anyone had followed the po‘ouli in the forest for ten days. I think we learnt more about the ecology of that species in that time than anyone had learnt in 30 years. We held the translocated bird for about two hours before we released it, and it took food items from us, which showed that the birds could be kept in captivity if necessary. We learnt a huge amount that could be applied to another project.
    Treading carefully: saving frankincense trees in Yemen
    You have to manage people’s expectations and have goals that are achievable. If you are starting a project on a species with fewer than ten individuals left in the wild, and your goal is to have thousands, that’s a difficult leap of imagination. Instead, perhaps start with finding a food that a species would eat in captivity. People need to remain connected with what’s achievable. There’s a delicate balance between being aspirational and being pragmatic.As a team member, what do you wish more conservation leaders knew?Often, there is too much emphasis placed on the command structure. Innovation in a conservation team is undersold, and easily quashed by a type of line-manager approach. The hierarchy in a team is important because people know what to do and who to report to, but you also have to encourage team members to use their initiative and ask questions. I remember when my team and I were in the cloud forests, tropical mountain regions covered by clouds for most of the year in Hawaii, we were struggling with baiting rats, which prey on eggs and fledglings of native birds. It’s one of the wettest places on Earth, and the rat poison basically turns to cottage cheese. However, one of my colleagues designed a bait box, which kept the bait dry for many weeks. When you’re working with critically endangered species and in field conditions, ingenuity is crucial.
    This interview has been edited for length and clarity. More

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    Similar adaptative mechanism but divergent demographic history of four sympatric desert rodents in Eurasian inland

    Species distribution modeling, spatial climate segregation and niche widthTo explore the selective regimes of the four species on external environmental factors, we first constructed species distribution modeling (SDM). We obtained a dataset including 22 environmental factors represented by climate, relief, and vegetation variables from 620 localities for DS, 1028 localities for OS, 581 localities for MM and 332 localities for PR, covering most of the species’ distribution ranges (Supplementary Fig. 1). The distribution areas of the four species overlapped widely. The contributions of environmental factors to SDMs showed similarities among the four species. The summer NDVI made important contributions for DS (41.0), OS (44.8), MM (32.5) and PR (8.1), and sand cover contributed significantly to PR (72.7) and DS (16.0) (Fig. 1c). Then, we assessed which set of environmental variables was most closely associated with species distribution via principal component analysis. The bioclimatic space occupied by the four species revealed a large overlap (Fig. 1d), which was consistent with SDM (Supplementary Fig. 1). The distribution of OS was more closely associated with higher-precipitation areas, whereas MM seemed to prefer areas with higher temperatures. Finally, we evaluated the macrohabitat niche breadth of each species. The breadths of environmental space occupation were similar for DS (0.527), MM (0.571), and PR (0.548) and slightly higher for OS (0.622), which suggests that niche selection among the four species is partially overlapping. In total, the four species are mostly similar in the selection of external environmental factors.High-quality genomic landscapes of the four desert rodentsTo investigate the genetic mechanism for desert adaptation of the four sympatric desert rodents, we generated four high-quality de novo genomes (Supplementary Fig. 2). The DS was sequenced using a combined strategy and generated 377.67 Gb of data from Illumina reads, 261.01 Gb from PacBio long reads, 299.51 Gb from 10X Genomics reads, and 389.13 Gb from Hi-C reads (Supplementary Table 1). The final genome size was 2.81 Gb with contig N50 of 31.41 Mb and ~472X mean coverage (Table 1, Supplementary Fig. 3, and Supplementary Tables 2, 3). The contigs for DS were further assembled into pseudochromosomes with lengths on the order of full chromosomes and a scaffold N50 size of 147.24 Mb (Fig. 2a, b, Table 1, and Supplementary Fig. 4). The OS, MM and PR were sequenced using the same hybrid strategy and generated 162.58 Gb, 172.22 Gb, and 214.34 Gb Illumina reads and 183.09 Gb, 161.34 Gb, and 186.45 Gb Oxford Nanopore Technologies long reads, respectively (Supplementary Table 1). The final assembly of OS, MM and PR was 2.83 Gb, 2.43 Gb, and 2.16 Gb with contig N50 of 25.87 Mb, 24.08 Mb, and 42.68 Mb, respectively (Table 1, Supplementary Fig. 4, and Supplementary Tables 2, 3).Table 1 Genome assembly statistics of the four desert rodents.Full size tableFig. 2: High-quality assembly of Dipus sagitta genome and genomic elements of the four sequenced desert rodents.a Hi-C heat map of Dipus sagitta genome assembly. b CIRCOS plot showing the distribution of GC content, transposable elements (TE), and coding sequences (CDS) in the D. sagitta genome. c Orthologous coding sequences composition inferred for thirteen rodents’ genomes. Mcar Mus caroli, Mmus Mus musculus, Mpah Mus Pahari, Mmer Meriones meridianus, Mung Meriones unguiculatus, Cgri Cricetulus griseus, Prob Phodopus roborovskii, Sgal Spalax galili, Osib Orientallactaga sibirica, Dsag Dipus sagitta, Jjac Jaculus jaculus, Hgla Heterocephalus glaber, Cpor Cavia porcellus. d Proportion of transposable elements (TEs). The barplots show the proportions of different types of TEs in corresponding species on the phylogenetic tree.Full size imageAnalyses of the four draft genomes showed that 92.9–95.9% of mammalian BUSCOs were complete, and the GC content was 41.38–42.16% (Table 1 and Supplementary Table 3). Whole-genome annotation was performed via three complementary methods: ab initio prediction, homology-based prediction and RNA-seq based prediction. A total of 23,482, 22,859, 22,533, and 22,314 protein-coding genes were annotated for DS, OS, MM, and PR, respectively (Fig. 2c, Supplementary Fig. 5, Supplementary Table 4). Approximately 98.8–99.1% of genes were functionally annotated for the four species (Supplementary Table 4). Transposable elements (TEs) accounted for 31.38–53.02% of genome assemblies, which predominantly consisted of long-terminal repeats (LTRs), long interspersed nuclear elements (LINEs) and other unknown TEs (Fig. 2d). DS and OS displayed significant LTR expansion of 47.39% and 50.88% in four sequenced genomes, while MM showed an unexpectedly high LINE expansion of 28.99% and sharp LTR contraction to 9.38% (Supplementary Table 5).Phylogenetic relationship and evolutionary historyUsing 5,102 single-copy orthologous groups, we constructed a high-confidence phylogenetic tree using the maximum-likelihood algorithm, including time calibrations based on fossil records and previous studies (Figs. 1b, 2c)22. The phylogenetic tree strongly supported nodes uniting the subfamilies Murinae and Gerbillinae, which together represented the family Muridae (Supplementary Fig. 6). This group was sister to a clade containing cricetids. Spalacidae was recovered as the earliest divergent lineage from Muridae and Cricetidae in the superfamily Muroidea. The split of the most recent common ancestor of Dipodoidea and Muroidea dated to ~56.5 Mya (Fig. 1b, Supplementary Fig. 7). In the Miocene epoch (23 Mya–5.3 Mya), accelerated global geotectonic movement aggravated global climate drying and cooling23. Geological disruptions that modified landscapes and offered new habitats favored the early adaptive radiation of extant desert rodents. The ancestors of four sequenced species emerged separately during this period (Supplementary Note 1). Our phylogenetic tree is consistent with previous evolutionary research on rodents22 and supports the independent evolution of desert adaptations in Jerboas, Gerbils and Hamsters.Expanded and contracted gene familiesComparative genomic analysis revealed 23/32, 4/22, 39/73, and 22/83 gene families exhibiting significant expansion/contraction in the genomes of DS, OS, MM, and PR, respectively (Fig. 1b and Supplementary Fig. 8). Genes belonging to the expanded/contracted families were functionally enriched (Fisher Exact  More