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Samples of host-seeking nymphal black-legged ticks were collected during 2018–2020 from Massachusetts and Rhode Island sites where DTV is enzootic (Fig. 1). Individual DTV-infected ticks were identified by RT-PCR; all ticks (including virus-negative ticks) were also analyzed for B. burgdorferi infection by PCR. Host bloodmeal remnant identification using assays targeting family and order specific retrotransposons was performed as described11, with the addition of newly described primers (see Table 1). Assays targeted likely mammalian reservoir hosts within our study sites.Fig. 1: Map of the field sites included in this study.Ticks were collected from two sites in Washington County, Rhode Island, MB and Trust, as well as, from three islands off the coast of Massachusetts: Nantucket, Martha’s Vineyard, and Naushon Island.Full size imageTable 1 Primers and probes targeting mammalian retrotransposons used in the study for bloodmeal identification in ticks.Full size tableWe identified 20 nymphal ticks that contained DTV RNA from 13 different sites (prevalence, 0.4–7%, Table 2) and confirmed viral identity by sequencing a 248 bp section of the NS5 gene and 286 bp section of the envelope gene. Cognate viral sequences from these ticks were assigned to the DTV lineage (Fig. 2). Sequences from ticks collected from field sites in close geographic proximity often clustered together. Borrelia burgdorferi prevalence was more variable, ranging from 0 to 21%., and B. burgdorferi infection was not associated with DTV infection (p = 0.5, Fig. 3), as sites with high numbers of ticks infected with spirochetes were not the same as those that had high numbers of DTV-infected ticks (Table 2).Table 2 Infection rate of deer tick virus (DTV) and Borrelia burgdorferi in ticks at each study site.Full size tableFig. 2: Maximum likelihood tree of deer tick virus (DTV) detected in this study.A 248 bp piece of the NS5 gene and the 286 bp piece of the envelope gene were sequenced from each positive tick in the study, as well as the positive shrew. These pieces were concatenated and aligned with deer tick virus (DTV) and Powassan virus (POW) sequences downloaded from GenBank (GenBank numbers are listed on the tree). A maximum likelihood tree was then created using MEGAX.Full size imageFig. 3: Correlation analysis of the percentage of ticks that fed on shrews compared to the percentage of infected ticks at our field sites.The B. burgdorferi (Borr) data are shown in panel a and deer tick virus (DTV) data are shown in panel b. The percentage of DTV (n = 20, p = 0.01), but not B. burgdorferi (n = 128, p = 0.5), in ticks at a site is associated with the percentage of ticks that fed on shrews.Full size imageThe source of the infectious larval bloodmeal was identified from 16 of the 20 DTV-infected ticks (80%), 13 of which were identified as shrews (65%) (Table 3). The other DTV-infected ticks had fed on diverse hosts such as bird, squirrel, and cat. One tick showed evidence of having fed on multiple hosts (shrew and deer). None of the ticks had fed on a mouse. We conclude that in our sites, during the years that we sampled, larval ticks feeding on shrews were more likely to be infected by DTV than by feeding on any other animal. Using the 0.1% estimated rate of transmission of adult female ticks to larval progeny for the related tick-borne encephalitis virus12, we calculated that up to four ticks (95% binomial confidence interval of 0 to 0.4% of ticks) from our study could derive from inheritance. Thus, we cannot exclude this as the source of the single infected ticks derived from a bird, squirrel, and cat. However, more than four infected ticks were derived from shrews, suggesting that inheritance alone cannot explain the apparent association.Table 3 Bloodmeal host identified from deer tick virus-infected ticks from each study site.Full size tableDuring the years that we sampled our study sites, mice did not contribute as many larval bloodmeals as might be expected13,14. The proportion of nymphal ticks that fed on mice ranged from 2 to 20% (median 10.5%) (Table 4). Our previous publication identified sites where the majority of ticks had fed on mice (Nantucket 2018, 100%, and Robin’s Island 2018 and 2019, 91% and 53%, respectively11), but DTV was not identified from these collections. Squirrels, or other Sciuridae, contributed ticks at only two sites (median host contribution, 1%). In contrast, shrews were common hosts at our study sites, with a median host contribution of 40.5% (range, 0–68%). The proportion of nymphal ticks that fed on shrews as larvae at a site was associated with the prevalence of DTV infection in ticks at that site (R2 = 0.44 p = 0.01, Fig. 3b), but not the prevalence of B. burgdorferi (R2 = 0.04, p = 0.5, Fig. 3a). DTV-infected nymphs were highly likely to had fed on a shrew (OR = 139, 95% confidence interval 42–456, but not a mouse, squirrel (or other Sciuridae) or other host (Fig. 4a). By contrast, B. burgdorferi-infected ticks were likely to have fed on mice, but not shrews (OR = 1.1, 95% confidence interval 0.6–1.9) (Fig. 4b). This excludes the hypothesis that shrews were found to have served as virus sources simply because these hosts were the dominant host in these sites.Table 4 The percentage of ticks at each site that tested positive for having fed on either a shrew (Soricidae), mouse (Peromyscus), squirrel (Sciuridae), or all other hosts tested (Odocoileus, Aves, Felis, Arvicolinae, or Lagomorpha).Full size tableFig. 4: The likelihood that an infected tick had fed on either a shrew, mouse, squirrel (or other Sciuridae), or other host.The data for deer tick virus-infected ticks are shown in panel a, and the data for B. burgdorferi-infected ticks are shown in panel b. Data are represented by boxplots of odds ratios (OR) with 95% confidence intervals, and all field sites are combined (n = 20 deer tick virus-infected ticks, n = 128 B. burgdorferi-infected ticks). A line is drawn at OR = 1, and any confidence interval that crosses it is not statistically significant. Sqrl= squirrel (or other Sciuridae).Full size imageThree B. brevicauda shrews were trapped from two of our study sites in September of 2020. DTV was detected in the brain of one shrew. Attempts to isolate virus by suckling mouse inoculation failed. Sequencing of two gene targets demonstrates greatest similarity to virus found in a tick from the same site (Fig. 2) and not to standard laboratory strains.DTV, like other tick-borne encephalitis viruses, may be perpetuated by three mechanisms15. Virus may be inherited by the tick, transovarial transmission16. We found that a greater number of ticks were associated with a specific host from all study sites than expected by vertical transmission, indicating that these ticks were not likely to have inherited the infection. There may be co-feeding or nonsystemic transmission in which an infected tick may serve as the direct source of infection for uninfected ticks attached to the skin around it, with no requirement for hematogenous viral dissemination17. Finally, horizontal transmission, in which a larval tick acquires infection from a viremic vertebrate host, requires a reservoir host that is susceptible to infection and allows for sufficient viremia to infect ticks as well as being sufficiently infested by the tick vector16. We focused solely on host-seeking nymphal ticks because they would only have one bloodmeal source, that of the larvae. Although adult ticks are also infected by DTV, they would have had two opportunities to become infected (a bloodmeal during the larval as well as the nymphal stage) and it would not be possible to determine whether the bloodmeal host that was identified from an adult was the source of the virus. Accordingly, we did not analyze adult ticks. Our analysis thus incriminates horizontal transmission between shrews and larval ticks, but we cannot exclude co-feeding transmission.Shrews (likely Blarina brevicauda, the most common shrew in our study sites; our retrotransposon assay, however, may also detect Sorex spp.) were the larval bloodmeal host for the majority (65%) of DTV-infected ticks. The infected ticks were collected from eight different sites over the course of three field seasons, indicating that the finding is not spatiotemporally specific. Although our sample size is small, the positive association between the proportion of shrew-fed ticks and the prevalence of DTV infection in ticks also supports a general finding; no association was found between DTV-infected ticks and either mouse-fed or Sciuridae-fed ticks. Finally, we detected virus in the brain of a shrew and find that it is genetically similar to virus within ticks from that site. Shrews are thus the main candidate for the vertebrate DTV reservoir but we cannot now rank the contribution of horizontal transmission relative to other modes of perpetuation. Shrews may be more likely to sustain an infectious viremia, or be more likely to simultaneously serve as host to nymphs and larvae (co-feeding), than the other mammals present in our study sites. Virus has been detected from xenodiagnostic ticks removed from skunks, raccoons and opossum in New York18. As with other tick-transmitted infections, contributions to the DTV enzootic cycle are likely to be dependent on local conditions and other hosts than shrews may contribute to maintenance. However, the association of shrews with DTV-infected ticks across multiple transmission seasons and across diverse sites, suggests that additional studies of shrews would be useful. Further investigations, including laboratory transmission studies are necessary to quantify the reservoir capacity of these hosts.Shrews have not previously been suggested as reservoir hosts for DTV or POWV, but they appear to be competent reservoirs for the related TBE virus in Eurasia19,20,21. When DTV was identified, white-footed mice were considered the likely reservoir given that these rodents maintained the tick-transmitted agents of Lyme disease, babesiosis, and human granulocytic anaplasmosis10,22. Shrews were considered to be poorly infested by ticks and thus were considered to have lower reservoir capacity for B. burgdorferi and B. microti23; this suggestion has been reconsidered24,25. Mammal surveys in DTV endemic sites have failed to detect virus or specific antibody in shrews18. Our use of host bloodmeal remnant analysis on infected ticks directly identified the source of the infecting animal reservoir without needing to extrapolate from indirect evidence such as comparative host density, tick infestation indices, and prevalence of pathogen exposure, and could be used to better understand the mode of perpetuation of other high consequence tick-borne pathogens such as the rickettsial agent of Rocky Mountain spotted fever, or those causing American tick-borne hemorrhagic fevers (Bourbon or Heartland virus). More

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Accumulation and distribution of N in flue-cured tobacco growing in different soilsAccumulation dynamics of N in different soilsNitrogen gradually increased in loam soil, clay loam, and sandy loam soils with plant growth (Fig. 1), attaining a maximum at the mature-plant stage(2.10 g/plant, 1.43 g/plant, and 2.90 g/plant, respectively). Nitrogen accumulation was lower in plants grown in clay loam than in plants grown in loam soil and sandy loam during the entire growth period, indicating that the N supply capacity of clay loam was relatively weak, and tobacco plants grown in this soil had the lowest levels of N uptake and utilisation. The N uptake and accumulation in flue-cured tobacco grown in loam soil and sandy loam were basically the same before the ceiling stage, but at the mature stage, N accumulation was significantly higher in plants grown in sandy loam than in plants grown in loam soil and clay loam (P  More

Author notesNina DombrowskiPresent address: Royal Netherlands Institute for Sea Research, Department of Marine Microbiology and Biogeochemistry, AB Den Burg, The NetherlandsKiley W. SeitzPresent address: EMBL Heidelberg, Meyerhofstraße 1, Heidelberg, GermanyThese authors contributed equally: Marguerite V. Langwig, Valerie De Anda.AffiliationsDepartment of Marine Science, University of Texas at Austin, Marine Science Institute, Port Aransas, TX, USAMarguerite V. Langwig, Valerie De Anda, Nina Dombrowski, Kiley W. Seitz, Ian M. Rambo & Brett J. BakerDepartment of Microbiology, Biomedicine Discovery Institute, Monash University, Clayton, VIC, AustraliaChris GreeningDepartment of Marine Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, USAAndreas P. TeskeAuthorsMarguerite V. LangwigValerie De AndaNina DombrowskiKiley W. SeitzIan M. RamboChris GreeningAndreas P. TeskeBrett J. BakerCorresponding authorsCorrespondence to
Marguerite V. Langwig or Brett J. Baker. More

Literature search and data retrievalWe performed a systematic literature search on the internet (Google Scholar, Web of Science) using the following keywords: [gut microbiota], [animal microbiome], [gut microbiome 16S] and [captive AND wild AND microbiota]. This search yielded 222 articles on animal microbiomes published between 2014 and 2020. The materials and methods of these articles were analysed to ascertain whether the study met the following criteria: (i) all wild and captive samples were processed using identical procedures, (ii) compared wild and captive animals were phylogenetically closely related (members of the same species or species complex), (iii) captive individuals were born in captivity, or no information was provided about the origin of the captive animals; i.e., wild animals brought into captivity and sampled some time later were excluded, (iv) captive animals that underwent a deliberate selection process (e.g. inbred mice or domestic animals) were also excluded for considering them genetically not comparable to the wild counterparts, and (v) only datasets with sample sizes over 12 individuals were considered for analysis. Raw data were extracted from the databases and repositories indicated in the articles (accession numbers listed in the “Bioinformatic resources”).Bioinformatic sequencing data processingDatafiles from the different studies were (i) stored at the University of Copenhagen’s Electronic Research Data Repository (ERDA), (ii) assigned a unique study identifier and (iii) re-processed in the Danish National Supercomputer for Life Sciences ‘Computerome2’ using a new bioinformatic pipeline we developed for processing data with different characteristics, including sequencing mode, read length and 16S rRNA gene fragment. The entire code can be found in the “Bioinformatic resources”. In short, for each individual dataset, we quality-filtered (mean phred score of q = 25) and (if necessary) trimmed and merged the paired-end reads based on the sequence overlap using AdapterRemoval224. Primers (if present) were trimmed using Cutadapt25, and reads were dereplicated with USEARCH Derep26 using a relative minimum copy number threshold of 0.01% of the total sequencing depth. Reads were then converted into zero-ratio OTUs using the denoising algorithm UNOISE327, and USEARCH was used to map the reads back to the OTUs and create an OTU table. HS-Blast28 was used to assign taxonomy against the non-redundant Silva 132 database29, and taxonomic assignments were filtered using different identity thresholds for each taxonomic level: 97% for genus-level taxonomy, 95% for family-level taxonomy, 92% for order-level taxonomy and 90% for higher taxonomic levels30. To minimise the impact of incorrectly assigned taxa, taxonomic annotations below these identity thresholds were converted into unclassified, and not considered in downstream analyses. This pipeline yielded relative read abundances assigned to different taxa for each individual dataset analysed.Data quality filteringIndividual data files generated by the aforementioned pipeline were aggregated by study and host species into genus-level abundance tables. The two datasets of Sarcophilus harrisii retrieved from two different studies were processed independently. Taxonomic resolution was limited to the genus level to maximise taxonomic annotation rate and minimise biases introduced by the different 16S rRNA gene markers employed in the analysed studies. On the one hand, wild animals’ microbial communities often contain taxa that do not map to any catalogued species with enough molecular similarity to assign species-level annotation. On the other hand, the analysed datasets were generated based on the V4, V3–V4 and V1–V3 regions of the 16S rRNA gene (Supplementary Dataset), which hindered comparability at the ASV or zOTU level. We then proceeded to quality-filter the genus-level abundance tables of each species through filtering individuals by minimum sequencing depth, minimum diversity coverage and taxonomic annotation. Only individual datasets with more than 1000 reads and diversity coverage values over 99% were retained, and final genus-level abundance tables that contained at least five animals in each contrasting group were considered for analysis. Since the studied datasets contained traces of dietary items and host DNA, read counts assigned to taxonomic groups not assigned to Bacteria genera, or not present in the LTPs132_SSU release of the SILVA Living Tree (https://www.arb-silva.de/projects/living-tree) used for measuring the phylogenetic relationships among bacteria, were removed to ensure accurate measurements of phylogenetic diversities. In the cases where one group (either wild or captive) outnumbered the other, samples were randomly selected to ensure even sample sizes.Diversity and compositional analysesDiversity and compositional analyses were carried out in the R statistical environment v.3.6.331 and Python 3.8 based on the Hill numbers framework. The operations explained below were conducted using the R packages ape32, dendextend33, dmetar34, hilldiv35, meta36, metamicrobiomeR37, phylosignal38, phytools39, treedist40, vegan41, and the python package qdiv42. Hereafter functions and their respective packages are displayed as ‘package::function’. Statistical significance level was set at a FDR-adjusted p-value of 0.05. All charts and figures in the manuscript were originally generated either in R (full code of all original figures is included in “Bioinformatic resources”) and subsequently modified in Adobe Illustrator to achieve the desired layout without distorting the dimensions of the quantitative elements.Hill numbersThe Hill numbers framework encompasses the group of diversity measures that quantify diversity in units of equivalent numbers of equally abundant taxa43,44—in our context bacteria genera. Hill numbers provide a general statistical framework that is sufficiently robust and flexible to address a wide range of scientific questions that molecular ecologists regularly try to answer through measurement, estimation, partitioning and comparison of diversities45. To obtain a complete vision of the gut microbiome differences between wild and captive animals, we conducted all our diversity and compositional analyses based on three contrasting Hill numbers based metrics: the so-called dR, which only accounts for richness (i.e., order of diversity 0, whether bacteria taxa were present or not), dRE which considered Richness + Evenness of order of diversity 1 (i.e., the relative abundances of bacteria are proportionally weighed) and dRER, which considered Richness, + Evenness + Regularity (i.e., the phylogenetic relationships among bacteria are accounted for). Detailed explanations of these metrics can be found elsewhere17,46,47.Phylogenetic treesThe dRER metric required a Bacterial phylogenetic tree to compute the relatedness among bacterial taxa. As our datasets contained different fragments of the 16S rRNA gene, and thus we were unable to generate a phylogenetic tree directly from our DNA sequence data, we relied on the SILVA Living Tree, and used the LTPs132_SSU release as the reference phylogenetic tree. In addition, the time-calibrated host phylogeny required by the host phylogenetic signal and phylosymbiosis analyses was generated using Timetree48.Diversity metrics and meta-analysisWe computed individual-based diversity metrics using the function hilldiv::hill_div, and obtained average alpha diversity metrics per species, as well as wild and captive populations per species. We used a Kruskal–Wallis (KW) test as implemented in the function hilldiv::div_test to ascertain whether the mean diversity values varied across analysed host species, and a PERMANOVA (PMV) test using vegan::adonis function based on the pairwise dissimilarity matrix to test whether host species were compositionally distinct.Average alpha diversity metrics of wild and captive populations per species were used to conduct a random-effects-model (REM) meta-analysis with raw effect sizes using the function meta::metacont. We used the Sidik–Jonkman estimator for the between-study variance and the Knapp–Hartung–Sidik–Jonkman adjustment method. The overall effect was calculated using Hedge’s g (SMD) and its 95% confidence interval and p-value. An identical analysis was performed for the entire dataset and two representative subsets of five species, containing only datasets derived from primates and cetartiodactylans. Higgin’s and Thompson’s I2 test, Tau-squared T2 and Cochran’s Q were used for quantifying the heterogeneity between the included species. Due to the high heterogeneity found in the study, we evaluated whether the between-study heterogeneity was caused by outliers with extreme effect sizes, which could be distorting our overall effect. We defined an outlier if the species’s confidence interval did not overlap with the confidence interval of the pooled effect using dmetar::find.outliers function.The function detected three outliers in dR metric (GOGO, PEMA and TUTR), four in dRE (GOGO, PEMA, MOCH, EQKI) and seven in dRER (RHBR, PYNE, PEMA, TUTR, MOCH, CENI and AIME). Even when these outliers were excluded from the analysis the I2 heterogeneity value was substantial for dR (I2 from 79.3 to 70.3%) and moderate for dRE (I2 from 80.1 to 60.0%) and dRER (I2 from 86.9 to 54.2%) and significant for both (Cochran’s Q, p-value  More