dataesr/scientific-paragraphs-categorization huggingface dataset:
p. i. after high-dose injections of viable spores (Fig. 5F, G ). Hence, mucormycete spores Larvae were observed for phagocyte clusters around injected viable and non-viable mucormycete spores and spore localization at 24 h. p. i. Larvae with spores outside the initial infection site were considered to show dissemination. Pooled data from three independent experimental repeats are presented. Fig. 4. Mucormycete spores induce differential pro-inflammatory cytokine expression dependent on spore viability in the hindbrain infection model. Initial mRNA levels for both cytokines were low at 6 h. p. i. (A) but increased after 24 h. p. i. (B) for infections with live spores. At 24 h. p. i. , high-dose infection with viable spores induced significantly higher mRNA levels than infection with a low dose of viable spores and non-viable spores.
Images were collected and processed using Fluoview imaging software (Olympus) and Photoshop (Adobe Systems Inc. , San Jose, CA, USA). Time-lapse imaging was performed over 12 h time periods, with Z-stack images taken every 5 min and movies generated with ImageJ. Figures represent single-slice differential interference contrast channel (DIC) and maximal projection of fluorescent image channels (UV, green, red) either independently or overlaid. The number of slices for each maximal projection is specified in individual figure legends.
qPCR reactions were carried out using a CFX96 thermocycler (Bio-Rad, Berkeley, CA, USA) with the primers detailed in Table  S2  and the following conditions: 3 min at 95°C, followed by 40 cycles of 5 s at 95°C and 20 s at 58°C; a final dissociation curve between 65 and 95°C was performed for each group. Threshold cycles (Ct) and dissociation curves were analysed with Bio-Rad CFX Manager software. Gene expression levels were normalized to zebrafish gapdh (ΔCt) and compared with PVP-injected controls (ΔΔCt). Results are presented as fold induction (2 ΔΔCt ;  Gratacap et al. , 2013).
Acknowledgements We would like to thank  S. C. Chan Lee  for providing the M. circinelloides strain,  G. Lieschke  for the transgenic zebrafish strain Tg(mpeg1:Gal4-FF) gl25 and  A. Planchart  for quantitative real-time PCR primer sequences. This article presents independent research funded by the  National Institute for Health Research (NIHR) Surgical Reconstruction and Microbiology Research Centre , and the views expressed are those of the authors and not necessarily those of the  NHS , the  NIHR  or the  Department of Health.
Data analysis and statistics All data were collected from at least three independent experimental repeats. Exact numbers of experimental repeats and larvae investigated are given in each results section or figure legend. Data were analysed with GraphPad Prism version 6. 0 software (GraphPad, La Jolla, CA, USA). Statistical analysis of survival curves ( pooled data from three independent experimental repeats) was performed using Mantel-Cox log-rank test. Cytokine responses were analysed by two-way ANOVA with Tukey's multiple comparisons test. Data from CFU counts, macrophage ablation and phagocyte recruitment were compared using the non-parametric Kruskal-Wallis test with Dunn's multiple comparison test. Categorical data for phagocyte clustering and dissemination were compared by two-tailed Fisher's exact test.
Classification of MafB proteins based on their N-terminal domain. Sequence alignment of MafB proteins revealed that MafB has a N-terminal conserved domain of unknown function named DUF1020 (or PF06255 in PFAM Database) and a Cterminal (CT) variable region (Fig. 1C ). DUF1020 is restricted to Neisseria genus and is preceded by a signal peptide domain (approximately 25 residues long). Amino acids alignment of 150 sequences of MafB proteins (see details in Materials and Methods section) revealed 3 classes of MafB with 3 conserved N-terminal regions (Fig. 1C ). The percentage of sequence identity within Nterminal conserved regions of MafB proteins belonging to the same class is over 85% (90%, 97% and 89% for class 1, 2, 3, respectively) whereas the percentage of sequence identity between the conserved regions of different classes is approximately 35%.
). All the MafB sequences, that we analyzed using the SignalP 4. 1 program  [36]  showed a signal peptide (SP) recognized by type 1 signal peptidase. For example in NEM8013, MafB MGI-1NEM8013 and MafB2 MGI-2NEM8013 harbor a predicted cleavage site between amino acid residues 27 and 28, MafB1 MGI-2NEM8013 between amino acid residues 24 and 25 and MafB MGI-3NEM8013 between amino acid residues 26 and 27. Despite the presence of two consecutive arginine residues in the signal-peptide sequence of class 1 MafBs, PRED-TAT  [37] , TatP 1. 0  [38]  or TATFIND 1. 4  [39]  programs did not predict a putative tat (twin arginine translocation) signal. Thus, the translocation of the 3 classes of MafB proteins through the inner membrane is likely to occur via the Sec pathway. In most cases, the variable domain of MafB shares no homology with protein of known function.
Only in few cases a homology with CT extremities of polymorphic Cdi and Rhs toxins family can be noticed. However, in contrast with Cdi and Rhs toxins no repeat domains are found in the N-terminal region of MafB. Gene content of the different maf genomic islands. In addition to the mafB-mafI module, a mafA gene is frequently found immediately upstream of genes encoding class 1 and class 3 MafB but not upstream of genes encoding class 2 MafB (Fig. 1A, 1B ). A BLASTp search using MafA sequence as a query evidenced that mafA is also specific of the Neisseria genus. The LipoP 1. 0 server  [40]  predicts that MafA exhibits a lipoprotein signal peptide without an aspartic acid in position +2 after the cleavage site and thus could be a lipoprotein attached to the outer membrane. It has been shown by immunoelectronic microscopy  [35]  that MafA of gonococcal strain MS11 is surface exposed and is able to bind to glycolipids  [35].
There are two types of mafB-CT genes: i) mafB-CTs starting with an initiation codon, which are potentially translated, and ii) mafB-CTs devoid of initiation codon (ATG, GTG, TTG, ATT or CTG), which are potentially silent cassettes. Nucleotide comparison of MGI-1 (A), MGI-2 (B) and MGI-3 (C) from meningococcal strains Z2491, MC58 and NEM8013, gonococcal strains FA1090 and NCCP11945 and N. cinerea strain ATCC 14685. Genome comparisons were generated using BLASTn implemented in Easyfig 2. 1 with a cutoff value of 80%. Grey vertical blocks indicate regions of shared similarity shaded according to BLASTn identity. The level of nucleotide identity is shown in the gradient scale for each MGI. Genes are indicated with arrows colored according to their predicted functions with the color code illustrated in the legend.
In support of this hypothesis, when two mafB-CTs are almost identical their associated mafI gene are also identical. For instance, the C-terminal region of MafB MGI-3Z2491 (NMA0324) and a cassette (NMB2107) found in MGI-3 of MC58 exhibit 100% amino acid identity (Fig. 2C ). The corresponding immunity proteins MafI MGI-3Z2491 (NMA0323) and NMB2108 are also 100% identical (Fig. 2C ). Similarly, MafB MGI-3FA1090 (NGO1971) and a cassette (NMV_2314) found in MGI-3 of NEM8013 exhibit 90% amino acid identity (Fig. 2C ). The corresponding immunity proteins MafI MGI-3FA1090 (NGO1970) and NMV_2315 are 100% identical (Fig. 2C ). A search in the Conserved Domain Database (CDD)  [44]  of the NCBI revealed that many mafI genes encode proteins containing domains typically found in putative immunity proteins of bacterial polymorphic toxin systems  [1, 2].
MafB MGI-1NEM8013 is a bacterial EndoU nuclease that can degrade multiple sources of RNA. Previous bioinformatics studies conducted by Zhang et coll. (in Supplementary material of  [2] ) suggested that MafB2 MGI-2Nm053442 (encoded by gene NMCC_0602 GI: 161869586) could contain a nucleasic toxin domain of the EndoU fold. Amino acid sequences comparison between MafB MGI-1NEM8013 and MafB2 MGI-2Nm053442 showed 95% identity over the entire length and 100% identity over the CT region. A search in the GenBank database with the BLASTp program using the CT domain of MafB MGI-1NEM8013 protein as a query confirmed the presence of a putative EndoU catalytic domain with two conserved histidine residues (Fig. 6A ). First, we examined total RNA from E. coli cells expressing MafB MGI-1NEM8013 for evidence of RNA degradation.
Filters recovered after overnight incubation were used to perform viable counts and the competitive index was calculated as the inhibitor/target ratio in the output divided by the initial inhibitor/target ratio. The data from three independent experiments were examined for significance using a two-tailed Student's t-test. * p-value p,0. 05. doi:10. 1371/journal. ppat. 1004592. g004 MafB MGI-1NEM8013 led to the non-specific cleavage of most cellular RNAs (Fig. 6B ). To further characterize MafB MGI-1NEM8013 ribonuclease activity, we purified a recombinant His 6 -tagged-MafB MGI-1NEM8013 protein and incubated it with various nucleic acids substrates. Recombinant MafB MGI-1NEM8013 was able to degrade purified total RNA from E. coli, N. meningitidis and human epithelial cells (Fig. 6C ). Thus, MafB MGI-1NEM8013 is a ribonuclease.
Discussion In this study, we have demonstrated that mafB encodes a functional polymorphic toxin and mafI, the downstream gene, a specific immunity protein. Focusing on MafB MGI-1NEM8013 , we were able to characterize its EndoU ribonuclease activity. Toxins carrying EndoU activities are predicted to be widespread among diverse polymorphic toxin systems, however no EndoU activity carrying toxins had been previously experimentally confirmed in bacteria. Polymorphic toxins encompass numerous families distributed in all bacterial lineages. Nevertheless, very few of these systems have been experimentally characterized except the Cdi and the Rhs families in Gram-negative bacteria  [6, 8]  and the PF04740 family from Gram-positive bacteria  [48]. CdiA and Rhs toxins are the best characterized and can be found in many species including E.
Bioinformatic analysis We used 6 fully sequenced and annotated genomes of N. meningitidis (Z2491, MC58, FAM18 and NEM8013) and N. gonorrhoeae (FA1090 and NCCP11945) present in the MicroScope database with curated annotations from the NeMeSys project  [19, [76] [77] [78] [79] [80]. We also used N. gonorrhoeae MS11 genome sequence available from the Broad Institute, N. cinerea ATCC 14685 genome sequenced by Washington University and the following meningococcal strains: H44/76  [81] , M04-240196  [82] , M01-240355  [82] , G2136  [82] , M6190  [82] , NZ-05/33  [82] , WUE2594  [83]  and 053442  [20]. We used BLASTp with default parameters to search for orthologs of the Maf proteins in other Neisserial strains present in the NCBI non-redundant protein database. To identify the different classes of MafB proteins, we downloaded 150 sequences stored on the PFAM database server  [84]  that have been used to build the DUF1020 (PF06255) family.
The alignment was performed with ClustalW (http://www. genome. jp/tools/clustalw/) [85, 86]  using default parameters and a rooted phylogenic tree (UPGMA) was generated. Multiple alignments were also performed using MUSCLE  [87]  or Clustal Omega  [88]  with default parameters and shaded using the BoxShade server (http://www. ch. embnet. org/software/ BOX_form. html). The LipoP 1. 0 server  [40]  and SignalP server 4. 1  [36]  was used to predict the presence of a signal sequence with default options for Gram-negative bacteria. PRED-TAT  [37] , TatP 1. 0  [38]  or TATFIND 1. 4  [39]  servers were used to identify putative tat (twin arginine translocation) signal. b-barrel structure prediction were performed with TBBpred server  [59]  and HMM-TM server  [60]  and conserved domains were identified with the Conserved Domain Database (CDD)  [44]  of the NCBI server.
10 ml of mixed cultures were also used for determination of the inhibitor/target initial ratio by plating on GCB with appropriate antibiotics. Filters recovered after overnight incubation were used to perform viable counts and the competitive index (CI) was calculated as the inhibitor/target ratio in the output divided by the initial inhibitor/ target ratio. The data from three independent experiments were examined for significance using a two-tailed Student's t-test. A pvalue p,0. 05 was considered significant.
2 ). The gentle slope of the species accumulation curve in Fig. 1b  suggests that there is little turnover in dominant crop-visiting species between years, crops and locations, mainly because within biogeographical regions, a small number of species tend to dominate the crop-visiting bee communities everywhere (Supplementary Table  2 ). The commonness of crop-visiting bee species. To test the hypothesis that the species providing the majority of the pollination services are generally regionally common species, we use two lines of enquiry. First, we examined the contribution of threatened bee species to the set of bee species found on crops. Four of the countries we studied have compiled Red Data books for bees, which we used to objectively identify threatened species. In these countries, on average 44% of the bee species are threatened, but in the 19 studies carried out in these countries only 12 threatened species were found accounting for 0.
3% (s. e. 0. 1%) of the individual bees observed on crops. Second, we determined whether the dominant crop-visiting bee species are common in agricultural landscapes generally, using an independent data set of bee communities in 264 sites in agricultural landscapes in Europe and North America (see Methods section). These studies compared bee communities in agricultural habitats such as arable fields (but not flowering, bee-pollinated crops), grasslands, old fields and hedgerows with bee communities in nearby sites that are actively managed for biodiversity enhancement (for example, agri-environment schemes and wildflower plantings) (Supplementary Fig. 1 ; refs 17,24). We used only the agricultural habitat controls to evaluate the frequency of dominant cropvisiting bee species (listed in Supplementary Table  3 ) in these 'background' agricultural habitats.
Methods Data sets to study crop visitation by bees. Our data sets record the relative visitation rate of bees to crop flowers, which is a good proxy for the relative contribution to pollination service delivery (see next section). We used data from 90 studies and 1,394 crop fields around the world that used standardized protocols to examine the abundance and identity of wild bees visiting flowers of 20 different crops that depend on bee pollinators for maximum yield (Supplementary Fig. 1  and Supplementary Table  1 ). We determined species abundance distributions of wild bee communities on insect-pollinated crops by pooling data within studies, that is, from fields sampled in the same year, region and crop species. We only included studies that directly observed individual bees on crop flowers, all individuals to species level and that were based on data from at least four fields that were 1 km or more apart.
The crops included are watermelon (5 years), tomato (2 years), cranberry (2 years) and blueberry (2 years), such that overall we analysed 11 crop-year combinations. Each annual data set was treated separately because different sites were studied in different years, and also because pollinator populations can fluctuate considerably among years. Each crop data set included extensive data on single-visit pollen deposition, a common metric used to assess per-interaction efficiency  46  (watermelon 302 single-visit pollen deposition experiments conducted with virgin flowers, cranberry 176 experiments, blueberry 100 experiments and tomato 66 experiments; for methods details see refs 48-50). Because our data on per-visit pollen deposition were resolved only to the level of species groups, we combined our visitation data into the same groups to avoid biasing our analyses with respect to the variance contributed by the visitation as compared with the pollen deposition factors (see below).
At least one known nectar robber (Xylocopa virginica) was included in several of our data sets. This would tend to increase the importance of per-visit deposition, and decrease the importance of visitation, in driving total pollination, which is a bias against the assumption tested here. We calculated total pollination as visitation multiplied by per-visit pollen deposition, as is generally done in the literature  47  , and then examined the Pearson correlations between each of these three values. Values of Pearson's r between visitation and total pollination were high (mean ¼ 0. 87; Supplementary Table  4 ). Although our methodology for estimating total pollination as the product of visitation and per-visit deposition makes such a correlation likely, it does not constrain it to be the case.
Crop-visiting bee species relative to regional species pool. Conservation policy objectives are often formulated at national or even continental levels. We therefore also explored how the number of bee species encountered in our studies compared with the total number of unique bee species existing in the political territories in which the studies were performed (that is, the regional species pool). We used a database compiled from published and unpublished sources by J. S. A. of all described bee species currently known to exist in each country, state or province (that is, at the lowest territorial level for which such lists could be obtained). We obtained these data for the German federal states of Hessen  55  , Lower Saxony 56 and Bavaria  57  , and for the European countries of France, Great Britain, Hungary, Israel, Italy, Netherlands and Sweden (from ref.
58). In North America, species lists were obtained from ref. 58, for the US states California (CA), Massachusetts, New Jersey (NJ), New York, Pennsylvania and Virginia, and the Canadian province of British Columbia. Elsewhere in the world, species lists were used from ref. 58 for Chiapas (Mexico), Costa Rica, Minas Gerais (Brazil), New Zealand, South Africa and Sulawesi (Indonesia). We subsequently calculated straight-forward sample-based species accumulation curves using EstimateS software  59  , treating each territorial species list as a sample. Because each species list is not an ecological sample but is based on collections, revisions, faunal surveys and national inventories, we refrained from calculating a true species richness estimator. To examine what proportion of the regional bee species pool visited crop flowers, and what proportion of them was dominant in at least one study, we similarly generated species accumulation curves for (dominant) crop-visiting bee species.
NATURE COMMUNICATIONS | DOI: 10. 1038/ncomms8414 The contribution of threatened species to crop visitation. To examine what proportion of the bee communities observed on crops had a recognized threat status, we used Red Data Books. Red Data Books were only available for four of the countries from which we had data of crop-visiting bee species: Germany 61 , Netherlands  62  , Sweden  63  and United Kingdom  64. In total, 19 separate studies had been carried out in these countries for which we calculated the per study mean pooled proportion of individuals from threatened species. Data sets to study commonness and effects of conservation. To address the hypotheses that dominant crop-visiting bee species are generally common species and that these species can be easily enhanced by simple management actions, we used data from a number of European and North American studies examining the effects of measures to promote biodiversity in agricultural areas.
In each field, all samples were taken along two 95-m-long transects: one along the field edge and another, parallel to the first one, 50 m from the edge in the grassland interior. We sampled bees using sweep nets (60 sweeps per transect per round) and transect surveys (15 min sampling per transect per round) in the edge and interior of the fields three times (May, June and July) in 2003. For analyses, all data per field were pooled. In the United States, unpublished 2012 data were used from two studies in CA, one in NJ and one in Michigan (MI). Biodiversity-enhancing management involved establishment of hedgerows of native perennial plants (study CA1) or establishment of wildflower plantings (studies CA2, NJ, MI). In contrast to the European studies, experimental sites in the United States were generally located adjacent to agricultural fields on pre-existing field edges or old fields.
All bees visiting flowers within 1 m of the transect were collected. In all three studies, each site was sampled four times throughout the summer. Again, for analyses, all data per site were pooled. Analysing commonness in relation to semi-natural habitat. To examine whether dominant crop-visiting bee species are common species in agricultural landscapes, generally (hypothesis 2) only data from the control sites were used because they were situated in agricultural habitats such as arable fields (but not flowering, bee-pollinated crops), grasslands, old fields and hedgerows. The proportion of the bee communities consisting of individuals from bee species dominating crop vistitation rates (Supplementary Table  3 ) were then calculated. The units of analysis were averages of multiple fields, as sample size per site was too low to yield reliable estimates of the relative contribution of dominant species to the bee community.
For the European sites, we used CORINE Land Cover 2006 data sets 67 (all land use classes with codes starting with 3 or 4) which, although less accurate than national data sets, provide spatially consistent land cover classifications across all countries. In NJ, land cover data sets provided by the State Department of Environmental Protection were used (http://www. nj. gov/dep/ gis/lulc07cshp. html). In MI, land cover was manually digitized from 2012 National Agriculture Imagery Program orthoimagery at the 1:2,000 scale (United States Department of Agriculture Geospatial Data Gateway, http:// datagateway. nrcs. usda. gov/). The other two US studies used the National Agricultural Statistics Service crop data file (http://nassgeodata. gmu. edu/ CropScape/). We used standard multiple linear regression models to relate the proportion of individuals from dominant crop-visiting species in bee communities to the proportion of semi-natural habitat, thereby correcting for latitude and continent.
Plotting residuals versus fitted values confirmed that model assumptions were met satisfactorily. The often used arcsine transformation of proportional data or binomial regression increased heteroscedasticity, and we therefore present the results of untransformed data. To subsequently explain the patterns in the proportional data, we calculated standardized abundances of dominant cropvisiting bees and, separately, for all other bees for each of the European study regions by dividing the per region bee abundance by the mean abundance across all 15 regions. Since the study in each region had used exactly the same survey protocol, a standardized bee abundance 41 indicates above-average bee abundance compared with the cross-study mean, and a value o1 indicates a below-average bee abundance. We similarly calculated standardized abundances of dominant crop-visiting bees and, separately, all other bees for the three US studies that used the same survey protocol (study CA1 used a different survey protocol and was excluded from this particular analysis).