dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS We thank  Dr. Selma Masri  ( UCI ) and fellows in the  Sassone-Corsi and Hawley laboratories  for discussions. We gratefully acknowledge the technical assistance from  Dr. Andrew Garnham ,  Mr. Marcus Callahan ,  Dr. Donny Camera  and  Dr. Jamie Whitfield  ( ACU ). We thank the participants for their time and commitment to completing the challenging study protocols. This research was supported by funding to JAH and  PSC  from  Novo Nordisk Foundation   Challenge Grant  ( NNF14OC0011493 ). The funding source had no involvement in research conduct, article preparation, study design, data collection, analysis or interpretation, or in the decision to submit for publication.
, 2017; Schultz and Brady, 2008; Mueller et al. , 1998). To explore nutritional mechanisms underlying putative mutualistic functions of these bacteria, and their association with changes in scale of farming over evolutionary time, we isolated and sequenced the EntAcro1 and EntAcro10 symbionts  (Sapountzis et al. , 2015; Zhukova et al. , 2017). We subsequently compared their draft genomes with ten published genomes of Mollicutes associated with insect hosts having specialized diets, as well as with several other Mollicutes genome-sequences to assess enrichments and losses of gene categories and metabolic pathways. For EntAcro1, where the genomic data suggested the most advanced mutualistic functions, we measured expression levels of bacterial transporter genes related to the decomposition of plantderived compounds and ant genes related to the uptake of exogenous acetate, an end-product of Mollicutes' anaerobic metabolism.
Substrate utilization and reconstruction of metabolic pathways We restricted our comparative evaluations and hypotheses testing to the Spiroplasma and Mesoplasma symbionts associated with insect hosts (the attine symbionts highlighted in dark yellow in Figure  1  and the ones in between) and plants (M. florum). Functional annotations (eggNOG database) showed that strains clustered primarily based on metabolic genes and secondarily according to shared identity for informational genes (transcription, translation and recombination/repair processes), which both correlated with host associations (Figure  1 -figure supplement 3; Figure  1 figure supplement 3-source data 1). Functional gene-similarities were confirmed by Mantel tests showing that the Euclidean dissimilarity matrix of orthogroups was more strongly associated with phylogenomic distances between insect hosts (r 2 = 0.
Metabolic pathway reconstructions were consistent with EntAcro10 being a less specifically adapted symbiont than EntAcro1 (Figure  2 ). Inferences of this kind, based on direct similarity between bacterial genomic databases, may not be fully accurate because gene-families encoding metabolic transporters evolve rather rapidly so the actual transported substrates may no longer be identical. However, the draft genomes that we obtained were sufficiently complete to provide reasonable confidence for reconstruction of operational metabolic transporters through the plasma membrane, the associated metabolic pathways inside the bacterial cells, and the metabolic end-/byproducts involved (Figure  2 ). We found that EntAcro10 can utilize glycerol from ant host cells and monosaccharides which are likely derived from fungus-garden metabolites or juices (glucose/fructose) ingested during foraging (Figure  2 ).
2. 11  (Carver et al. , 2010). The assembled contigs were further checked for errors using the Reapr v1. 0. 18 software  (Hunt et al. , 2013)  and contigs that had less than 9x coverage or were smaller than 250 bp were removed. The final set of assembled contigs Supplementary file 1) was deposited in the NCBI Genome submission portal under accession numbers SAMN06251630 and SAMN06251631. Genes for each contig were predicted using the RAST annotation server  (Aziz et al. , 2008)  after which predicted amino acid sequences were compared to a local database Uniref100 using BLASTP v2. 2. 28+ (evalue <1e-15, percentage identities > 30%), and top matches with the assembled contigs were phylogenetically binned (Supplementary file 1). This grouped the contigs belonging to Mollicutes together in what we refer to as 'A' bins allowing further evaluation of the strain-specific RAST annotations  (Sima ˜o et al.
, 2015). We functionally annotated the protein sequences using a standalone version of InterproScan v-5. 17-56. 0 with SUPERFAMILY, Pfam, ProSiteProfiles, Coils, ProSite-Patterns, TIGRFAM, Hamap and ProDom  (Jones et al. , 2014)  and Phobius  (Ka ¨ll et al. , 2007)  (http:// www. cbs. dtu. dk/services/SignalP/ and http://phobius. sbc. su. se/) to predict signal peptide and transmembrane domains. To identify and compare metabolic pathways we used the KAAS tool  (Moriya et al. , 2007)  provided by the KEGG database  (Kanehisa and Goto, 2000; Kanehisa et al. , 2010)  together with the BLAST algorithm and the single best hit (SBH) procedure with default settings.
Phylogenomic analyses For the phylogenomic reconstructions, we first downloaded all 176 available Mollicutes genomes that were present in the Ensembl database (page accessed in January 2016;  (Yates, 2016) ) and created a merged customized BLAST database, which we used to compare the predicted amino acid proteins of EntAcro1A and EntAcro10A. We included only complete genomes and thus excluded the partially sequenced Entomoplasma melaleucae genome. The  BLAST comparisons (Altschul et al. , 1990)  using an e-value of 1e-15 as cutoff and a percentage identity of 30%, revealed clear similarities between our two EntAcro symbionts and 59 previously genome-sequenced Mollicutes strains. We therefore used their genome sequences to define the orthologous single-copy protein-coding genes using the Orthofinder software  (Emms and Kelly, 2015)  which resulted in 73 genes being available for phylogenomic analyses.
Both the nucleotide and amino acid sequences of these genes were extracted for each of the two EntAcro symbionts, which allowed the construction of gene-specific alignments using MUSCLE v3. 8. 31  (Edgar, 2004). These alignments were subsequently refined using the trimAl software, which removed all positions with gaps in 10% or more of the sequences unless this left fewer than 50% of the original sequences  (Capella-Gutierrez et al. , 2009). The filtered alignments were further tested for recombination using the Phipack software  (Bruen et al. , 2006)  and for nucleotide saturation using the Xie test implemented in DAMBE5  (Xia and Xie, 2001). For the 65 genes that remained (Supplementary file 2) individual alignments were concatenated using Amas 0. 98  (Borowiec, 2016)  and the appropriate substitution models for the nucleotide and protein alignments were selected after testing them with jmodeltest v2.
1. 7 and prottest v3. 4  (Darriba et al. , 2011). We used the nucleotide and amino acid alignments for the two EntAcro symbionts and the other 59 Mollicutes genomes to reconstruct phylogenomic trees with maximum likelihood (ML) and Bayesian methods. For the ML analyses we used the RaxML software v. 8. 2. 10 (  Stamatakis et al. , 2008)  with specified partitions of the concatenated alignments, which produced 65 partitions (one for each gene) using the GTR model with invariable sites for the nucleotide alignment and the LG + IG model for the amino acid alignments  (Le and Gascuel, 2008)  after 1000 bootstrap sampling replications. For the Bayesian inferences, we used the MrBayes v3. 2. 6 software  (Ronquist et al. , 2012)  applying the same nucleotide and amino acid models as above. The concatenated alignments were specified for each gene-specific partition and a variable rate of sequence evolution (ratepr) was allowed for each of them.
Comparative genomics We used the bactNOG database v4. 5 (page accessed April 2016) to find clusters of orthologous genes and to compare the predicted proteins with HMMER v3. 1. b1 (Eddy, 2011; Huerta-Cepas et al. , 2016). To obtain specific comparisons among Mollicutes genomes based on different numbers of genes assigned to distinct functional categories via bactNOG, we obtained ratio estimates for the number of genes in each functional category by counting the number of genes assigned to specific orthogroups and dividing by the total number of annotated genes (proportional abundances), and used these data as input for Principal Component Analysis (PCA) using the 'stats' package in  RStudio (v. 1. 0. 136). We used Mantel tests to compare phylogenomic distances based on orthologous genes for insect hosts or bacterial symbionts with the overall genome dissimilarities.
sp. , Culicoides arakawae instead of Atrichopogon, and Solenopisis geminata instead of Ac. echinatior and Ap. dentigerum  (Ward et al. , 2015). A host phylogeny for our matrix comparisons was then reconstructed using the selected mitochondrial genome sequences retrieved from NCBI (Accession numbers available in Supplementary Material of  (Song et al. , 2016 ) using MUSCLE v3. 8. 31 (Edgar, 2004)  and FastTree v1. 0  (Price et al. , 2009). The final Mantel tests were performed in R using 10,000 permutations, which produced correlations between: 1. the functional annotation matrix and the bacterial phylogeny matrix, 2. the functional annotation matrix and the host phylogeny matrix, and 3. the host and the bacterial phylogeny matrices.
, St. Germain en Laye, France) on the Mx3000P system (Stratagene, Santa Clara, CA, USA). Reactions took place in a final volume of 20 ml containing 10 ml buffer, 8. 3 ml sterile double-distilled water (ddH 2 O), 0. 4 ml of each primer (10 mM), 0. 4 ml ROX standard, and 0. 5 ml template cDNA. PCR conditions were as follows: denaturation for 2 min at 94 ˚C, followed by 40 cycles of 30 s at 94 ˚C, 30 s at the annealing temperature (see Supplementary file 5 for primers used), and 30 s at 72 ˚C, followed by dissociation curve analysis. All quantitative PCRs (qPCRs) were replicated and each run included two negative controls with no added template. To generate the delta values for the qPCR analyses, we used the fold change method, based on a standard curve with PCR products in a tenfold dilution series of known concentrations to calculate the PCR efficiency of each primer pair using the REST software  (Pfaffl, 2002).
Collection of forage substrates harvested by colonies in the field Foraging substrate preference data were collected in the field from nine attine ant species (T. cornetzi,  T. zeteki, S. amabilis, C. costatus, C. longiscapus, C. rimosus, Myc. smithii, Myr. ednaella and Ap. dentigerum)  in Gamboa, Panama and represent 101 hr of observation time on 103 colonies (T. cornetzi (n = 48), T. zeteki (n = 12), S. amabilis (n = 6), C. costatus (n = 8), C. longiscapus (n = 5), C. rimosus (n = 8), Myc. Smithii (n = 14) and Ap. dentigerum (n = 2). Colonies were located and marked in several field sites within lowland Panamanian rainforest near Gamboa after placing polenta baits in the leaf litter and then tracking workers back to their nests when vouchers of workers were collected in EtOH to allow identification.
Similar data were extracted from a recently published study conducted at the same field site at the same time of year (May) but focusing on six leaf-cutting ant species  (At. colombica, At. cephalotes, At. sexdens, Ac. echinatior, Ac. octospinosus and A. volcanus)  in Gamboa, Panama  (Kooij et al. , 2014a). Merged datasets were normalized by converting all observations to total observed counts in one hour. To visualize foraging substrate preferences across the attine ant species, we converted the count data to proportions and performed an unscaled Principal-Component Analysis (PCA) in R using the 'ade4' package. To independently verify the statistical significances obtained, we used the mean of the replicated count values per colony. We then fitted the normalized hourly count data in a zeroinflated negative binomial (ZINB) regression model with the function 'zeroinfl' in the package 'pscl'  (Kleiber et al.
, 2008). ZINB regression is typically used for true count variables to model positivelyskewed data with an abundance of zeros and it fitted our data better than a zero-inflated Poisson or a negative binomial generalized linear model (GLM) without zero-inflation when we compared them using the Akaike Information Criterion (AIC) and the Vuong's non-nested test ('vuong' function in 'pscl' package). We used the absolute itemized foraging substrate preference values as response variable and the interaction of the foraging substrate types (i. e. leaves, fruits, flowers, seeds, insect frass and other) and phylogenetic group (i. e. leaf-cutting versus non-leaf-cutting ants) as fixed categorical variables. We conducted Tukey's HSD post hoc tests for each substrate type between leafcutting and non-leaf-cutting using the 'lsmeans' package  (Lenth, 2016).
We used Mantel tests to compare differences in absolute EntAcro1 or EntAcro10 abundances (calculated for each species using the qPCR data) with the overall dissimilarities in their foraging substrate preferences. We used only data from the 12 attine ant species which were common in both datasets  (At. colombica, At. cephalotes, At. sexdens, Ac. echinatior, Ac. octospinosus, T. cornetzi, T. zeteki, S. amabilis, C. costatus, C. rimosus, Myc. smithii and Ap. dentigerum). We created a bray-curtis distance matrix for each of the EntAcro strains and a similar dissimilarity matrix based on the the seven foraging substrate categories using bray-curtis distance in R. The final Mantel tests were performed in R using 10,000 permutations. and Rachelle Adams allowed us to use colony RMMA150520-03. Funding was provided by the Danish National Research Foundation (DNRF57), an ERC Advanced grant (323085) to JJB, and Marie Sklodowska-Curie Fellowships to PS (300584), MZ (660255) and JZS (327940), who was also supported by a Postdoctoral Fellowship from the Smithsonian Institution Competitive Grants Program (to WT Wcislo, JJB, and JZS).
Additional information software, which gave the same numbers of genes as for EntAcro1. These results suggested that our 'A' bins represent proper draft genomes of single EntAcro1 and EntAcro10 strains. DOI: https://doi. org/10. 7554/eLife. 39209. 024. Supplementary file 2. The 65 single-copy orthologs used in our phylogenetic reconstructrions as identified by Orthofinder based on 59 Mollicutes genomes, associated mainly with mammal, insect and crustacean hosts (see Materials and methods). Genome names are presented at the top of each column with the same identifiers as in the ensembly database (http://bacteria. ensembl. org/info/website/ftp/index. html). NCBI accession numbers are given for all ortholog genes (rows) except for EntAcro1 and EntAcro10 where we used the RAST annotation identifiers.
From left to right: the 59 Mollicutes strain IDs for which a phylogeny was constructed (Figure  1 , Figure  1 -figure supplement 2) using the same identifiers as in the Ensembl database (http://www. ensembl. org), their most likely symbiotic associations (based on the available literature), their hosts, and previous studies that examined their symbiotic interactions. DOI: https://doi. org/10. 7554/eLife. 39209. 027. Supplementary file 5. Primers used in the present study. From left to right: Primer names, the function of the gene targeted based on the RAST annotation, the target organism, the primer sequences from 5' to 3' and the annealing temperatures used for the qPCR. DOI: https://doi. org/10. 7554/eLife. 39209. 028. Supplementary file 6. Correlation statistics for the association between the number of EntAcro1 cells and the expression of 10 MCT-like genes in the Ac.
echinatior midgut and fat body tissues. From left to right we present for each gene examined: The protein ID in the Uniprot database (http://www. uniprot. org/), the gene name and the gene ID based on the Ac. echinatior annotation, the predicted protein size, and the results of statistical analyses examining the correlation between expression of the bacterial ftsZ gene in midgut/fat body tissues and the expression of MCT-like genes. The significance of each correlation was first evaluated using a non-parametric Spearman correlation test (see Methods), for which we give r values, p values and Bonferroni corrected p values. The only significant correlation (marked in bold) was between the ftsZ and the MCT1 (F4WHWZ) transporter, for which previous functional experiments have demonstrated that it is actively involved in the import of acetate in eukaryotic cells  (Kirat and Kato, 2006; Moschen et al.
Acknowledgements We thank  Panagiotis Ioannidis ,  Luigi Pontieri ,  David Nash  and  Lucas Schrader  for bioinformatical and statistical advice,  Sen Li  for allowing access to the server that ran all phylogenomic analyses,  Anna Fomsgaard  for helping with the RNA extractions and cDNA synthesis of ant colonies,  David Donoso  for verifying the species identification of our vouchers of attine ant workers, and  Ernesto Gomez  for assistance in the field. The  Smithsonian Tropical Research Institute in Panama made facilities available , the  Autoridad Nacional del Ambiente (ANAM) of Panama  issued collection and export permits,.
Data availability Sequencing data were deposited in the NCBI Genome submission portal under accession numbers SAMN06251630 and SAMN06251631. The following datasets were generated: Author(s) Year Dataset title Dataset URL Database and Identifier Panagiotis Sapountzis 2015 Entomoplasmatales bacterium EntAcro1 https://www. ncbi. nlm. nih. gov/biosample/? term=SAMN06251630 NCBI BioSample, SAMN06251630 Sapountzis P 2015 Entomoplasmatales bacterium EntAcro10 https://www. ncbi. nlm. nih. gov/biosample/? term=SAMN06251631 NCBI BioSample, SAMN06251631.
Immunocapture enzyme-linked immunosorbent assay (Ic-ELISA): A total of 80 samples were tested by immunocapture-ELISA kit as described by  Libeau et al. (1994). The Ic-ELISA kit produced jointly by Biological Diagnostic Supplies Limited (BDSL) with the collaboration of Flow Laboratories and CIRAD, EMVT, France was used for this purpose. The absorbance was measured at a wavelength of 492 nm. Percent positivity (PP) values were calculated by ELISA Data Interchange (EDI) software (FAO/IAEA, Vienna, Austria). The optical densities were converted to percentage positivity by using following formula: PP = 100 -(OD control/test sample) × 100 OD of PPR ref. antigen The samples with PP value greater than 18% were considered positive.
2A ). To probe these interactions in more detail, the distance between basic residues in SecA-SecYEG and nearby CL head groups was calculated, revealing multiple residues implicated in CL binding, including several with >90% binding occupancy (SI Appendix, Fig. S1A , red data). Next, we ran five simulations of SecA-SecYEG in bilayers containing ∼1% CL, chosen to reduce the high occupancy in the self-assembly dataset, thus showing only specific Sec-CL interactions. Each bilayer was simulated for an extended period of up to 55 μs (over 100 μs in total), with rmsd analyses confirming the stability of the simulations (SI Appendix, Fig. S1B ). As above, we determined the CL occupancy for each basic residue in the system, finding a range of 0-11% over the course of the simulation (SI Appendix, Fig.
3D , blue data), although, unsurprisingly, there is substantial cross-talk between the sites. To quantify the affinities more precisely, we carried out a global fit of all six datasets, accounting for both the specific and nonspecific FRET (lines in Fig. 3  C and D, details are provided in SI Appendix, Supplementary Methods). This allowed us to extract estimates of the affinities for CL at each site, both natively and when knocked out (Fig. 3E  and SI Appendix, Table  S6 ). Under the specific conditions in this experiment [n-Dodecyl-β-D-Maltoside (DDM) micelles, no SecA], we obtain K d s of ∼3. 5 μM for each site, reduced approximately threefold by knocking out the head group binding site. These FRET results clearly demonstrate the presence of two bona fide CL binding sites rather than just a generalized lowering of CL affinity.
Materials and Methods Coarse-Grained Simulations. Simulations of SecA-SecYEG were built using the T. maritima crystal structure [Protein Data Bank ID code 3DIN (25)], with the nucleotide removed and missing loops remodeled in Modeler  (42). Following this, two distinct setups were used, each described below, with full details of the simulations given in SI Appendix, Supplementary Methods. First, for the self-assembly method, 460 distearoyl phosphoethanolamine and 52 CL molecules [bacterial di-1-palmitoyl-2-cis-vaccenate-PG in a -2 charge state  (43, 44) ] were placed around the protein and allowed to selfassemble into a bilayer. From the following production simulations, frames in which the bilayer had not yet formed, or formed in the y-z axis in one case, were removed, and the simulations were analyzed for CL density using Visual Molecular Dynamics (VMD) software (Theoretical and Computational Biophysics Group, University of Illinois at Urbana-Champaign).
Second, symmetrical membranes of nine CL and 823 dipalmitoyl phosphatidylcholine molecules were built around the protein beads using insane. py  (45) , modified here for the inclusion of CL. Rmsd analyses were carried out to ensure the stability of the protein beads (SI Appendix, Fig. S1B ). In both cases, occupancy analyses were carried through monitoring of CL to residue distances over the time course of the simulation using tools available in the GROMACS package  (46). Protein Expression and Purification. Mutations were introduced using the QuikChange protocol (Stratagene) to secEYG in a pBAD expression plasmid. The SecYEG variants were expressed as previously described (47); a full description is provided in SI Appendix, Supplementary Methods. SecA and the model substrate proOmpA were expressed and purified as described previously  (48).
To facilitate quantification by Western blot analysis, a C-terminal minimal V5 tag (IPNPLLGL) was added to the proOmpA gene by PCR and confirmed by DNA sequencing. Blue Native PAGE. Blue native PAGE was run using 100 ng of SecYEG in 0. 044% DDM in 20 mM Tris (pH 8. 0), 130 mM NaCl, and 10% glycerol. CL was added to 30 μM CL, and G250 solution was added to 0. 11%. PAGE was run using 3-12% NativePAGE gels (Invitrogen) according to the manufacturer's protocol (i. e. , 150 V over 105 min). Gels were then silver-stained using an Invitrogen Silver Quest staining kit, as per the manufacturer's protocol. Native Mass Spectrometry. Full details of the nMS setup are provided in SI Appendix, Supplementary Methods. The relative abundance of each oligomeric/lipid-bound state of SecYEG was calculated with the Bayesian deconvolution software UniDec  (49).
ACKNOWLEDGMENTS. We thank  Audrey Solgadi  for mass spectrometry analysis at  L'Institut Paris Saclay d'Innovation Thérapeutique (IPSIT)  and  Mikhail Bogdanov  for fruitful discussions. This work was funded by the  Biotechnology and Biological Sciences Research Council  (Grants  BB/M003604/1 ,  BB/I008675/1 , and  BB/N015126/1 ) and the  Wellcome Trust  (Grants  104632  and  109854/Z/15/Z ). The mass spectrometry work was supported by the  Centre National de la Recherche Scientifique, INSERM  (Grant " DYNAMO ,"  ANR-11-LABEX-0011-01  to M. C. ) and the  Région Ile de France  for cofunding the  Le Service d'Analyse des Médicaments et Métabolites Mass Spectrometry Facility at IPSIT. E. P. is the recipient of an  Imperial College London Institute of Chemical Biology Engineering and Physical Sciences Research Council (EPSRC)   Centre for Doctoral Training studentship.