dataesr/scientific-paragraphs-categorization huggingface dataset:
Identification of Lys and Arg methylation in the AT_CHLORO database Database searching. The peak lists (494 runs) that constituted the AT_CHLORO database  [36, 37]  and data from additional samples (envelope, 3 biological replicates, and thylakoids, 2 biological replicates) were used. Thus, a total of 587 peak lists were searched against a target-decoy version of the complete Arabidopsis proteome (nuclear, mitochondrial, and plastid genome; TAIR v9. 0; June 19, 2009; 33,518 entries) using the Mascot 2. 3 search engine (Matrix Science). The target-decoy version of the database was generated by introducing a reversed version of the sequences. The search parameters were as in ref. [36]  but with the introduction of methyl-specific aspects. In addition to the usual modifications (cysteine trioxidation, acetylation of protein Ntermini, methionine oxidation and dioxidation) the set of variable modifications comprised the mono, di, or trimethylation of Lys and mono or dimethylation of Arg residues.
The enzyme was set to Trypsin/P. As modified Lys and Arg might alter the efficiency of the protease, up to three miscleavages was allowed. The mass tolerances were set at 10 ppm for the precursor ions and at 0. 8 Da for the fragment ions. Parsing of results. Mascot search results were automatically filtered using the home-developed IRMa 1. 25. 0 software  [40]. The following parameters were applied. (i) The number of report hits was fixed automatically to retrieve proteins with a p-value ,0. 05, as defined by Mascot. (ii) A cutoff score of 20 was applied for the peptides. (iii) Only peptides ranked first and with a homology threshold with p,0. 05, as defined by Mascot, were kept. Every duplicated peptide sequences were conserved. Parsed results were then imported into a relational mass spectrometry identifications results database.
Compiling the 587 different analyses in order to retrieve the list of peptides and non-redundant protein groups was performed using a home-made software. Filtering of methylpeptides. In order to discriminate true methylated peptides from false positives, we applied the following filtering procedure (Fig. 1 ). Benefiting from searching a targetdecoy version of the Arabidopsis database, a false discovery rate (FDR) for the methylpeptides was estimated according to the formula FDR = 2[n rev /(n rev +n real )], where n rev is the number of methylated peptide-spectrum matches (PSMs) derived from reverse sequences and n real the number of methyl-PSMs derived from real sequences  [41]. To reduce this FDR, the first filtering step consisted in selecting PSMs above a Mascot score threshold of 50 (Fig.
In silico prediction of protein subcellular localization and Lys/Arg methylation sites The subcellular localization of Arabidopsis protein was analyzed using the SUBA3 search interface (http://suba. plantenergy. uwa. edu. au)  [44] , the AtSubP program (http://bioinfo3. noble. org/ AtSubP/?dowhat = AtSubP)  [45] , and ChloroP (http://www. cbs. dtu. dk/services/ChloroP/)  [46]. We used the following predictors for searching Lys and Arg methylation sites: MeMo v2. 0  [47] , BPB-PPMS (http://www. bioinfo. bio. cuhk. edu. hk/bpbppms/ index. jsp) [48] , MASA (http://masa. mbc. nctu. edu. tw/index. html) [49] , PMeS  [50] , and PLMLA (http://bioinfo. ncu. edu. cn/inquiries _PLMLA. aspx)  [51].
Mass spectrometry methods Mass spectrometry analysis on recombinant proteins was carried out essentially as described in ref. [30]  with minor modifications. For sample preparation, the oxidizing H 2 O 2treatment of gel bands was replaced by reduction with dithiothreitol for 45 min at 56uC and alkylation with iodoacetamide for 35 min at room temperature. LC-MS/MS raw data were acquired on a LTQ-Orbitrap (Velos) hybrid mass spectrometer (ThermoFisher) as in ref. [30]. Peak lists were generated with the Mascot Distiller version 2. 4. 3 software (Matrix Science) from the LC-MS/MS raw data. Using the Mascot 2. 4 search engine (Matrix Science), we searched all MS/MS spectra against the target-decoy version of an updated compilation of the A. thaliana protein database provided by TAIR (nuclear, mitochondrial, and plastid genome; TAIR v10.
0; December 14, 2010; 35,386 entries) and a home-made list of contaminants (260 entries). The set of allowed variable modifications were acetyl (N-termini), methionine oxidation and dioxidation, methyl (Lys, Arg), dimethyl (Lys, Arg), and trimethyl (Lys). In addition, carbamidomethyl cysteine was set as a fixed modification. Mascot search results were automatically filtered as described in ref. [30]  with the IRMa 1. 30. 4 version software. Spectra of interest were checked manually to confirm sequence and modifications.
Results and Discussion In silico identification of Arabidopsis protein Lys and Arg methyltransferases targeted to chloroplasts More than 60 protein methyl-transferases have been previously annotated in the Arabidopsis genome, including nine PRMTs  [8]  and about 50 SET domain-containing PKMTs  [4]. An increasing number of PKMTs with a seven-beta-strand structural fold is being identified in yeast and human cells  [5, [56] [57] [58] [59] , but this subfamily is poorly documented in plants  [60]. Using BLAST searches we identified ten Arabidopsis orthologs for these evolutionary conserved proteins. Then we analyzed the subcellular distribution of the full set of methyltransferases using the SUBA3 search interface  [44]  and the AtSubP program  [45]. These predictions were combined with a review of proteomic data dedicated to Arabidopsis chloroplasts  [36, 37, 61, 62].
For this purpose we set up a filtering pipeline for the discovery of Lysand Arg-methylated peptides in the large pool of spectra collected to edit the Arabidopsis chloroplast proteome (AT_CHLORO database)  [36, 37]. AT_CHLORO is a comprehensive database that identifies 1,323 chloroplastic proteins and provides accurate sub-plastidial localization (stroma, envelope, and thylakoids) for 819 proteins. The strategy of reexamination of high-quality MS/ MS data using criteria specific to post-translational modifications has been used success-fully to discover methylated proteins in the yeast proteome  [11]. The workflow is composed of six steps combining automatic filtering and expert examination of spectra. The procedure is detailed in the Experimental procedure section (Fig. 1 ). Some of the essential steps to produce high-confidence methylpeptides include the removal of ambiguities due to amino acid substitutions and trimethylation vs.
acetylation. These modifications on Lys residues induce very close mass shifts compared to the non-modified peptide (42. 04695 and 42. 01056 Da, respectively). Instruments with high-mass accuracy, such as the Fourier transform ion cyclotron resonance mass spectrometer used to generate the AT_CHLORO database  [36] , and identification of neutral loss of trimethylamine (59 Da) that is specific to trimethylation can allow to discriminate these modifications  [42, 43]. Nine ambiguous Lys sites were examined and trimethylation was supported for four of them by their accurate mass and the presence of neutral loss at 59 Da (Fig. S1 ). For one candidate, a glyceraldehyde 3-phosphate dehydrogenase protein (At1g12900), the absence of neutral loss suggested an acetylation, in agreement with a previous proteome survey  [21].
To support this assumption a review of the Arabidopsis interactome map  [69]  identified 12 chloroplastic proteins of AT_CHLORO_Me involved in binary protein-protein interactions. For example, the yeast two-hybrid system and literature-curated binary interactions identified the Protein accession, symbol and description were from TAIR, Uniprot and PPDB databases. Functional annotation was done according to MapMan bins and sub-bins  [67]. Subcellular and subplastidial location was curated using data from AT_CHLORO  [36] , PPDB  [62]  and dedicated studies. The trimethylated vs. acetylated status of Lys residues was confirmed (c) or remained ambiguous (a). Modeling of 3D-structures was done using the Phyre 2 server  [52]. NA, not available (the protein structure could not be modeled or the model does not cover the methylation site).
Annotated proteins from Table  2  were grouped to create categories 'metabolism (other than photosynthesis)' and 'protein synthesis and targeting'. (c) Amino acid motif surrounding Lys methylation sites was created using WebLogo (http://weblogo. berkeley. edu/). (d) Positioning of the Lys395 methylation site on the 3Dstructure model of fructose bisphosphate aldolase (FBA1, At2g21330). The model was generated with the Phyre 2 server using the 3D structure of aldolase from rabbit muscle (PDB entry 1ZAI) and imaged with the PyMOL software. doi:10. 1371/journal. pone. 0095512. g003 the methylated product by MS/MS indicated that trimethylation occurred at Lys109, as for the assays conducted with stroma, validating PRPL11 as a substrate of PrmA-like. To analyze whether functional redundancy can exist among protein methyltransferases, we tested the ability of four alternative chloroplastic PKMTs to modify PRPL11 in vitro.
Acknowledgments We thank  Drs. Norbert Rolland  and  Jacques Bourguignon  for fruitful discussions and critical reading of the manuscript. We thank  Dr. Jean-HerveAlix  ( Institut de Biologie Physico-Chimique, Paris, France ) for providing us with the E. coli KNOK16 strain. We acknowledge the  Arabidopsis Biological Resource Center  for providing us with the Arabidopsis stocks  U09645  and  U21597.
This study received financial support from the  Department of Plant Biology INRA , the Cluster 9 of the  Region Rhone-Alpes , the  French National Research Agency  ( ANR-2010-BLAN-1610-01   Chloro-Pro ), and the  Proteomics French Infrastructure  ( ProFI ;  ANR-10-INBS-08-01 ). This work was supported by a PhD fellowship (to  M. Mininno ) funded by the  French Ministry of Higher Education and Research. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Hypericum Fossil Record and Assignment to Phylogenetic Clades Hypericum has a rich fossil record that extends from the Late Eocene to present times (Fig. 1b ; Supplementary Table  S1  in online Appendix 1, available at http://dx. doi. org/10. 5061/dryad. 5845b). This record comprises some macrofossils (leaves), but mostly consists of microfossil remains, seeds and pollen. Among them, seeds are particularly distinctive in Hypericum. The typical seeds in the family Hypericaceae are small, narrowly cylindrical to ovoid-cylindrical, or ellipsoid  (Robson 1981) , with a size ranging from 8 to 0. 3 mm. They also present brownish to blackish tannin contents. According to  Corner (1976) , the shape of the exotegmen cells in Hypericaceae-tubular or radially elongated and with stellate-undulate or lobate facetes-is very characteristic of this family and found only in a few other families: Clusiaceae, Elatinaceae, and Geraniaceae.
Distributional and Climatic Data Distribution data for extant Hypericum species were obtained from taxonomic monographs  (Robson 1981onwards) , herbarium collections (e. g. , MA), and online databases (GBIF, http://www. gbif. org). Additionally, distribution data for species of Triadenum L. , Santomasia N. Robson, and Thornea Breedlove & McClintock were included; these genera appear nested within Hypericum in most molecular studies  (Ruhfel 2011; Meseguer et al. 2013; Nürk et al. 2013a). Distributions were georeferenced when latitudinal and longitudinal data were not provided and all data points were collated to exclude ambiguous occurrences (e. g. , points in the sea or in regions where Hypericum has never been described). In total, 1033 occurrences were included, representing more than 160 identified species.
Fossil distribution data (143 records; Supplementary Table  S1  in online Appendix 1 available at http://dx. doi. org/10. 5061/dryad. 5845b. ) were obtained from the literature  (Meseguer and Sanmartín 2012 ) and online resources (Paleobiology Database, http://paleodb. org/). We grouped non-Pleistocene fossil records into two time slices to ensure an adequate number of occurrences to model ancestral species data: "greenhouse" lineages covering the Eocene, Oligocene, and Early Miocene periods (68 records corresponding to 18 grid cells of 2. 5°× 3. 75°) and "coldhouse" fossil lineages, spanning the Late Miocene and Pliocene periods (65 records, 19 grid cells). The boundary between greenhouse and coldhouse time slices was set at the Late Miocene Cooling event (LMC, about 10 Ma), a dramatic drop in temperature following the Middle Miocene Climatic Optimum  (Zachos et al.
2008). This is a period when important changes in vegetation occurred, including the replacement of the mixed-mesophytic forest by a boreal coniferous forest across the northern regions of Eurasia and North America  (Schneck et al. 2012). Fossil paleocoordinates were calculated with the PointTracker application of the Paleomap software  (Scotese 2010). Climatic data for current conditions (≈1950-2000) were extracted from WorldClim  (Hijmans et al. 2005). For past scenarios, we used six Hadley Centrecoupled ocean-atmosphere general circulation models (HadCM3L) that incorporate the effect of changes in atmospheric CO 2 concentration; the latter has been shown to be a good proxy for variations in global temperatures  (Beerling and Royer 2011). These simulations were generated with the same underlying algorithms and the same set of variables (monthly temperature and precipitation values at a resolution of 2.
Four outgroup taxa representing the Cratoxyleae and Vismieae tribes in family Hypericaceae were also included in the analysis: Harungana Lamarck, Psorospermum Spach, and Vismia Vand. of the tropical tribe Vismieae sister to tribe Hypericeae and Eliea Cambess. , representing the tropical tribe Cratoxyleae. Phylogenetic relationships and absolute lineage divergence times within Hypericum were estimated in BEAST v. 1. 8. 1  (Drummond et al. 2012 ). The "Two-marker" chloroplast data set was analyzed partitioned by-gene-that is, applying individual GTR+G substitution models to each gene-based on the results of  Meseguer et al. (2013) , who carried out sensitivity analyses to evaluate the effect of different partitioning strategies on their data. Choice of clock and tree model priors in the  Meseguer et al.
Given that mixing, EES, and convergence were better in the latter model we used this model in our analysis with two replicate MCMC searches. As in  Meseguer et al. (2013) , two calibration points were used to derive absolute ages: the Late Eocene fossil H. antiquum to constrain the crown node of Hypericum (=Hypericeae) using a lognormal prior (offset = 33. 9 myr, standard deviation "SD" = 0. 7), and a secondary calibration point for the root of the tree (crown-node family Hypericaceae) from the calibrated clusioid clade phylogeny of  Ruhfel (2011)    Biogeographic Analysis Biogeographic analyses were conducted with the DEC parametric likelihood method implemented in Lagrange  (Ree et al. 2005 ; Ree and Smith 2008) using the fast C++ version  (Smith 2009 ; https://github. com/ rhr/lagrange-cpp).
Geographic-Dependent Extinction We used the Geographic State Speciation and Extinction model "GeoSSE"  (Goldberg et al. 2011)  implemented in the R package diversitree (FitzJohn 2010) to investigate the existence of historical differences in speciation and extinction rates across biogeographic regions in Hypericum. In trait-dependent diversification models like GeoSSE or BISSE  (Maddison et al. 2007 ), the trait itself (transitions between character states) influences the birth-death process that generates speciation times in the phylogeny. GeoSSE extends the BISSE binary model to incorporate a third, polymorphic state for geographic characters, since taxa are often not endemic but present in more than one area/state  (Goldberg et al. 2011). Biogeographic regions were defined the same as above, except that we excluded the NT and OC from the analysis: the first does not include widespread species, whereas occurrence of Hypericum in OC is only marginal.
This avoids making assumptions about the length duration (longevity) of the fossil  (Nauheimer et al. 2012) , or the need to code fossil morphological characters for every extant taxa or introducing missing data for all molecular characters in the fossil  (Mao et al. 2012 ). To our knowledge, this is the first empirical study using this fossil functionality in Lagrange. Integrating the spatial range of the oldest fossil of Hypericum (H. antiquum from West Siberia) in the biogeographic analysis resulted in the EP being inferred as part of the ancestral distribution of several basal nodes in the phylogeny-that is, the ancestor of the tribes Hypericeae and Vismieae (Node 142), the crown node of Hypericum, the ancestor of all extant sections excepting Elodes-Adenotrias (Node 140), and the ancestor of the New World group (Node 141 in M3, Fig.
ACKNOWLEDGMENTS This work is based on the PhD thesis of A. S. M. defended in 2013. The authors are indebted to  J. Fuertes ,  G. Nieto ,  L. Pokorny , and  E. Jousselin  for useful suggestions to early drafts of the manuscript. They thank  L. Taylor  for technical support in relation to the global climate data sets, and  C. Bradshaw ,  D. Lunt , and  P. Valdes  for their earlier modeling of Cenozoic climates. They also thank  F. Anderson  ( Editor-in-Chief ),  A. Mast  (Associated Editor), and two anonymous reviewers for their valuable comments, which greatly helped improve the manuscript.
For each herd involved in the program, the database contained 2 to 6 test dates (TD) with individual MIR spectral information of goats. Additional information on SCC, milk yield, and fat and protein contents, collected monthly by the milk recording organization, were extracted from the national goat database (CTIG, Centre de Traitement de l'Information Génétique, INRA, Jouy-en-Josas, France). Only data from the first lactation were considered for estimation of genetic parameters. Preliminary studies showed that fat content showed a considerable residual standard error during the earlier stages of lactation (between 5 and 20 DIM). Therefore, records with DIM of <20 or >364 were discarded. Additional edits were applied according to the following inclusion criteria: (1) herds with at least 5 FA profile records by herd within test-day (HTD) classes; (2) animals from Alpine and Saanen purebred, with known sires and at least 2 TD during the lactation; and (3) sires with at least 5 daughters.
Fatty acid estimates were expressed in g/100 g of milk using milk density of 1. 027 g/cm 3. To avoid taking into consideration glycerol, which is automatically included in the estimation, a correction coefficient of 0. 95 was applied to the data. To study FA profiles in fat content, FA were also converted into contents in milk fat (g/100 g of fat), using the total fat content estimated from the calibration equations (with relative error equal to 0. 60% and R 2 > 0. 99) and compared with GC data. Ferrand-Calmels et al. (2014)  provided accuracies for FA for FA expressed in g/100 mL of milk and in g/100 g of FA. The latter accuracies were calculated on the same set of data as in  Ferrand et al. (2011) , to which 134 Alpine and Saanen samples were added. Conversion affected differently the performance of the equation depending on the individual FA and the species.
Model In a preliminary step, the MIXED procedure of SAS  (SAS Institute, 2008)  was used to study the statistical significance of the following variation factors: HTD, lactation stage, breed, parity, month of kidding, sampling moment within lactation stage, and season of control. Considering all significant factors as random, the VARCOMP procedure of SAS  (SAS Institute, 2008)  was used to estimate the REML variance component of the factors, to quantify the percentage of variance explained by each factor. The HTD factor was the most important of all studied effects, because it explained 20 to 40% of the variance for the analyzed traits [18 FA explained in both units (i. e. , g/100 g of milk and g/100 g of milk fat) as well as 4 milk production traits]. This factor is associated with the circumstances of the herd on the day of testing within each herd, particularly with feeding and, to a lesser extent, with other management factors.
ACKNOWLEDGMENTS Cyrielle Maroteau  acknowledges the support of the  Union Nationale des Cooperatives d'Elevage et d'Insémination Animale (UNCEIA, Paris, France) ,  APIS GENE (Paris, France)  and  CAPGENES (Mignaloux-Beauvoir, France)  for her scholarship ( CIFRE = Conventions Industrielles de Formation par la Recherche ). The data used in this study were funded as part of the multispecies  French PhénoFinLait program , with the financial contribution of  ANR, APIS GENE, CN-IEL (Maison du Lait, Paris, France ), FranceAgriMer (Montreuil-sous-Bois, France),  France Genetique Elevage (Paris, France) , and the  Ministry of Agriculture (Paris, France). We thank all the people involved in collecting and processing the data: breeders, laboratories, milk recording agency,  Jean Legarto  ( Institut de l'Elevage, Castanet-Tolosan, France ), and  Hugues Caillat  ( INRA, Toulouse, France ).
Bioinformatics analysis of che genes Protein sequences were analyzed by Pfam (release 24. 0) (see comment on Tables  1, 2  and 3 ) databases  [59]. Signal peptides and transmembrane helices were predicted using the signalP 3. 0  [60]  and TMHMM v. 2. 0  [61]  servers, respectively. Genomic regions were investigated using the complete genome sequence available on NCBI  [15]. For the dataset construction and phylogenetic analyses, M. xanthus Che and MCP homologues were retrieved from the complete M. xanthus DK 1622 proteome available on NCBI (http://www. ncbi. nlm. nih. gov/genome/proteins/1120/ ? project_ id = 58003;  [15]  using Blastp with default parameters  [62]. The distinction between homologous and non-homologous sequences was assessed by visual inspection of each Blastp outputs (no arbitrary cut-off on the E-value or score).
To ensure the exhaustive sampling of homologues, iterative Blastp queries were performed using homologues of M. xanthus MCP identified at each step as new seeds. The retrieved homologues were gathered in a dataset and the corresponding sequences were aligned using the ClustalW2 program (Default parameters,  [63]. Each alignment was visually inspected and manually refined when necessary using the ED program from the MUST package  [64]. Regions where the homology between amino acid positions was doubtful were manually removed using NET from the MUST package. Both Maximum likelihood (ML) and Bayesian phylogenetic trees were computed for the MCPs. ML analyses were run using PhyML version 3. 0 with the Le and Gascuel (LG) model (amino acid frequencies estimated from the dataset) and a gamma distribution (4 discrete categories of sites and an estimated alpha parameter) to take into account evolutionary rate variations across sites  [65].
The robustness of each branch was estimated by the non-parametric bootstrap procedure implemented in PhyML (100 replicates of the original dataset with the same parameters). Bayesian analyses were performed using MrBayes  [66]  with a mixed model of amino acid substitution including a gamma distribution (4 discrete categories) and an estimated proportion of invariant sites. MrBayes was run with four chains for 1 million generations and trees were sampled every 100 generations. To construct the consensus tree, the first 1500 trees were discarded as ''burnin''. In order to obtain a high-resolution taxonomic distribution of the Che systems, we combined the CheR, CheB, CheA and MCP conserved sequences in a so-called supermatrix. When more than one homologue of these genes was present in a given genome, the genes were combined according to their physical linkage on the chromosome.