dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus, the presence of multiple RR domains and a branch point at a circuit node formed by MglA asks how signaling from the FrzE kinase is channeled to the downstream motility systems (Fig  1B ). Overall, the motility regulation circuit is assembled from four interconnected modules with genetically separable functions: the control of the reversal frequency (switch control module), the coordination of the A-and S-motility systems (polarity control module) and the function of A-and S-motility (A-and S-motility modules, Fig  1B). By investigating the evolutionary origin of each module, we identified the core structure of the Frz pathway, genetically uncoupled A-and S-motility regulations and thus identified key regulations that led to the emergence of the evolved pathway. Remarkably, we find that adaptation of the ancestral circuit to A-and Smotility regulation required amplification of the signaling efficiency, suggesting that the evolution of signal reinforcement mechanisms may be linked to signal transduction pathway diversification in cells.
Although the exact amplification mechanism remains to be determined the epistastic interactions between FrzE, FrzZ and RomR suggest that the FrzE kinase might activate reversals both by acting directly or indirectly on RomR and by direct phosphorylation of FrzZ, which would then interact with RomR to further activate it. The blue and purple curves are drawn from data shown in Fig 7B. doi:10. 1371/journal. pgen. 1005460. g008 motility system to understand these signaling intensity requirements. At the molecular level, FrzZ must act between the FrzE kinase and RomR because (i), a hyper active FrzE kinase (frz on mutation) still requires FrzZ for maximal signal transmission (Fig 6C ), suggesting that FrzZ does not exert its action by feedback stimulation of the autokinase activity of FrzE; and ii), a frzZ romR mutant behaves like a romR mutant (Fig 4C ), showing clear epistatic relationships.
FrzZ is a dual response regulator protein and it will be important to determine how the phosphorylation of each receiver domain contributes to signal amplification. The FrzZ phosphorylation sites appear partially redundant but only the phosphorylation of D52 and not D220 is important for the polar localization of FrzZ  [33]. At the cell pole, the phosphorylated form of FrzZ could interact directly with RomR to facilitate the reversal switch. Two-component cascades frequently employ accessory response regulator domains to achieve a variety of functions in phosphorelays, signal inhibition or negative feedback loops  [52, 53]. To our knowledge, this is the first time that a signal amplifier function is identified for a RR protein and because FrzZ-like CheY-CheY fusion proteins are predicted in other complex two component systems (Survey of the Microbial Signal Transduction database,  [54] ), characterizing the amplification mechanism may generally impact our understanding of bacterial signal transduction.
Automated cell tracking and statistics Image analysis was performed with a specific library of functions written in Python and adapted from available plugins in FIJI/ImageJ  [59]. Cells were detected by thresholding the phase contrast images after stabilization. Cell tracking was obtained by calculating all objects distances between two consecutive frames, thus selecting the nearest objects. The computed trajectories were systematically verified manually and when errors were encountered, the trajectories were removed. The analysis of the trajectories is done automatically by a Python script that calculates the angle formed by the segments between the center of the cell at time t, the center of the cell at time t-1 and the center at time t+1. Directional changes were scored as reversals when cells switched their direction of movement and the angle between segments was less than 90°.
Genomic dataset construction A local protein database containing the 2,316 complete prokaryotic proteomes available in the NCBI (http://www. ncbi. nlm. nih. gov/) as of  May 23, 2013  was built. This database was queried with the BlastP program (default parameters,  [61] ) using the full length sequences of the signaling proteins (FrzF (MXAN_4138), FrzG (MXAN_4139), FrzE (MXAN_4140), FrzCD (MXAN_4141), FrzB (MXAN_4142), FrzA (MXAN_4143) and FrzZ (MXAN_4144)), the polarity control proteins (MglA (MXAN_1925), MglB (MXAN_1926) and RomR (MXAN_4461)) and the downstream proteins (FrzS (MXAN_4149) and AglZ (MXAN_2991)) of M. xanthus as a seed. The homology was assessed by visual inspection of each BlastP output (no arbitrary cutoffs on the E-value or score). The retrieved sequences were aligned using MAFFT version 7 (default parameters,  [62] ).
Regions where the homology between amino acid positions was doubtful were removed using the BMGE software (BLOSUM30 option;  [63] ). For each protein, preliminary phylogenetic analyses were performed using FastTree v. 2 using a gamma distribution with four categories  [64]. Most of the studied proteins belong to very large protein families. Based on the resulting trees, the subfamilies containing the sequences from M. xanthus were identified and selected for further phylogenetic investigations. The corresponding sequences were realigned using MAFFT version 7 with the linsi option, which ensures accurate alignments. The resulting alignments were trimmed with BMGE as previously described.
Phylogenetic analyses Maximum likelihood (ML) trees were computed using PHYML version 3. 1  [65]  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. Branch robustness was estimated by the nonparametric bootstrap procedure implemented in PhyML (100 replicates of the original dataset with the same parameters). Bayesian inferences (BI) were performed using Mrbayes 3. 2. 2 [66]  with a mixed model of amino acid substitution including a gamma distribution (4 discrete categories). MrBayes was run with four chains for 1 million generations and trees were sampled every 100 generations. To construct the consensus tree, the first 2000 trees were discarded as "burn in".
Reference phylogeny of Delta/Epsilonproteobacteria The 79 complete proteomes of Delta/Epsilonproteobacteria available at the NCBI in May 17, 2013 were retrieved and assembled in a local database (S5 Table ). We used SILIX to build the protein families of homologous sequences present in these genomes (default parameters;  [72] ). The homologous sequences corresponding to protein families present exactly in a single copy per genome (17 proteins) were aligned using MAFFT Version 7 (linsi option), trimmed with BMGE and combined to build a large supermatrix (6958 positions). The ML phylogenetic tree corresponding to this large supermatrix was inferred with PhyML, as described above.
Funding: CBA is supported by the  Investissement d'Avenir grant  " Ancestrome " ( ANR-10-BINF-01-01 ). LE is supported by a  Fondation pour la Recherche Médicale  "grands équipements 2010" ( FRM DGE2010221257 ). EMFM is supported by a  Marie Curie Career Integration Grant  ( BMC-CIG-293952 ). VM and GB are supported by grants from the  Agence Nationale de la Recherche  ( ANR-09-MIEN-004 ). MG is supported by a  European Research Council  grant to TM and a 4th year PhD thesis fellowship from  Association pour la Recherche  contre le Cancer signal transduction pathways. ( DOC20140601482 ), RA and TM are supported by an  European Reseach Council starting  grant  DOME-261105  and a Bettencourt-Schueller "Coup d'élan pour la recherche française 2011" to TM. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
From the concentration and the average size of each amplicon library, the amount of DNA fragments per microliter was calculated and libraries for each run were diluted to 3E9 DNA molecules prior to clonal amplification. Emulsion PCR was carried out using the Ion OneTouch 200 Template Kit v2 DL (Life Technologies, Monza, Italy) according to the manufacturer's instructions. Sequencing of the amplicon libraries was carried out on a 316 chip using the Ion Torrent PGM system and employing the Ion Sequencing 200 kit (Life Technologies) according to the supplier's instructions. DNA sequencing was performed at GenProbio ltd (Parma, Italy). After sequencing, the individual sequence reads were filtered by the PGM software to remove low quality and polyclonal sequences. Sequences matching the PGM 3 0 adaptor were also automatically trimmed.
All PGM quality-approved, trimmed and filtered data were exported as bam files. The bam files were processed using QIIME  [29]. According to what is previously published  [28] , quality control of sequencing reads retained sequences with a length between 140 and 400 bp, homopolymers <8 bp and mean sequence quality score >25. Additionally, sequences were truncated at the first base if a low quality rolling 10 bp window was found and sequences with mismatched primers were omitted. Lastly, primers were removed before analysis with QIIME  [28]. The data set is available in the Sequence Read Archive (SRA) of NCBI, accession n°PRJNA254809.
Statistical analysis Cumulative diabetes incidence was evaluated by actuarial survival curves using the Kaplan-Meier estimation. Differences in the incidence curves and their statistical significance were calculated using the nonparametric log-rank test (appropriate to use when the data are adequately skewed and censored). For gene expression, cell phenotyping and cytokine analysis, 5 mice per experimental group were analyzed individually. For each parameter assessed mean values were deduced from the data recovered for the 5 individual mice tested per group. For each organ tested, each sex (males or females) and each parameter, mean values were compared between treated and control mice using the nonparametric Mann-Whitney test. All tests were performed using Prism software (GraphPad Software, La Jolla, CA, USA).
In addition, diversity was significantly lower in mice treated with antibiotics, as showed by the rarefaction curves based on the Chao1 and Shannon indexes (Fig  2b  and 2c ). Strikingly, the average Chao1 index value dropped from 1605 in control mice to 428 in those treated with vancomycin treatment and to189 in those subjected to Strp-Col-Amp treatment. In mice having received the antibiotic mixture, nearly all bacterial communities had disappeared. This is clearly supported by the reduced amount of 16S rRNA amplicon sequenced and an increase in aspecific amplicons that were removed in the filtering process (S1 Table ). The 16S rRNA sequences were taxonomically identified at the phylum, as well as family and genus levels using the Qiime software suite  [29] , based on the GreenGenes database and the RDP classifier.
19 to 0. 70. It is remarkable that there was a tendency for the median of H 2 to be higher between generations G7 and G16 than between generations G1 and G6 (Table  1  and Table 2 ). Note that transcient increase of H 2 is expected from fixation of initial polymorphisms in selfing populations. Starting from an heterozygote, the variance between progenitors is expected to increase, and the variance within progenitor is expected to decrease due to selfing  [47]. Overall, our observations indicated no significant decrease in genetic variance neither within Early nor within Late families. Hence, decrease in genetic variance cannot account for the lack of response to selection between G7 and G16 in the FL2. 1, FL2. 2, ML1, ML2 families. At generations G7 and G17, we performed flowering time measurements for all plants and compared them with the average flowering time of progenitors selected to form G8 and G18 (data not shown).
Markers discriminate early and late families In order to assess the genetic and epigenetic determinants of the response to selection, we combined both The response to selection was analyzed separately in each family from progenitor phenotypic data after correction for block and year effects. Response (degree-days per generation) is measured as the slope of the linear regression of flowering date (thermal time) on generations of selection (see Methods section). df is the number of degrees of freedom. Note that variation between families mainly denotes variations in the number of progenitors. stde is the standard error for the response, and R 2 the adjusted R-squared of the regression model. Broad-sense heritability (H 2 ) was estimated at each generation as the ratio of the variance between progenitors belonging to the same family on the total phenotypic variance Methyl-Sensitive Amplification Polymorphisms (M-SAPs) and Amplified Fragment Length Polymorphisms (AFLPs) to genotype all progenitors selected during the first 7 generations.
In all families, the initial heterozygosity decreased at a rate that was not different from (1/2). As a result, all progenitors were homozygous for one or the other allele at each locus at generation G6. The dataset was complemented with information from the previously obtained PCR marker at locus eIF-4A associated with variation of flowering time  [51]. While scarce, markers offered a surprisingly high discrimination power. Discriminant analyses indeed revealed that 94 % to 95 % of the variation between families was explained by the two first axes with different types of markers equally contributing to their definition (Fig. 3 ). In F252, the first axis discriminated all 4 families, the Early FE1, FE2 from the Late FL1, FL2 while the second axis essentially distinguished the 2 Early families.
Association and Linkage mapping reveal a dearth of causal polymophisms We calculated the linkage disequilibrium between the markers associated with flowering time with the purpose of clustering and ordering them in linkage groups. We found 5 distinct linkage groups for F252 and two for MBS (Fig. 4 a  and b ). Among the 5 F252 linkage groups, one contained 17 markers clustered in a region bearing the gene eIF-4A associated with flowering time variation both within the F252 pedigrees and a large panel of maize inbred lines  [51]. For each marker, we measured its additive and dominant effect using flowering data gathered along pedigrees. We assessed its significance using gene dropping simulations, thereby explicitly accounting for pedigree information. In F252 populations, among 33 markers, 10 exhibited a significant additive effect while none displayed a dominant effect.
9 % for F252, and 0. 19 % for MBS over 29,000 SNPs mainly concentrated in genic regions. Our starting material was therefore characterized by a narrow genetic basis, even though our estimates are likely biased downards by undetected residual heterozygosity in gene-poor portions of the genome such as pericentromeric regions  [56, 57]. With our molecular data we confirmed the greater variation of the F252 inbred, as compared with MBS (about one order of magnitude higher). Despite such a narrow genetic basis, standing variation for flowering time contributed to the response to selection. Consistently, we found a significant genetic variance component in F2 populations derived from crosses between one early-and one late-flowering plant from the initial F252 and MBS seed lots (data not shown). In F252, the measured genetic variance was similar to the one measured in an F2 population derived from a cross between an Early and a Late progenitor at generation G13, while in MBS, it was about half.
Evaluation of all progenitors from G0 to G6 in 2004 and 2005 Of the 249 selected progenitors (from generation G0 to G6), S2 seeds (produced by two generations of selfing) from 229 progenitors were evaluated in a 2 years field trial (in 2004 and 2005) at Gif-sur-Yvette (France) following the procedure described in  [46]. Controls were S2 seeds produced from the F252 and MBS initial seed lots. Briefly, progenitors were evaluated in randomized block design and female flowering time was recorded as the date (in days after july 1st) at which 50 % of plants within a plot were silking. For the present paper, we converted flowering dates in thermal time following  [73]  (with parameter values Tb 6 and To 30). We estimated genotypic data after correcting for fixed year, and block within year effects  [46].
Response to selection from G0 to G16 As described above, we recorded at each generation the flowering dates of 12 plants per progenitor within Early and within Late poualtions. After transformation into thermal time and correction for the block effects, data collected from generations G0 to G16 were analyzed separately for each DSE. Within each data set, we supposed that variations for flowering date Z could be decomposed into a fixed year effect for all progenitors, a linear response component that depended on the population (Late, Early, Control), and a residual. Z ijklm = Year i + b j • gener i + ijklm ( 1 ) where i stands for the year and corresponding generation of selection, j for the population, k for the family within population, l for progenitor within family, and m for the plant measurements within progenitor.
We used equation  (1)  to correct the data for the fixed year effect. In order to verify the validity of this approach we compared the resulting corrected flowering time estimated from G0 to G6 with the ones obtained from the G0 -G6 complete evaluation trials  (in 2004 and 2005). As shown in Additional file 1: Figures  S1  and S2 , there was an extremely high correlation between the two measures (r = 0. 83 for F252 and r = 0. 88 for MBS, p-values lower than 2. 2e -16 in both cases). We therefore used yearly measurements of the most extreme plants to analyze the response to selection over 16 generations. We corrected values Y ijklm = Z ijklm -Ŷear i to analyze separately the response to selection for each independent family. Note that we did not exactly measure the same trait as the one measured in the evaluation trial  [46] , i.
Acknowledgements ED was founded by a  joint CNRS-INRA doctoral fellowship. All the field experiments in this work received the technical support of  INRA  ( Institut National de la Recherche Agronomique ). We are grateful to  Alain Charcosset  for initiating with CD the DSEs and helpful discussions throughout this work. We also would like to acknowledge the enthousiastic participation of 18 undergraduate students to the selection experiments, especially  Thomas Blein ,  Laurent Cohuet  and  Emmeline Bertrand. Current French  policy for granting research favors short term projects oriented towards applications, and the authors want to inform the scientific community that long term research projects in the spirit of the present one would be practically impossible to launch today.
proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral. proteomexchange. org) via the PRIDE partner repository with the dataset identifier PXD000294. The metabolomic analysis was conducted using both Acquity UPLC s system (for amino acid identification), and a gas-chromatography-mass spectrometry platform (for sugars identification). Metabolomic fingerprintings were assessed in the University of Rennes 1, UMR CNRS 6553 EcoBio (France). A detailed interpretation of the obtained data can be found in  Hidalgo et   Proteomic data were assessed using a two-dimensional fluorescence difference gel electrophoresis (2D DIGE) coupled with a matrix-assisted laser desorption/ ionisation-time of flight (MALDI-TOF) and tandem mass spectrometry (MS) Metabolomic data were assessed using a gas-chromatography-mass spectrometry (GC-MS) and an Acquity UPLC s system (Waters Corporation, Milford, MA, USA) platforms Data format Raw, analyzed Experimental factors We compared the protein and metabolites (amino acids, polyols, sugars) in two malarial mosquito species form the Anopheles gambiae species complex (formerly known as the M and S molecular forms).
Data source location Both M and S mosquitoes were collected in Burkina-Faso (West Africa) in human dwellings of Bama (11°23'N, 04°24′W) and Soumousso (11°01'N, 04°02′W), respectively. The metabolomic data were collected at the University of Rennes 1, UMR CNRS 6553 EcoBio (France). The proteomic data were obtained from Applied Biomics (USA).
Image analysis Effect of rearing conditions on protein expression in both species and differences between species were assessed by overlaying images of the seven different gels. Note that the abundance of each protein spot was normalised using the seventh gel. The resulting 2D gels were scanned using a Typhoon Trio scanner (Amersham BioSciences) with excitation and emission wavelengths for Cy2labelled (488/520 nm), Cy3-labelled (548/560 nm) and Cy5-labelled (641/660 nm) proteins using settings that match in similar relative fluorescence intensities for the Cy3-or Cy5-labelled samples (Fig. 1 ). Image analysis for intensity measurements of the different protein spots was performed using ImageQuantTL and DeCyder softwares (GE Healthcare). Then, images were subjected to in-gel analysis and cross-gel analysis using DeCyder v6.
The MALDI-TOF mass spectra data were acquired in reflectron positive ion mode, averaging 2000 laser shots per spectrum. The TOF/TOF tandem MS fragmentation spectra were acquired for each sample, averaging 2000 laser shots per fragmentation spectrum on each of the 5-10 most abundant ions present in each sample (excluding trypsin autolytic peptides and other known background ions). Both the peptide mass and the associated fragmentation spectra were submitted for database search using GPS Explorer software version 3. 5 (Applied Biosystems) equipped with the MASCOT search engine (http://www. matrixscience. com, Matrix science). This submission allows the identification of proteins from the National Center for Biotechnology Information non-redundant protein sequence database (NCBInr), restricted to the A.
gambiae species complex. The searches were performed without constraining the protein molecular weight or isoelectric point, with carbamidomethylation and oxidation as variable modifications, and with one missed cleavage allowed in the search parameters. The highest protein scoring hit with a protein score confidence interval over 95% from the database search for each 2D gel spot was accepted as positive identification. Based on an average difference of at least 1. 3-fold (absolute value), only 39 spots were selected, which corresponded to 29 distinct proteins, for further identification. The ratio of the 39 spot expressions between the species and/or rearing conditions was obtained from in-gel analysis using DeCyder. Table  1  shows the identification of the 39 spots retained in A. gambiae based on this method.
Data analysis Average-fold differences of the 39 spot abundances were first normalised (Logarithmic abundance of the protein contents from each 2D DIGE gel), then calculated for the four following comparisons: (i) rainy vs. dry condition in A. gambiae M; (ii) rainy vs. dry condition in A. gambiae S; (iii) A. gambiae M vs. A. gambiae S when exposed to rainy conditions; and (iv) A. gambiae M vs. A. gambiae S when exposed to dry conditions. For each ratio, differences in spot abundance were assessed using Student's t-tests on individual spots (P-value o0. 05). Accordingly, we observed that 15 proteins exhibited significant variations between the two rearing conditions in 1-h old females A. gambiae M, among which 10 were more abundant when females grew under the dry conditions (Fig. 2A ). In contrast, 14 proteins showed significant variations between the two rearing conditions in 1-h old females A.