dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods cDNA cloning and protein purification. We produced helicase domains of Homo sapiens UPF1 (295-914), Saccharomyces cerevisiae UPF1 (221-851) and Homo Sapiens IGHMPB2 (1-652), as well as the full-length form of IGHMPB2 (1-993, IGHMPB2-FL) (Uniprot accession codes Q92900-2, P30771, and P38935, respectively). Boundaries were defined according to previous structural studies  17, 19, 33. IGHMPB2, IGHMPB2-FL, and yeast UPF1 coding sequences were PCR amplified using oligos HLH 2696/2697, HLH 2696/2698, and HLH 2705/2706, respectively. Purified PCR fragments were inserted in a home-made variant of pET28a plasmid pHL5 (Novagen) between NdeI/NotI (IGHMPB2) and NheI/XhoI (yUPF1) restriction sites. To generate UPF1 mutants, we modified the wild-type coding sequence of plasmid pHL 1281 (yeast UPF1 helicase domain) through PCRs followed by ligation reactions using amplification oligos carrying the corresponding mutations at the targeted sites.
Peroxidase activity was monitored with the Clarity Western ECL chemiluminescence kit (Bio-Rad) and pictured on a Bio-Rad GelDoc imaging system. Image processing was performed with FIJI/ImageJ and consisted in contrast adjustment. Uncropped blots are presented in Supplementary Figure  8B. Single-molecule data acquisition. Camera images were used to collect the raw data of DNA extension. Raw data represented the real-time evolution of the DNA extension in nm, which was converted into the number of base pairs unzipped by helicases using a calibration factor determined from the elastic properties of ssDNA (Supplementary Figure  8C ). Instantaneous unwinding and translocation rates were obtained from a linear fit to the traces filtered with a least square method over a time window define by the user mouse.
Helicase concentration used is the lowest possible to observe single-binding events and varied between 1 and 20 nM. Single-molecule data analysis. For unwinding processivity, large processivity was determined by taking into account only those traces where helicase starts unwinding the hairpin in fully closed state until it falls off. This leads to a histogram with a few points corresponding to excursion shorter than the hairpin length and a last bin of this precise length with many events (Supplementary Figure  2C ). This truncated histogram was fitted to a truncated exponential providing a processivisity value usually larger than the hairpin length. For moderately processive helicase, the histogram of the length of unwound events was calculated. These histograms were exponential in nature, which gave the mean processivity of the helicase on fitting them.
Acknowledgements We thank  E. Conti  and  B. Séraphin  for plasmids,  I. Barbosa  and  M. H. Pontier  for experimental help,  F. Fiorini  for scientific advice,  O. Bensaude ,  A. Jacquier ,  N. Leulliot  and  D. Bensimon  for reading the manuscript, and members of our labs for discussion. This study was supported by the  ANR   CLEANMD grant  ( ANR-14-CE10-0014 ) from the  French Agence Nationale de la Recherche  to C. S. , V. C. , and H. L. H. , by the program « Investissements d'Avenir » launched by the  French Government  and implemented by  ANR  ( ANR-10-LABX-54   MEMOLIFE  and  ANR-10-IDEX-0001-02 PSL* Research University ) to J. K. and H. L. H. , by the  European Research Council  grant Magreps [267 862] to V. C. and S. R. , by the  Fondation ARC pour la recherche sur le cancer PhD  fellowship to J.
Data availability The data that support the findings of this study are available from the corresponding authors upon reasonable request. Single-molecule data supporting the findings of this study are available at http://pimprenelle. lps. ens. fr/Magnetic_tweezers_graphs_Kanaan. zip (2. 2 Go). Data analysis software (PlayItAgainSam) can be downloaded at (http:// www. picotwist. com/download/Pias_setup. exe). Figures  1b, c, 2c, d  and 3a -d in the main text have associated raw data. Supplementary Figures  2a, d , 3a, 4a-f, 6a-f, 7a, b and 8c have associated raw data.
(2013b). For each alcoholic fermentation, two types of phenotypic traits were measured or estimated from sophisticated dataadjustment models: 35 fermentation traits and 615 protein abundances. The For proteomic analyses, the samples were harvested at 40% of CO 2 release, corresponding to the maximum rate of CO 2 release. Protein abundances were measured by liquid chromatography-MS/MS techniques from both shared and proteotypic peptides relying on original Bayesian developments  (Blein-Nicolas et al. 2012). A total of 615 proteins were quantified in. 122 strains 3 temperature combinations, as explained in detail in  Blein-Nicolas et al. (2015). Cross-referencing the Munich Information Center for Protein Sequences (MIPS) micro-organism protein classification database  (Ruepp et al. 2004) , the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway classification  (Kanehisa and Goto 2000; Kanehisa et al.
2016 Kanehisa et al. , 2017)) , and the Saccharomyces Genome database  (Cherry et al. 2012) , we attributed each protein to a single functional category based on our expert knowledge (Supplemental Material, Table  S1 ). Considering the genes encoding the proteins, we also assigned to each protein a number of putative transcription factors (TFs). A total of 313 TFs with a consensus DNA-binding sequence were retrieved from the YEASTRACK database  (Teixeira et al. 2006 (Teixeira et al. , 2014;; Monteiro et al. 2008; Abdulrehman et al. 2011).
The fitting algorithm Fixed effects, variance components of the genetic effects, and their best linear unbiased predictors (BLUPs) were estimated using the hglm package in R  (Ronnegard et al. 2010) , which implements the estimation algorithm for hierarchical generalized linear models and allows fitting correlated random effects as well as random regression models by explicitly specifying the design matrices, both for the fixed and random effects. The model, based on a maximum-likelihood estimation, is deemed to produce unbiased statistics  (Gumedze and Dunne 2011). A separate analysis was conducted for each trait at each temperature, considering the vector of observations for the trait-by-temperature combination of interest, y, and rewriting the model (Equation  2 ) in matrix form: y ¼ Xb þ Zu þ e; ( 7 ) where X is the design matrix for the fixed effects; Z the design matrix for the random effects; b ¼ ðm; b S: uvarum ; b S: cerevisiae Þ and u ¼ ðA w ; A b ; B; H w ; H b Þ are the vectors of fixed effect parameters and random effect parameters, respectively; and e is the vector of residual errors.
Acknowledgments We thank  Arnaud Le Rouzic  for exciting discussions, and for his R code which helped us pursue the preliminary analysis of the diallel design. We thank  Monique Bolotin  for her help in the functional annotation of the proteins, and  Warren Albertin  and  Philippe Marullo  for their advice regarding yeast genetic material. This work was supported by a public Ph. D. grant from the  French National Research Agency (ANR)  as part of the  Investissement d'Avenir program , through the  Initiative Doctoral Interdisciplinaire  (IDI) 2015 project funded by the  Initiative d'Excellence (IDEX) Paris-Saclay ,  ANR-11-IDEX-0003-02.
The spectral Scheme 1 Preparation of the supramolecular Fe III TPPS/Cu II TerpyCD 2 complex. changes were completed at one equivalent of Cu 2+. The complexation between TerpyCD 2 and Cu 2+ was also monitored by electrospray mass spectroscopy. In the 1 : 1 mixture of CuSO 4 and TerpyCD 2 in H 2 O, the 1 : 1 complex (Cu II TerpyCD 2 ) was observed at m/z 1577 and 1059 (Fig. 2b ), which corresponds to [Cu II TerpyCD 2 ] 2+ and [(H 2 O)Cu II TerpyCD 2 + H] 3+ , respectively. The 1 : 2 complex was also detected as a small ion peak when the 1 : 2 mixture of CuSO 4 and TerpyCD 2 in H 2 O was analysed by electrospray mass spectroscopy (data not shown). The Cu II TerpyCD 2 complex was then titrated with Fe III TPPS (Fig. 3a ). The Soret band of Fe III TPPS shied from 408 nm to 418 nm, indicating that a m-oxo-dimer of Fe III TPPS dissociated to the monomeric monohydroxo complex (Fe III (OH À )TPPS)  19  through interaction with Cu II TerpyCD 2.
Acknowledgements We thank (late)  Prof. Takashi Ogura  ( University of Hyogo ) for the use of rR instruments and helpful discussion. This work was nancially supported by  MEXT/JSPS KAKENHI  (Grant No. 15H02569 ,  16K13092 ,  17H02208 ), the  MEXT-Supported Program  for the  Strategic Research Foundation at Private Universities  ( 2015-2019 ), the  Naito Foundation ,  Iketani Science and Technology Foundation , and  Suntory Foundation for Life Sciences. JW and TH thank nancial support from the bilateral  France-Japan ANR-JST program TMOL"Molecular Technology" project  " MECANO "  AI\R. 1 4. JTIC. OOO2. O 1.
Acknowledgments-We are thankful for the valuable help of Daniel Picot and Francesca Zito (H 6 F 5 ), as well as Manuela Zoonens (amphipol) and Olivier Vallon (⌬ANR1). The Proteomic Platform of IBPC (PPI) was supported by CNRS Grant EQUIPEX CACSICE ANR-11-EQPX-0008.
The intensity of transcriptome fluorescence was determined with the use of Affymetrix Gene Array Scanner 3000 7G and Gene Chip ® Command Console ® Software (Affymetrix, Santa Clara, CA). Table  1. PASI values for patients under study observed during various monitoring tests miRNA microarray procedure Expression profile of genes encoding microRNA molecules was determined with the use of microarray Gene Chip® miRNA 2. 0 Array (Affymetrix), according to the producer's protocol (FlashTag™ Biotin HSR RNA LabelingKits). 750ng of total RNA was used for the analysis. Evaluation of the staining efficiency was made with the use of ELOSA QC Assay (FlashTagBiotin HSR RNA Labeling Kit; Affymetrix), according to the producer's protocol. RNA hybridization with the microarray was performed for 16h in temperature 48°C at 60 RPM (rotations per minute).
Statistical and bioinformatic analyses The comparative analysis covering the results of the oligonucleotide microarray experiment was performed with the use of PL-Grid Infrastructure and GeneSpring 12. 6. 1 program (Agilent Technologies, Inc. , Santa Clara, CA, USA). The statistical analysis of results obtained from miRNA microarray was conducted with the use of miRNAQCTool version 1. 1. 10 (Affymetrix) and Transcriptome Analysis Console 2. 0 (Affymetrix) software. The miRNA molecules that have potential ability to regulate expression of genes encoding caspases were identified in the microRNA database (http://www. microrna. org). The miRanda-mirSVR algorithm, a combination of the miRanda and mirSVR algorithms, was used to find the relevant miRNA molecules. The statistical analysis of qRT-PCR results was performed with the use of Statistica 12 PL (StatSoft, Tulsa, Oklahoma, USA).
Bioinformatic analysis In order to identify which out of 24 differentiating miRNA molecules may regulate the expression of genes encoding caspases (CASP1, CASP2, CASP3, CASP4, CASP6 and CASP9), miRanda-mirSVR algorithm was used, based on microRNA. org database (http://www. microrna. org). The analysis revealed 5 different miRNA molecules regulating the expression of: CASP2 (three miRNAs), CASP3 (two miRNAs) and CASP6 (one miRNA) (Table  5 ). 2 hours after adalimumab administration, 3 miRNAs showed overexpression: hsamir-34a, hsa-mir-377, hsa-mir-106a potentially capable of regulating expression of gene encoding caspase-2. At the same time, lower expression of the tested gene was observed. After 8hours, expression of the gene encoding caspase-2 reached the level observed in the control. The hsa-mir-106a molecule also shows potential capability to regulate caspase-6.
The mechanism underlying these changes may be connected with miRNA molecules. For the analysis 19 miRNA molecules were chosen from the microRNA. org (http://www. microrna. org) database. According to miRanda-mirSVR algorithm -may be capable of regulating the gene encoding caspase-6, among them being Hsa-mir-106a molecule. Hsa-mir-106a plays either the role of neoplastic process suppressor, or an oncogene, depending on tissue type  [41] [42] [43]. The studies showed that the molecule is capable of promoting apoptosis  [44]. The mirSVR software interaction analysis for hsa-mir-106a and caspase-6 indicates poor consensus for interaction between target mRNA and miRNA molecule. However, the obtained results show certain correlation between the direction of expression of both molecules. The correlation between the direction of expression of the gene encoding caspase-6 and hsamir-106 gene may indicate the regulating role of this miRNA, however this thesis requires further studies.
Acknowledgements MJŁ kindly acknowledges the support from  NCN  grant #:  2016/21/B/NZ1/02812 , the supported by  LE STUDIUM Institute for Advanced Studies (region Centre-Val de Loire, France)  through its  Smart Loire Valley General Program , co-funded by the  Marie Sklodowska-Curie Actions , grant #  665790. CG kindly acknowledges the support by the  FUI16 grant 'GLYCOSKIN I'  from the  Pole of competitiveness Cosmetic Valley , the  French Ministry for Industry and the region Centre-Val de Loire  ( BPI n°131   1004 F/ Region CVL n°2014-00090160 ).
RNA-Seq data analysis and bioinformatics Raw read files have been stored in ng6  [46]  and were checked using fastQC  [47]. The sequencing adapters were removed using cutadapt (version 1. 8. 3, standard parameters)  [48]. The reads were then aligned to EDL933 genome (Genbank accession numbers NZ_CP008957. 1 and NZ_CP008958. 1) [49]  using bwa mem (version 0. 7. 12-r1039, standard options)  [50]. Reads were counted using featureCount (version v1. 4. 5-p1)  [51]. Read counts corresponding to the 131 rRNA, tRNA and ncRNA genes were excluded  [49]. Differential gene expression was then performed using DESeq2 version 1. 12. 4  [52]  with R version 3. 3. 2 [53]  following the standard workflow. The DESeq2 method internally corrects for library size and uses negative binomial generalized linear models to test for differential expression.
All genes with a log2 fold-change (log2FC) in expression greater than 2 and a Benjamini-Hochberg adjusted p-value (q-value) smaller than 0. 05 were reported as differentially expressed. For further analysis, additional gene annotations from older published EDL933 chromosome and plasmid sequences were collected (Genbank accession numbers CP008957. 1 and  [49] ; AE005174. 2 [54]  and AF074613. 1  [55] ). Additional gene annotations were also performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG). Differentially expressed genes were assigned to functional categories of Clusters of Orthologous Groups (COGs) of proteins using blastp against the NCBI COG 2014 database  [56]. RNA-seq data have been deposited under SRA accession SRP136076. Note that the fold change was first calculated by comparing the expression ratio of each gene from a specific DC relative to M9-Glc.
Statistical Analysis Statistical analyses of the growth and survival of E. coli strains in DCs were analysed by the two-way ANOVA with the Bonferroni post-hoc test with p < 0. 05 considered as significant. For RT-qPCR, Student t-test and the one-way ANOVA with the Tukey post-hoc test were used to identify significant differences in gene expression (p < 0. 05 was considered as significantly different). The statistical analysis of RNA-seq data is presented above. PCA plot was generated using plotPCA function from DESeq2 package version 1. 12. 4 and ggplot2 version 2. 2. 0.
Acknowledgments The authors thank  Laura Quentin  for excellent technical assistance. We thank the  Herbipole (Abattoir)  personnel for their help in collecting the bovine gastrointestinal contents. The sequencing was performed in collaboration with the  GeT core facility, Toulouse, France  (http://get. genotoul. fr). We are also grateful to the  Genotoul bioinformatics platform Toulouse Midi-Pyrenées (Genotoul Bioinfo)  for providing computing and storage resources. The authors thank  Dr RJ Forster  for careful reading and editing of the manuscript.
Funding This work was supported by  INRA. AS was the recipient of an  INRA-Région Auvergne PhD fellowship  (contract  23000731 ). The GeT sequencing platform was supported by  France Genomique National  infrastructure, funded as part of " Investissement d'Avenir" program  managed by  Agence Nationale pour la Recherche  (contract  ANR-10-INBS-09 ). The funding bodies had no role either in the design, analysis and interpretation of data, or in the writing of the manuscript.
As expected, in cells undergoing a synchronous S phase after release from G2 arrest, we observed that Cdc45 binds to the efficient origin ori2004 at an earlier time than to the inefficient origins oriJW1072 and oriJW1088 (Fig 3A, left panel). However, this was not the case in the G1+15 condition, where Cdc45 bound similarly to all three origins (Fig 3A, right panel). These data suggest that extending G1 results in a redistribution of limiting replication components. We then asked whether these alterations in pre-IC formation occur throughout the genome and contribute to the changes in efficiencies that are observed after a G1 extension. To this end, we performed chromatin immunoprecipitation followed by microarray analysis (ChIPchip) of Cdc45 in the G2B and G1+15 conditions. For these experiments, cells were collected at the G1/S transition, when Cdc45 recruitment reaches a maximum at early-firing origins ( shows very different distributions for the deviations of the regional profiles of origin efficiencies and Cdc45 from their corresponding means in G1+15 compared to G2B.
Assessment of DNA content by flow cytometry For DNA content analysis, cells were fixed in 70% cold ethanol, washed in 50 mM sodium citrate and treated with RNase A (0. 1 mg/ml). Samples were then stained using 2 mg/ml propidium iodide, sonicated, and analyzed using a BD Accuri C6 flow cytometer (BD Biosciences, Franklin Lakes, NJ, USA) and the Flowjo analysis software (FlowJo LLC, Ashland, OR, USA). Note that in contrast to a number of model systems, the fission yeast cell cycle displays a short G1 phase, with S phase occurring prior to cytokinesis. Cells therefore have a 2C DNA content during most of their life cycle: in proliferating cells, genome duplication takes place in binucleated cells, giving rise to a transient 4C peak that is then resolved upon cytokinesis. In asynchronous cultures, this 4C peak is barely detectable due to the low percentage of cells undergoing DNA replication at any given time in such populations.
For S2. 5, S4, and S6, the same protocol was used with the indicated concentrations of 3-MBPP1. Samples were then collected at the following times after G1 release: 2. 5 μM-40 min, 4 μM-45 min, and 6 μM-60 min. These different timings accommodate the changes in S phase length in these experiments (Fig  5A ). To exclude the possibility of a bias in our method due to maintaining cells for different periods of time in HU, we assessed whether the duration of HU exposure has an effect on origin efficiencies. To this end, we collected samples from Cdc13-Cdc2 cells released from G2 arrest and maintained in 24 mM HU for 30 min longer than the time indicated for G2B above (cells were therefore collected 90 min after HU addition; referred to as G2B+30). Our data showed that this does not alter the replication program and that any differences in origin efficiencies between G2B and G2B+30 are not statistically significant (S2 Table, S2C Fig).
The datasets were averaged, and the outliers removed. For each experiment, two biological repeats were performed and averaged. For analysis and comparison of origin efficiencies, the baselines between different conditions, representing unreplicated DNA, were matched and set to 1. For this correction, we surmised that 1) as cells are in HU, there are genomic regions that remain unreplicated in our S phase samples and 2) the unreplicated regions of the genome have the lowest values in each dataset. Thus, for each averaged dataset of biological repeats, we took the lowest 10% of the ratios of replicated/unreplicated DNA, calculated their median, and normalized the dataset to this value. The following correction factors were applied: Control, 0. 17; Cdc13-Cdc2, 0. 13; G2B, 0. 1; G2B+30, 0. 12; G1+5, 0.
Origin identification and replication program analyses S1 Table provides the list of origins and efficiencies in all analyzed conditions. For origin identification in Control (cdc25-22) and Cdc13-Cdc2 cells (Fig 1, S1 Table ), we determined the moving geometric means over five consecutive probes along the chromosomes for each dataset representing the average of two biological repeats. This procedure was then repeated on the resulting datasets for a total of 10 rounds of smoothing. Origins were then identified by the peaks where the copy number ratio of replicated (in HU) vs. unreplicated was greater than 1. 05, representing a clear increase over the background after smoothing. Their positions were attributed based on the local maxima followed by visual confirmation. Once the coordinates of the origins were determined, their efficiencies were obtained from the non-smoothed datasets, and a threshold of 1.
1 for the copy number was applied. This was further confirmed by visual analysis of the data. When two origins were less than 5 kb apart, they were considered as a single origin and assigned to the position with the higher copy number. This approach is validated by the observation that the extent of replication surrounding an origin in HU can reach 10-15 kb  [31]. To compare origin numbers, efficiencies, and positions between two experiments, peaks identified in each dataset were matched in position  [4]. Indeed, the precise location of the same origin can vary slightly between experiments, due in part to the resolution of the microarrays and to the extension of replication beyond the initiation sites in these assays. Therefore, when peaks in two different datasets were within a distance of less than 5 kb, they were considered to be the same origin.
For the comparisons of origin efficiencies in Figs 2-5 and S2-S5 Figs, we further refined our origin list. Indeed, as we observed an induction of some inefficient origins in the presented conditions, we visually inspected the averaged and baseline-corrected G1+165/S1 dataset and noted origins with a copy number ! 1. 1 that were not in our initial list. This resulted in an additional 48 origins that were then added to the list identified in Cdc13-Cdc2 above (S1 Table ). For the analyses in Figs  2 3 4 5 , we therefore used the positions of this larger set of 670 origins and assigned the local maximum values in regions of 5 probes surrounding each origin as their efficiencies. To determine the regional profiles of origin usage (Figs 1F, 2E, 2F, 3D, 4D, 4E, 5D, 5E, S2D and S5F), the averages of origin efficiencies were determined for continuous windows of ~250 kb (1000 probes) along each chromosome.
Coverslips were washed 5 times for 2 min with PBST between antibody incubations. Dako fluorescence mounting medium (Agilent Technologies, Santa Clara, CA, USA) was then added to samples prior to imaging. Imaging was performed using a 63x objective on an inverted Zeiss Axio Observer (Carl Zeiss AG, Oberkochen, Germany) equipped with a Lumencor Spectra X illumination system (Lumencor Inc. , Beaverton, OR, USA). Images were acquired with a Hamamatsu Orca Flash 4. 0V2 sCMOS camera (Hamamatsu Photonics, Hamamatsu City, Japan) via the VisiView software (Visitron Systems GmbH, Puchheim, Germany). Fibers were analyzed using Fiji and the Pointpicker plugin. Based on previous studies  [54]  and our control experiments, a threshold of 0. 6 kb (3 pixels) was used as a minimum for positive BrdU staining, and a cutoff of 3 kb (15 pixels) was used to identify stretches of non-BrdU labelled DNA.