dataesr/scientific-paragraphs-categorization huggingface dataset:
Immunoprecipitated calnexin was expressed as a ratio of the signals of calnexin and cystinosin-EGFP using BIO-1D software (Vilber Lourmat). Statistical analysis using the Kruskal-Wallis test (p Ͻ 0. 05) with post-hoc Dunn's test was performed with Prism version 5 (GraphPad Software, La Jolla, CA). The threshold for statistical significance was set at p Ͻ 0. 05. Each bar represents the mean Ϯ S. E. from four independent experiments. Immunofluorescence-NIH/3T3 cells were plated on glass coverslips in 12-well tissue-culture dishes, then 24 -38 h later were treated as previously described  (5). For the colocalization analysis, the reticular and lysosomal compartments were defined by delimiting the PDIor Lamp1-labeled areas, respectively, using ImageJ 1. 50 software. The reticular or lysosomal accumulation of cystinosin-EGFP or its mutants was measured as a ratio, expressed in percentage of GFP fusion proteins that colocalized with the PDI-or Lamp1-labeled areas.
Statistical analysis using the Kruskal-Wallis test with post-hoc Dunn's test was performed with Prism version 5 (GraphPad software). The threshold for statistical significance was set at p Ͻ 0. 05. Three independent biological replicates were analyzed, and for each experiment, colocalization GFP/PDI or GFP/Lamp1 for five cells of each cell type was performed. PNGase F and Endo H Treatments-Cell lysates were treated for 1 h at 37 °C with 100 U of PNGase F or Endo H (BioLabs, Evry, France) per g of total proteins according to the manufacturer's recommendations except for the denaturation step, which was done at 50 °C instead of 100 °C. The resulting cell lysates were analyzed by Western blotting. Drug Treatment and FACS Analysis-NIH/3T3 cells stably expressing WT cystinosin-EGFP and ⌬ITILELP-EGFP were incubated with 10 M clasto-lactacystin ␤-lactone (Calbiochem) or 100 nM bafilomycin A1 (SIGMA) for 24 h in the complete DMEM medium (Merck Millipore, Alsace, France).
Cells were then harvested by using trypsin, and centrifuged for 2. 5 min at 1500 rpm. The cell pellets were washed once with PBS and resuspended in 500 l PBS with 1% bovine serum albumin. EGFP expression was analyzed by direct flow cytometry using FACSCalibur with CellQuest software (BD Biosciences). Dead cells and debris were excluded by gating on forward/side light scatter, and 10,000 events corresponding to EGFP-positive cells were analyzed per sample. The average expression level of EGFP in individual NIH/3T3 cells was determined by calculating the geomean fluorescence intensity (GFI). The GFIs of the treated cells were normalized relative to the GFI of the untreated cells (with DMSO vehicle). Statistical analysis using the Kruskal-Wallis test with post-hoc Dunn's test was performed with Prism Version 5 (GraphPad software).
Raw files were processed using the Proteome Discoverer 1. 4. 0. 288 software (Thermo Scientific, San Jose, CA) and searched using Mascot 2. 2 against the Mus musculus Uniprot KB/Swiss-Prot database v. 5/7/2012 (16,331 entries) integrated with the sequences of the EGFP cystinosin WTs and mutants. Search parameters included fixed modification with carbamidomethyl (C) and variable modifications with oxidation (M), Label: 13 C(6) (K), Label: 13 C(6) (R), and two missed cleavages. The enzyme was trypsin, monoisotopic peptide mass tolerance was Ϯ2 ppm, and fragment mass tolerance was Ϯ0. 05 Da. The identification validation was performed using a percolator, allowing 1-5% of target FDR for peptides based on the q-value and determined by the target-decoy approach. The mass spectrometry proteomics data have been deposited at Panorama Public (https://panoramaweb.
org/labkey/Guerrera. url). The MS raw files related to the turnover study have been deposited at the ProteomeXchange Consortium via the PRIDE (  17 ) partner repository with the data set identifier PXD005357. Turnover Data Analysis-The loss of a protein can be calculated from the relative isotope abundance (RIA) at each time point. The RIA represents the portion of the residual preexisting protein (MS1 area of the light peptides: A L ) versus the total amount of the protein in the cell, both pre-existing (A L ) and newly synthesized (MS1 area of the heavy peptides: A H ), at each time point. RIA is therefore calculated as: RIA L,t ϭ A L,t A H,t ϩ A L,t The MS1 areas A H and A L were extracted for peptides SVSLTVP-PVVK, EDGNILGHK, FEGDTLVNR and FSVSGEGEGDATYGK for cystinosin-EGFP using Skyline v2.
For each experiment, three to four peptides were measured at 0, 6, 18, and 24 h (details in supplemental Table  S1 ). Each mutant was always run in parallel with WT cystinosin-EGFP. The cell-growth rate (D) was measured in at least two independent experiments for each sample (four for WT cystinosin-EGFP, two each for ⌬ITILELP, N 323 K, N 288 K, and N 1 N 7 ) (details in supplemental Table  S3 ). Statistical analysis using one-way ANOVA was performed with Prism version 7 (GraphPad software), using K loss values obtained by curve-fitting and formulas, from at least three independent experiments and three to four peptides per time point. The threshold for statistical significance was set at p Ͻ 0. 05. Each bar represents the mean Ϯ S. D. Mass Spectrometry Analysis for Interactomic Study-For interactomic analysis, coimmunoprecipitated protein samples were concentrated on top of 10% SDS-PAGE gel and excised.
In-gel trypsin digestion was performed as previously described  (5). Analysis by LC-MS/MS was performed as described in "Mass spectrometry (MS) analysis for turnover study," except for gradient length, which was 120 min rather than 38 min. The mass spectrometry proteomics data and details on the identifications and quantifications have been deposited at the ProteomeXchange Consortium via the PRIDE  (17)  partner repository with the data set identifier PXD004948. Interactomic data analysis of the MS files was processed with MaxQuant software version 1. 5. 2. 8 and searched with the Andromeda search engine against the Mus musculus Uniprot KB/Swiss-Prot database 2016_01 (20199 entries), integrated with FASTA sequences of WT cystinosin-EGFP. To search parent mass and fragment ions, we set initial mass deviations of 4.
Protein quantification was obtained using at least two peptides per protein. For pathway analysis, we annotated all of the proteins according to the KEGG database, and selected those classified as "protein processing in endoplasmic reticulum. " Three biological replicates were analyzed for WT cystinosin-EGFP, ⌬ITILELP-EGFP and controls (without GFP). Statistical analysis between WT cystinosin and ⌬ITILELP was performed after the filteringout of all proteins identified the negative control with Perseus software (version 1. 5. 5. 3, freely available at www. perseus-framework. org) (20). For statistical comparison, the LFQ data were transformed in log2 and imputed to fill in the missing data points by creating a Gaussian distribution of random numbers with a standard deviation of 33% relative to the standard deviation of the measured values, and a three-standard-deviation downshift of the mean to simulate the distribution of the low signal values.
For each experiment, three to four peptides were measured at 0, 6, 18, and 24 h (details in Table  S1 ). Each mutant was always run in parallel with WT cystinosin-EGFP. The cell-growth rate (D) was measured in at least two independent experiments for each sample (four for WT cystinosin-EGFP, two each for ⌬ITILELP, N 323 K, N 288 K, and N 1 N 7 ) (details in supplemental Table  S3 ). Statistical analysis using one-way ANOVA was performed with Prism version 7 (GraphPad software) using K loss values obtained by a formula (threshold p Ͻ 0. 05) (details in supplemental Table  S4A ). For MS-based interactomic studies, we performed three independent biological replicates for WT cystinosin-EGFP, ⌬ITILELP-EGFP and controls (cells without GFP expressing plasmid). Statistical analysis between WT cystinosin and ⌬ITILELP was performed with Perseus software (version 1.
Detection of EGFP-tagged Cystinosin Peptides by MS-To analyze the turnover of WT and mutated cystinosin, we transduced 3T3 cells with lentiviral constructs to stably express the cystinosin-EGFP fusion protein and its mutated forms. The schematic representation of WT cystinosin and ⌬ITILELP can be found in Fig. 1A  and 1B. We consistently identified specific cystinosin peptides in accordance with the WT or the mutated forms N 288 K and N 323 K (supplemental Fig. S1 ). In all analyzed forms of cystinosin, we could identify the peptide AA23-33 (SVSLTVPPVVK). These data suggest that the signal peptide of cystinosin was cleaved after S22, generating a new N-terminal sequence starting with SVSLTVPPVVK, despite previous reports describing an uncleavable signal peptide  (3, 21). Our data agreed with the prediction of the currently used algorithm (http://www.
S4 ). Together, these data indicate that the ⌬ITILELP mutation leads to partial retention of cystinosin in the ER because of misfolding of the protein. N-linked High-mannose Glycans of ⌬ITILELP-Lysosomal transmembrane proteins are synthesized in the ER, where folding and the initial stages of glycosylation (high-mannose type glycans) occur. They are then targeted to lysosomes via FIG. 3. Degradation curves of cystinosin and its mutants. RIA (relative isotope abundance, y axis) for each time point (x axis) were plotted using GraphPad software. The curve in black represents the degradation of WT cystinosin, whereas the curve in red represents the degradation curve of the indicated mutant. The histograms represent the loss rate (K loss ) for each protein and its two components: rate of degradation (K deg ) and cell-division rate (D).
5. Protein partners for WT cystinosin and ⌬ITILELP in lysosomal and reticular compartments. Volcano plot representation of proteins immunoprecipitated by the anti-GFP antibody and identified by mass spectrometry in ⌬ITILELP-EGFPversus cystinosin-EGFPexpressing cells. In blue, proteins known to be lysosomal partners of cystinosin. In red, proteins classified by the KEGG pathway database as "protein processing in endoplasmic reticulum". complex and some proteins of the mTOR pathway. The presence of functional ⌬ITILELP on the lysosomal membrane aligns with the fact that patients with homozygous ⌬ITILELP mutations develop juvenile rather than infantile cystinosis. In addition, ⌬ITILELP interacts with 15 proteins of the ER, most of which have been implicated in ER quality-control and processing (e.
Acknowledgments-We thank  Prof. R. J. Beynon  for valuable advice in turnover data interpretation;  Dr. P. Codogno  and  Dr. A. Delaunay-Moisan  for thoughtful discussions; and  N. Gaudin ,  M. Garfa-Traore , and  R. Desvaux  at  Imagine Cell Imaging  for expert assistance in confocal microscopy. * This work was supported by grants from the  Cystinosis Research Foundation  and from the  Investments for the Future program  ( ANR-10-IAHU-01 ). □ S This article contains supplemental material. ¶ ¶ To  whom correspondence should be addressed: Hopital Necker, site Broussais 14  Rue Maria Helena Vieira Da Silva. 75993 Paris cedex 14. Tel. :  01 72 60 63 31 ; E-mail: chiara. guerrera@inserm. fr. ʈʈ These authors contributed equally to the work.
As a control population, 100 healthy women, 20-39 years of age were included in the present study belonging to the VARIETE cohort which was an open, prospective, national, multicenter, nonrandomized study of healthy volunteers, designed to establish normative data for hormones (ClinicalTrials. gov Identifier: NCT01831648). All women had normal age of puberty onset, body mass index, and regular spontaneous menstrual cycles  (27-31 days). None had clinical signs that could suggest POI. All healthy subjects gave their written informed consent to participate in the study, which was approved by the Paris-Sud Ethics committee before the beginning of the study.
5 mM MgCl 2 , 0. 2 mM dNTPs, 0. 06 μM concentrations of each primer, 100 ng of genomic DNA, and 1. 25 U of Dream Taq (Fermentas) in a total volume of 50 μL. The amplified samples were subjected to agarose gel electrophoresis to ensure single bands. PCR products were purified, then sequenced on an automated sequencer (3100 Applied Biosystems) using the same primers as for PCR by Sanger Method. Nucleotide numbering reflects cDNA numbering (http://www. HGVS. org/varnomen) with +1 corresponding to the A of the ATG translation initiation codon (codon 1) in the reference sequence. Ethnically-matched controls from the 1000 Genomes Project Database  [20]  and the Exome Aggregation Consortium  [21]  were used for individual variant and gene mutation frequencies in this study.
Molecular modeling PolyPhen (Polymorphism Phenotyping, http://genetics. bwh. harvard. edu/pph2/; http://www. HGVS. org/varnomen) and Sorting Intolerant From Tolerant (SIFT; http:// sift. bii. a-star. edu. sg/) software packages as well as evolutionary conservation were used to predict the pathogenic nature of the coding sequence alteration. Alternative Splice Site Predictor (ASSP) (http://wangcomputing. com/assp/), Netgen2 (http://www. cbs. dtu. dk/ services/NetGene2/), NNSplice. v. 0. 9 (http://www. fruitfly. org/seq_tools/splice. html), Human Splicing Finder v. 2. 4. 1 (http://www. umd. be/HSF/) software were used to predict splicing site of intronic variations.
Identification of SNPs in the RSPO2 locus Analysis of the RSPO2 gene in 100 POI patients revealed the presence of 9 sequence variations identified in the non-coding and coding portions (Fig. 2  and Table  3 ). All 9 variations were heterozygous and 4 of them were equally homozygous. Among them, 4 of these SNPs have never been described (c. -359C > G, c. -190G > A, c. -170 + 13C > T and c. -169-8 T > A). Of the 9 identified SNPs, only one c. 557 T > C was predicted to alter a single amino acid in the RSPO2 protein (p. Leu186Pro). This variant was found heterozygous in 51% and homozygous in 20% of patients and was also described as very common in controls by Exome variant server and the 1000 Genomes. It is predicted as non-deleterious by Polyphen and SIFT softwares and hence clearly could be considered as not disease-causing.
The results of our topological analysis of biologically realistic digital  reconstructions provide a convincing argument for considering topology as a useful mathematical tool for analyzing the structural and functional connectome of neural circuits. FR 1D 2D β 0 β 1 β 2 EC FR 1D 2D β 0 β 1 β 2 EC FR 1D 2D β 0 β 1 β 2 EC FR 1D 2D β 0 β 1 β 2 EC Our results lead naturally to many new questions, most notably concerning the biological significance of the high-dimensional simplices and homology classes we have discovered in the digitally reconstructed neocortical microcircuits. We intend to explore these questions in future studies. In particular we hypothesize that the time series of different topological metrics could reveal an evolving spatio-temporal code that goes beyond either rate or timing information to one that incorporates the structural organization.
4. 1. Computation of flag complexes and their Betti numbers. We represent the directed flag complex of a directed graph by a reference-based data structure, using vectors to store the references to the simplices in the simplicial complex. The required storage space grows linearly with the number of vertices and with the number of edges. A publicly available C++ implementation of the code will be available on http://neurotop. gforge. inria. fr/. All homology computations carried out for this paper were made with F 2 coefficients, using the boundary matrix reduced by an algorithm from the PHAT  [2]  library. For further details, please see (SI, Supplementary Text, ST2). 4. 2. The Point vs. Circle experiment. The stimulated reconstructed microcircuit is innervated by 310 VPM fibers, whose horizontal centers of innervation are evenly distributed over the microcircuit (one fiber per mini-column).
4. 3. Computation of transmission-response matrices. Transmission-response matrices were calculated according to the specifications mentioned above, using a custom-written program in the Python programming language. It combined the matrix of synaptic connections (structural matrix), constructed as part of the standard reconstruction process of the BBP, with the spiking output of a simulation run and user-defined values for time steps ∆t 1 and ∆t 2 (5 and 7. 5 ms in our analyses). For further details, please see (SI, Supplementary Methods ,SM1).
ST2. Data structures and algorithms In this section we describe our basic data structures and provide a detailed overview of the algorithm that constructs the directed flag complex associated to a directed graph. We also indicate briefly how our homology computations were performed. A publicly available C++ implementation of the code will be available on http://neurotop. gforge. inria. fr/. ST2. 1. Data structures. We represent an admissible Hasse diagram H corresponding to the directed flag complex of a directed graph G = (V, E, τ ) by a reference-based data structure, using vectors to store the references to the vertices of the diagram. Each vertex v ∈ H stores the following information. (1) Ver(v): A vector of the vertices of G determining the simplex of the flag complex to which v corresponds.
Acknowledgments This work was supported by funding from the  ETH Domain for the Blue Brain Project (BBP). The BlueBrain IV IBM BlueGene/Q system is financed by  ETH Board Funding  to the  Blue Brain Project  and hosted at the  Swiss National Supercomputing Center (CSCS). We thank  Ahmet Bilgili  for providing the visualization of the microcircuit in Figure 1. Partial support for P. D. was provided by the  GUDHI  project, supported by an  Advanced Investigator Grant  of the  European Research Council  and hosted by  INRIA. M. S. was supported by the  NCCR Synapsy of the Swiss National Science Foundation.
In bacteria, small non-coding RNAs (sRNAs) are critical regulators of cellular adaptation to changes in metabolism, physiology, or the external environment. In the last decade, more than 2000 of sRNA families have been reported in the Rfam database and have been shown to exert various regulatory functions in bacterial transcription and translation. However, little is known about sRNAs and their functions in Mesorhizobium. Here, we predicted putative sRNAs in the intergenic regions (IGRs) of M. huakuii 7653R by genome-wide comparisons with four related Mesorhizobial strains. The expression and transcribed regions of candidate sRNAs were analyzed using a set of high-throughput RNA deep sequencing data. In all, 39 candidate sRNAs were found, with 5 located in the symbiotic megaplasmids and 34 in the chromosome of M.
huakuii 7653R. Of these, 24 were annotated as functional sRNAs in the Rfam database and 15 were recognized as putative novel sRNAs. The expression of nine selected sRNAs was confirmed by Northern blotting, and most of the nine selected sRNAs were highly expressed in 28 dpi nodules and under symbiosis-mimicking conditions. For those putative novel sRNAs, functional categorizations of their target genes were performed by analyzing the enriched GO terms. In addition, MH_s15 was shown to be an abundant and conserved sRNA.
, 2007; Livny, 2007; Livny and Waldor, 2007). Subsequently, more complex combined approaches have been used to predict sRNAs by comparative genomics. More recently, transcriptional signs-based methods that include the prediction of transcription factor binding sites, promoters and terminator signals have changed the focus toward the predicting bacterial genomic transcription units  (Chang et al. , 2010; Pellin et al. , 2012; Sridhar and Gunasekaran, 2013; Su et al. , 2014; Tsai et al. , 2015). In rhizobia, Sinorhizobium meliloti is the first strain in which sRNAs in IGRs were screened by comparative genomic sequences from eight related alpha-Proteobacteria using the programs eQRNA and RNAz programs as predictive tools. Eight of the original 32 candidates were confirmed to express small transcripts by using Northern blotting experiments  (del Val et al.
, 2014). Subsequently, the transcriptomes of M. huakuii 7653R in bacteroids and free-living cells were analyzed and compared using RNA-seq and microarrays. However, due to the low sequencing depth, the detected genes were limited to mRNAs (≥200 bp)  (Peng et al. , 2014). In this study, we predicted the existence of novel sRNA genes in the IGRs of M. huakuii 7653R via genome-wide comparisons with four related mesorhizobial strains, including M. huakuii bv. loti MAFF303099, M. ciceri bv. biserrulae WSM1271, M. australicum WSM2073 and M. opportunistum WSM2075. The transcription units of the predicted sRNAs were further analyzed basing on the high-throughput deep sequencing data of the Mesorhizobium huakuii 7653R global transcriptome, and the expression profiles of the nine selected sRNAs under diverse stress conditions were revealed using Northern blotting.
Prediction of Potential sRNAs in the IGRs of M. huakuii 7653R The IGRs of M. huakuii 7653R with a length ≥50 nt are 5125, and these were compared with four other Mesorhizobial strains by WU-BLASTN to identify conserved candidate sequences in the IGRs. The resulting homologies of IGRs revealed by these genome-wide comparisons were further analyzed. Raw data from ∼1,500 sequences were detected after WU-BLAST analysis. Each of the conserved intergenic regions in these candidate sequences was scanned individually using eQRNA and RNAz, and overlapping sequences were determined. Meanwhile, the promoters and Rho-independent terminators were predicted using Promoter 2. 0, RNAMotif and Erpin, and the resulting information was used to further assess the sequences mentioned above (Tables  S1, S2 ). Finally, 40 conserved sequences were identified as potential sRNAs (Table  1 ).
To find the annotated known sRNAs in 7653R, we performed Rfam searches using the above potential sRNAs. Of the 40 predicted sRNAs, 24 were annotated in the Rfam database, thus, the other 16 sRNAs are regarded as putative novel sRNAs (Table  1 ). Several known sRNAs, which are highly conserved in bacteria, were successfully predicted, including 6S RNA (MH_32), 4. 5S (MH_s40), RNase P class A (MH_s20), and tmRNA (MH_s19), suggesting that the methods used in this study worked well. A few glycine, cobalamin and TPP riboswitches were also detected. Notably, most of the sRNAs (35 of 40) were located on the chromosome, with only a few (three in pMhu7653Ra and two in pMhu7653Rb) located on  "no" indicates that the predicted sRNA was not detected in sequencing products. "un" indicates that the candidate sRNA was not annotated in the Rfam database and its function is unknown.
Predicted sRNA Transcription Units As noted above, the genomic comparison identified 40 potential sRNAs. Further analyses were conducted to describe their transcription units and transcriptional start regions using Illumina high-throughput sequencing data. These analyses were performed on RNA isolated from 28-and 50-dpi (days postinfection) symbiotic nodules and free-living cells of 7653R (see Materials and Methods) that were treated with TAP. The depths of RNA-seq were shown in Tables  S3, S4. A comparison of the analysis between the predicted sRNAs and RNA-seq products showed that, apart from 5 riboswitches, 19 functional annotated sRNAs and 15 of 16 putative novel sRNAs were detected in the sequencing dataset, and only one was not expressed in plant nodules or in free-living cultured cells (Table  S5 ).
Experimental Verification of Nine Selected sRNAs by Northern Blotting To confirm the existence of the predicted novel sRNAs and to understand their basic functions, 9 of the 15 potential novel sRNAs were selected randomly and detected by conducting Northern blotting on the total RNA extracted from cultured cells grown under non-stressed and stressed conditions (Figure  3 ), and 5S RNA was used as a positive control (Figure  S1 ). As shown, the sizes of the nine sRNAs that were tested roughly consistent with those of the RNA sequencing dataset. Two candidate sRNAs (MH_s10 and MH_s22) exhibited two bands, and MH_s25 showed a complex banding pattern. Six of the nine tested sRNAs (MH_3, MH_s10, MH_s15, MH_s22, MH_s25, and MH_s36) displayed strong signals in response to a variety of external stimuli.