dataesr/scientific-paragraphs-categorization huggingface dataset:
Extraction of Double-Stranded RNAs, High-Throughput Sequencing, and Sequence Bioinformatics Analysis Double-stranded RNAs (dsRNAs) were extracted from five grams of symptomatic leaves of the Cic accession according to Gentit et al. [6] , and thoroughly purified as described in Candresse et al. [7]. The purified dsRNAs were randomly reverse-transcribed and submitted to Illumina sequencing in a multiplexed format as described in Candresse et al. [7]. After demultiplexing and quality trimming steps, reads were assembled using CLC Genomics Workbench 9 (http://www. clcbio. com). The obtained contigs were annotated by Blastn and BlastX analyses against the Genbank database. Scaffolds were finally assembled by mapping the FMV contigs and residual reads on the FMV reference genome. The complete sequences of the six FMV-Cic genome segments were fully reconstructed by Sanger sequencing of overlapping PCR fragments obtained using a battery of sense and antisense primers designed on de novo assembled contigs (Supplementary Table  S1 ), together with those specific to amplifying the 5 and 3 termini of all FMV segments  [1, 4, 8].
Sequence Analyses All PCR products were ligated in Strata-Clone™ PCR cloning vector pSCA (Stratagene, La Jolla, CA, USA), cloned into Escherichia coli DH5α cells, and finally custom sequenced (Eurofins Genomics, Ebersberg bei München, Germany). Multiple alignments with the distance matrix for both nucleotide and amino and phylogenetic tree reconstruction were performed using CLC Genomics Workbench v. 3. 6. 5 on the obtained FMV sequences and on corresponding sequences available in the GenBank database (www. ncbi. nlm. nih. gov).
HTS Data Analyses and Identification of FMV High-throughput Illumina sequencing of randomly amplified cDNA prepared from purified dsRNAs extracted from the Cic cyclamen accession yielded a total of 875,401 high quality reads (average length of 160 nt). De novo assembly of these reads yielded 193 contigs of more than 400 nt, which were then annotated by BlastN and BlastX analysis against the Genbank database. Besides some contigs with relatively distant homologies to dsRNA viruses, only six contigs were identified as viral with high Blast e-values. These contigs corresponded to genomic RNAs 3 to 6 of FMV (two contigs each for segments 3 and 5). Further analysis, by mapping the Illumina reads against the FMV reference genome (NC029562-8), allowed to identify a total of 4302 reads (0. 49% of total reads) mapping to the genomic RNAs of FMV.
The sequences of the six RNA segments of the Cic cyclamen isolate of FMV were almost completely re-sequenced using Sanger sequencing of RT-PCR products obtained using a battery of sense and antisense specific primers (Supplementary Table  S1 ) designed on HTS-generated FMV contigs or from the FMV reference sequences. The obtained complete sequences have been deposited in Genbank under the accession numbers: LT978305-LT978307 and MG813799-MG813801. Sequence comparisons show that five RNA segments of FMV-Cic are similar in length to those of the reference FMV from fig (isolate Gr10), whereas RNA6 is longer by one nucleotide (1213 nt). All segments share high sequence identity with those of FMV isolates from Genbank, the most conserved being the RNA-3, with the RNA-3-encoded protein sharing 98-99% amino acids identity with its homolog of other isolates (Table  1 ).
RT-PCR Detection and Genetic Variability of FMV from Cyclamen Plants RT-PCR assays conducted on symptomatic and asymptomatic cyclamen samples collected from two nurseries in southern Italy showed that four additional accessions (G, GU, TD, and AD) were infected with FMV. All positive accessions showed symptoms like those observed on Cic accession. Furthermore, results of PCR tests showed that three of these four FMV-infected accessions, i. e. , GU, TD, and AD, were also infected by phytoplasma agents. Sequence analyses of these phytoplasma PCR amplicons showed that two phytoplasmas, belonging to two different groups, i. e. , Aster yellows (16SrDNA I) and stolbur (16SrDNA XII), were present in these samples. However, when their sequences were compared with the homologous sequences present in the Genbank database, they were found to contain mutations suggesting that the isolates involved belong to new subgroups of Aster yellows and Stolbur.
Phylogenetic Affinities of Cyclamen FMV Isolates The phylogenetic tree constructed with the partial RNA-3 nucleotide sequences of cyclamen isolates of FMV (TD, G, GU, AD, and Cic  Phylogenetic tree constructed with the partial nucleotide sequences of RNA-3 (304 nt) of FMV isolates obtained in this study (labeled with an asterisk) and those retrieved from Genbank (32 isolates). Accession numbers of sequences used: AD (LS997754), G (LS997755), GU (LS997756), TD (LS997757), Cic (LT978307), D10 (KC182499), DE6 (KC182497), IS28 (KC182508), IS30 (KC182510), WS12 (KC1824789), JTT-Ki (AB697847), IS1 (AB697843), BM53 (KC182487), CD87 (KC182492), Can01 (HQ703345), Arazi (KC182500), YEDI (KC182501), CM68 (KC182490), WS6 (KC182476), AKCAINCR (KC182504), JF1 (AB697844), WS20 (KC182484), By145 (LN908832), 35 (LN908836), Zd32 (LN908827), Gr10 (FM991954), F3 (LN908837), Bt152 (LN908833), Bd55 (LN908829), Zd5 (LN908826), Ba176 (LN908834), 36 (LN908838), Tg122 (LN908831), Bd62 (LN908830), St50 (LN908828), SB2-5 (AB697855), SB2-6 (AB697856), and SB2 (AB697852).
Pigeonpea sterility mosaic emaravirus 1 (HF568803) was used as out-species to root the tree. The tree was constructed from a multiple-sequence alignment generated with CLUSTAL W, using the "Neighbor Joining" (NJ) method in the software CLC Genomics Workbench. Numbers on branches indicate percentage of support out of 1000 bootstrap replications. Bootstrap values above 70% are shown. Scale bar represents 0. 1 nt substitutions per site related to branch length. Alg (Algeria), Can (Canada), Fra (France), Ita (Italy), Isr (Israel), Jap (Japan), Leb (Lebanon), Serb (Serbia), Tk (Turkey), Tu (Tunisia), and the USA (United States).
Conclusions The HTS technology has proven once more its superiority over other techniques in the identification of new plant pathogens in the absence of prior information on their nature or on their genome structure. Definitively, the outcome of this study, i. e. , the identification of FMV in a new host (Cyclamen persicum), would not have been possible using more traditional investigation approaches. In addition, it is worth noting that through this technique it was possible to establish somehow the FMV isolates obtained in this study (labeled with an asterisk) and those retrieved from Genbank (32 isolates). Accession numbers of sequences used: AD (LS997754), G (LS997755), GU (LS997756), TD (LS997757), Cic (LT978307), D10 (KC182499), DE6 (KC182497), IS28 (KC182508), IS30 (KC182510), WS12 (KC1824789), JTT-Ki (AB697847), IS1 (AB697843), BM53 (KC182487), CD87 (KC182492), Can01 (HQ703345), Arazi (KC182500), YEDI (KC182501), CM68 (KC182490), WS6 (KC182476), AKCAINCR (KC182504), JF1 (AB697844), WS20 (KC182484), By145 (LN908832), 35 (LN908836), Zd32 (LN908827), Gr10 (FM991954), F3 (LN908837), Bt152 (LN908833), Bd55 (LN908829), Zd5 (LN908826), Ba176 (LN908834), 36 (LN908838), Tg122 (LN908831), Bd62 (LN908830), St50 (LN908828), SB2-5 (AB697855), SB2-6 (AB697856), and SB2 (AB697852).
Figure2. Phylogenetic tree constructed with the partial nucleotide sequences of RNA-3 (304 nt) of FMV isolates obtained in this study (labeled with an asterisk) and those retrieved from Genbank (32 isolates). Accession numbers of sequences used: AD (LS997754), G (LS997755), GU (LS997756), TD (LS997757), Cic (LT978307), D10 (KC182499), DE6 (KC182497), IS28 (KC182508), IS30 (KC182510), WS12 (KC1824789), JTT-Ki (AB697847), IS1 (AB697843), BM53 (KC182487), CD87 (KC182492), Can01 (HQ703345), Arazi (KC182500), YEDI (KC182501), CM68 (KC182490), WS6 (KC182476), AKCAINCR (KC182504), JF1 (AB697844), WS20 (KC182484), By145 (LN908832), 35 (LN908836), Zd32 (LN908827), Gr10 (FM991954), F3 (LN908837), Bt152 (LN908833), Bd55 (LN908829), Zd5 (LN908826), Ba176 (LN908834), 36 (LN908838), Tg122 (LN908831), Bd62 (LN908830), St50 (LN908828), SB2-5 (AB697855), SB2-6 (AB697856), and SB2 (AB697852).
2f , g, Supplementary Fig. 1d-g ). IRE1 gene signature associates with basal-like breast cancer. Transcriptomic data obtained from MKC8866 versus vehicle-only microarray experiments identified 401 differentially expressed probe-sets representing 395 genes. These initial candidate markers were tested in a training dataset containing gene expression profiles of a panel of 27 breast cancer cell lines. Genes with a strong positive or negative correlation with IRE1 activity were further prioritized using BioInfoMiner to generate an 83gene signature predictive of IRE1 activity (IRE1 gene signature) (Supplementary Table  1 ). This IRE1 gene signature was applied to the 27 breast cancer cell lines, ranking them based on their predicted level of IRE1 RNase activity (Fig. 3a ). The resultant ranking largely mirrored results achieved by western blotting for XBP1s (Fig.
1b ) with those cell lines representative of TNBC being ranked highest (MDA-MB-231) while those lines representative of HER2-positive (SKBR3) and estrogen receptorpositive (T47D) ranked lower. We then applied the IRE1 gene signature to a cohort of 595 breast cancer tumors from The Cancer Genome Atlas (TCGA) database and, using the same approach, identified two distinct subsets of patients indicative of low (n = 79) and high IRE1 (n = 63) activity (Fig. 3b ). Analysis of the breast cancer subtype in each, based on PAM50 classification criteria  20  , revealed that a high IRE1 gene signature associated exclusively with basal-like breast cancers, while tumors associated with a low IRE1 gene signature were predominantly of the luminal subtype (Fig. 3c ). Gene set enrichment analysis (GSEA) indicated cancers with an elevated IRE1 gene signature associated with a more mesenchymal-like phenotype, increased invasiveness and a worse clinical outcome (Supplementary Fig.
IRE1 activity induces production of pro-inflammatory factors. To identify biological processes associated with constitutive IRE1 RNase activity in breast cancer patients, we again applied the IRE1 gene signature to the 595 breast cancer tumors from TCGA database. An IRE activity score for each tumor was predicted based on expression levels of the 83 genes from the IRE1 gene signature and the Pearson correlation coefficient was calculated between the score and every other gene across 595 tumors to produce a ranked gene list. We then performed GSEA using gene ontology terms on the ranked gene list. Using this approach, we found that predicted IRE1 activity strongly associates with the expression of genes involved in inflammatory responses (Supplementary Fig. 3 ). We also noted that genes encoding proinflammatory factors (IL6, IL8, and TGFB2) comprised a subset of the downregulated genes in the microarray experiment, suggesting a link between IRE1 RNase activity and the production of pro-inflammatory factors in TNBC cells.
When applied to TCGA breast cancer gene expression data sets, this gene signature identified two distinct cohorts reflective of IRE1 RNase high and IRE1 RNase low activities. Examination of these groupings revealed that breast cancers characterized by a high IRE1 gene signature associated with basallike breast cancers and exhibited increased expression of proinflammatory factors when compared to those with low IRE1 gene signature. These data, in conjunction with our in vitro cell line findings, strongly support the hypothesis that an elevated IRE1 RNase activity is associated with basal-like/TNBC. The development of genetic signatures such as this could enable the identification of breast cancer patients most likely to benefit from treatment with IRE1 RNase inhibitors and act as a companion diagnostic.
MDA-MB-231 cells obtained from ATCC and ECACC were used in this study. MDA-MB-231 cells were cultured in DMEM high glucose (Sigma-Aldrich, D6429) supplemented with 10% FBS, 50 U ml -1 penicillin, 50 μg ml -1 streptomycin, and 2 mM L-glutamine. The data from both cell clones were similar. Cells from ATCC were tested and were mycoplasma negative. HEK293T cells were from ATCC and were cultured in DMEM high glucose supplemented with 10% FBS, 2 mM L-glutamine, 50 U ml -1 penicillin, and 50 μg ml -1 streptomycin. All cells were cultured at 37 °C, 5% CO 2 in a humidified incubator and seeded at an appropriate number 24 h prior to treatment. Cells were treated with the indicated concentrations of MKC8866 (Fosun Orinove PharmaTech Inc. ), Tunicamycin (Tm) (Sigma-Aldrich, T7765), Thapsigargin (Tg) (Sigma-Aldrich, T9033), Paclitaxel (Sigma-Aldrich, T7402), Boc-D-fmk (Biovision, 1160-5), Etoposide (Sigma-Aldrich, E1383), or an equal volume of DMSO (Sigma-Aldrich, D2650).
0 in at the EMBL genetics core facility in Heidlberg, Germany. The generated data have been deposited in the Gene Expression Omnibus (GEO) and are publicly available under the GEO accession number GSE99766. The data were analyzed using GeneSpring GX software (Life Sciences Informatics, Agilent) whereby genes with P value below 0. 05 and absolute fold change greater than 1. 3 were considered differentially expressed between the two conditions and selected as potential IRE1 markers for further analysis. Initial candidate markers were subsequently curated in a training data set by utilizing them to hierarchically cluster, in a semi-supervised manner, a panel of 27 breast cancer line gene expression data sets (GEO accession GSE50832)  41. Hierarchical clustering utilizing the 401 candidate markers revealed two robust groups of genes that either negatively or positively correlated with expected IRE1 activity.
Genes strongly associated with either group were further prioritized using BioInfoMiner tool to generate an 83-gene signature predictive of IRE1 activity (Supplementary Table. 1). This IRE1 83-gene signature was then utilized for unsupervised clustering of a cohort of 595 breast cancer tumors from the publicly available The Cancer Genome Atlas (TCGA) database  42. Breast cancer tumor groups with gene expression profiles corresponding with high (n = 63) and low (n = 79) IRE1 activity (as predicted by the IRE1 gene signature) from both ends of the spectrum were taken for further analysis and comparison. P values for box plots were calculated using a two-tailed t test with Welch's correction and performed with GraphPad Prism software (GraphPad Software, San Diego, CA, USA). GSEA between the two groups was performed with GSEA software (Broad Institute), using transcriptome profiling data from the high IRE1 and low IRE1 tumor sets (TCGA) and pre-defined gene signatures from the Molecular Signature Database (MSigDB).
After recovery cell viability was determined using trypan blue staining. Viable cells from each treatment were seeded in triplicate at 1 × 10 3 cells per well in 96 well ultra-low attachment surface plates (Corning, 10554961) in DMEM/F12 medium supplemented with B27-supplement (ThermoFisher, 12587010) and 20 ng ml -1 epidermal growth factor. Completely untreated cells were also seeded as a control. Mammospheres measuring >40 μm were quantified in five fields per well, and mammosphere formation efficiency (%) was determined using the following formula: (number of mammospheres > 40 μm/ number of cells seeded) × 100. Immunohistochemistry. The study was conducted with human samples and clinical data of Rennes Biobank Breast Cancer Collection (BRIF number: BB-0033-00056) certified NF S96900 for receipt preparation preservation and provision of biological resources.
Mice were observed frequently for health and overt signs of any adverse treatment-related side effects, and noteworthy clinical observations were recorded. Individual body weight loss was monitored per protocol, and any animal whose weight exceeded the limits for acceptable body weight loss was euthanized. Acceptable toxicity was defined as a group mean body weight loss of <20% during the study and not more than one treatment related death among ten treated animals, or 10%. Any dosing regimen resulting in greater toxicity was considered above the maximum tolerated dose. Statistical analysis. Assumptions concerning the data (normal distribution and similar variation between experimental groups) were examined for appropriateness before statistical tests were conducted. Statistical analysis was carried out using pairwise single factor ANOVA, unpaired two-tailed Student's t test, or two-tailed t test with Welch's correction as indicated.
Values with P < 0. 05 are considered statistically significant. Data availability. Microarray data supporting the findings of this study have been deposited in the Gene Expression Omnibus and are publicly available under the GEO accession number GSE99766. of samples used in Fig. 1c. A. O. helped with analysis of data associated with Fig. 3b. P. L. and F. G. provided access to patient material used in Supplementary Fig. 5. E. C. M. , B. L. , and J. O. designed and performed experiments. J. B. P. and Q. Z. provided access to MKC8866 and critical feedback on the study. R. J. contributed to initiation of the study and helped secure funding. A. M. G. provided critical feedback on the study and contributed to preparation of manuscript and revisions. S. E. L. wrote the manuscript and contributed to study direction.
Acknowledgements The authors wish to acknowledge the expertise provided by Charles River Study Director,  Kay Mesham , and the  NCBES Flow Cytometry, Genomic  and Screening core facilities at the  National University of Ireland Galway  which are funded by  NUI Galway  and the  Irish Government 's  Programme for Research in Third Level Institutions , Cycles 4 & 5, National Development Plan 2007-2013. We thank  Dr. Sandra Healy  for critical review of the manuscript and  Gwénaële Jégou  for her assistance with analysis of in vivo samples. We are grateful to  H2P2 facility of Biosit (Rennes, France ) for its technical assistance with immunohistochemistry studies. Microarray analysis was carried out in collaboration with Genomics Core Facility EMBL. The work in our groups are funded by  Breast Cancer Campaign  grant (2010NovPR13, 2015MaySP550),  Health Research Board  (grant number  HRA-POR-2014-643 ),  Belgium Grant  ( IAP 7/32 ),  Science Foundation Ireland (SFI)  grant co-funded under the  European Regional Development Fund  (grant number  13/RC/ 2073 ),  SFI   Starting Investigator Grant  (grant number  15/SIRG/3528 ),  EU   H2020 MSCA ITN-675448  (TRAINERS),  EU   H2020 MSCA RISE-734749  (INSPIRED), and  Institut National du Cancer (INCa ;  PLBIO: 2017-148 ).
RRM2mut and LCDmut show opposing effects transcriptomewide and counteracting biological effects We next sought to directly compare our two mutants to a bona fide TDP-43 LOF. We could not use TDP-43 null mice as they die before E6. 5, so we performed RNA-seq on MEFs homozygous for the RRM2mut and the LCDmut alleles comparing them each to their wild-type controls, in parallel with wild-type MEFs in which we induced TDP-43 knockdown with shRNA (Tardbp-shRNA) comparing them to scramble sequence shRNA  (Appendix Fig S5). Two-way comparisons showed that RRM2mut aligns with Tardbp-shRNA, with all significantly differentially expressed exons in both datasets up-or downregulated in agreement. Conversely and strikingly, LCDmut showed all of the significant differentially expressed exons changing in the opposing direction to RRM2mut and to Tardbp-shRNA (Fig 2A, Appendix Table  S2 ).
Motif enrichment analysis within 100 bp of each cassette exon:intron boundary identified the TDP-43 binding motif, UGUGUG, only for exon exclusion (P = 5. Fig EV1C). Therefore, exon skipping is associated with TDP-43 direct RNA binding in LCDmut (Fig  EV1G  and H ). This is in stark contrast to what we found in RNA-seq from our RRM2mut LOF line, where, consistent with previously published TDP-43 LOF datasets, the exon inclusion events have a stronger association with the UGUGUG TDP-43 binding motif (Fig EV1D  -F ). We further analysed the skewed preference in LCDmut for exon skipping and identified a novel previously unreported effect: the skipping of otherwise constitutively expressed exons. We termed these novel events skiptic exons (SEs) and to define them we used the following per cent spliced in (PSI) criteria-exons that were included in WT (PSI > 0.
SEs are highly conserved and lead to impaired transcript levels To further characterise the SEs, we investigated their conservation, and interestingly, in contrast to CEs, conservation of SEs is high between species  (Fig 5C). We also found that the novel skipping events produced by the LCDmut lead to frameshift or premature termination codons (PTC) in 15/48 transcripts, resulting in likely unstable transcripts. Therefore, we compared expression of transcripts containing SEs to transcripts carrying other differentially regulated cassette exons, and all other transcripts expressed at similar levels in our RNA-seq dataset; we found the transcripts that included SEs were significantly more downregulated  (Fig 5D). Furthermore, the proportion of predicted unstable transcripts significantly increases (P < 0.
RNA-sequencing RNA-seq was performed using polyA libraries as previously described  (Wang et al, 2012; Joyce et al, 2016)  and all RNA-sequencing generated for this study, along with read-length and average uniquely mapped reads are summarised in the table below. All mutants were compared to wild-type littermates. N refers to the number per genotype (control, heterozygous and homozygous). All sequencing data are deposited in the NCBI Sequence Read Archive (reference: SRP133158). iCLIP iCLIP was performed as detailed by Ko ¨nig et al (2010). Dissected forebrain (RRM2mut) or adult brain (LCDmut) were homogenised in phosphate-buffered saline (PBS) and UV-crosslinked 4× at 100 mJ/cm 2 at a wavelength of 254 nm (RRM2mut n = 2, LCDmut n = 1-2). Protein concentrations were normalised using the DC Protein Assay (Bio-Rad), and the lysate was then sonicated for 30 s ×10 times.
Physiology analysis Muscle force and motor unit number female WT and LCDmut littermates (N = 5) were examined at 24 months of age, as described previously  (Joyce et al, 2016). In summary, in anesthetised mice (4. 5% chlorohydrate, 1 ml/100 g of body weight), the distal tendons of the tibialis anterior (TA) and extensor digitorum longus (EDL) muscles in both hindlimbs attached to isometric force transducers (Dynamometer UFI Devices, Welwyn Garden City, UK), and the sciatic nerve exposed and sectioned. Squarewave pulses of 0. 02 ms duration at supra-maximal intensity were used to stimulate the nerve and elicit TA or EDL isometric contractions. Trains of stimuli at frequencies of 40, 80 and 100 Hz were used and were measured using Scope software. Motor unit numbers innervating the EDL was determined by using stimuli of increasing intensity, resulting in incremental increases in twitch tension due to successive recruitment of motor axons.
0001; Bonferroni multiple comparison tests are plotted as: **P < 0. 01; ***P < 0. 001; mean and SEM plotted; N = 3 per genotype. D EMSA assay performed with increasing quantities of recombinant WT and RRM2mut TDP-43 with radiolabelled (UG) 6 RNA and TDPBR RNA shows reduced RNA binding of recombinant RRM2mut. E Scatter plot of pentamer enrichment in TDP-43 iCLIP shows a canonical TDP-43 pattern of enrichment in TG/GT-containing pentamers in RRM2mut (left) and LCDmut (right) along with littermate WT controls. F Mapping of TDP-43 binding sites in RRM2mut (left) and LCDmut (right) along with littermate WT controls iCLIP datasets shows a similar distribution genome-wide. Source data are available online for this figure.