dataesr/scientific-paragraphs-categorization huggingface dataset:
Results Antisense transcription is abundant in E. histolytica. To characterize the transcriptome of E. histolytica, we sequenced poly(A) + RNA of strain HM1:IMSS (n = 1 × 4, dataset 1A) (Table  S1 ) and performed de novo assembly of transcriptome. We obtained 27,139 contigs (i. e. transcribed fragments) with a mean length of 327 nt (from 100 to 3881nt). About 63% of these transcribed fragments (n = 16,993, average 382nt) mapped to the same strand in 81% of genes (n = 6667), attesting a good coverage of the transcriptome. Another 14% of transcribed fragments (n = 3658, averaged 222nt) mapped to the opposite strand in 28% of the genes (n = 2335). We thus conclude at least a quarter of genes showed evidence of NAT transcription (Table  S2 ). The validation of the detected NATs was performed through a northern blot for transcribed fragments that mapped to the opposite strand of one gene (EHI_036570).
Transcription initiation of NATs at stop codon. To get an insight into the initiation of NAT transcription, we mapped the TSS genome-wide using two libraries, one including a 5′-monophosphate dependent terminator exonuclease (TEX) (n = 1, dataset 2A in Table  S1  and a capped small RNA (csRNA) method (n = 1, dataset 2B in Table  S1 ). Briefly, the TEX method  27  relies on the fact that 5′ ends of primary transcripts which lack a 5′-monophosphate are protected from TEX degradation, while the csRNA method  28  is based on the fact that csRNAs (30 to 50nt) are produced at TSS through promoter-proximal pausing of Pol II  29, 30. While the mapping of a TSS of a transcript depends on its expression level in the TEX method, the level of Pol II pausing at the TSS (i. e. number of csRNA) is not strictly correlated to its expression level  30.
Therefore, these two methods are complementary. To control the level of background noise (i. e. false positive TSS derived from incomplete TEX digestion), we sequenced a control library with the same input RNA as in the TEX library but without TEX and TAP digestion, which should be unable to capture the TSS (n = 1, dataset 2C in Table  S1 ). screenshot for gene EHI_15260 take as an example, representing RNA-seq coverage and assembly contigs for both sense (blue) and antisense (red) transcription. We then pooled the background subtracted signal (i. e. candidate TSS) from both TEX and csRNA dataset and clustered these candidate TSS (see "Methods"), yielding 95,326 TSS (i. e. TSS clusters, Table  S4 ). These TSS were strongly enriched around the annotated start codons, peaking at 10nt upstream (Fig.
Expression of NATs is regulated upon stress and between trophozoites of different virulence. We assessed whether NAT expression was regulated by analyzing its transcription in different contexts. We profiled, using RNA-Seq, the transcriptomes of (1) trophozoites under stress (i. e. heat shock and recovery) and (2) trophozoites exhibiting different virulence. Here we first focus on their transcriptomes upon heat shock and recovery. Briefly, cells were passed from 37 to 42 °C, followed by a recovery stage at 37 °C. Samples were collected at 0 h (37 °C), 2 h (42 °C), 4 h (42 °C) and 8 h (37 °C) (dataset 3 in Table  S1 , "Methods"). We counted the number of genes with clear NAT transcription at each timepoint, by the presence of antisense transcribed fragments from de novo transcriptome assembly (Table  S7 , "Methods").
To further extend the above observations, we performed similar analyses on the transcriptomes of trophozoites of different virulence and in different infection conditions, as described previously  12  (dataset 1A-D in Table  S1 , "Methods"). Briefly, we profiled samples: (1) the "normal" trophozoites maintained in short-time culture (NorCultr), (2) virulent trophozoites freshly extracted from hamster with liver abscess (VirCultr), (3) virulent trophozoites cultured in human colon explants (VirColon), (4) attenuated trophozoites with long-time culture (AttCultr). First, we observed slight changes of NAT transcription across the 4 infection conditions (in terms of presence of antisense transcribed fragments as described) (Fig. 6A , Table  S10 ). We next identified the genes differentially expressed in VirCultr, VirColon and AttCultr, compared to NorCultr (Fig.
Sequencing was performed on a HiSeq 2000 (Illumina) to obtain 58 base single-end reads. All reads from different experiments were cleaned from adapter sequences with AlienTrimmer 56 (version 0. 4. 0) and low quality (< 20) or short sequences (< 20 nt) were removed from datasets. Genome reference and data repositories. The reference genome was downloaded from AmoebaDB v34  57  (https ://amoeb adb. org/commo n/downl oads/relea se-44/Ehist olyti caHM1 IMSS/). The total genome size is 20,80 Mbases, with a GC content of 24. 2%, assembled in 1496 contigs, and 8201 annotated coding sequences. The data from experiments of TSS identification and heat-treated amoebae (dataset 2 and 3 in this paper) are available in the SRA database (https ://www. ncbi. nlm. nih. gov/sra/) under the accession number PRJNA615171.
The data concerning virulence conditions (dataset 1) derived from (Weber et al. )  12  , these data were already in the ArrayExpress database (www. ebi. ac. uk/array expre ss) under accession number E-MTAB-4882. Transcript contig construction. Illumina libraries were merged within each dataset (Table  S1 ) for NAT fragment analysis in order to reach bigger set of reads. Sequences were mapped to the reference genome of E. histolytica, using STAR v2. 5. 0 58 with a maximum intron length of 900 (-alignIntronMax), 2 mismatches maximum (-outFilterMismatchNmax), 10 locations maximum for a read mapping (-outFilterMultimapNmax), minimum overhang (-alignSJoverhangMin) of 25 for spliced alignments. Mapped reads were then split into direct and reverse strand sets with SAMTools  59. Trinity assemblies  60  were performed on each set (direct and reverse) to construct transcript fragments, with a kmer size of 15nt (-KMER_SIZE 15), a maximum intron size of 900nt (-genome_guided_max_intron 900), a minimum coverage of 20 (-genome_guided_min_coverage 20) and a minimum length of 100nt for each assembled contigs (-min_contig_length 100).
Resulting transcript contigs were mapped on the reference genome using Gmap  61  with a maximum intron length of 900nt (-K 900). Lastly, NAT fragment counting for each gene was produced by featureCounts  62  on the opposite strand (-s 2), with a minimum fraction of 10% of the contig length overlapping the gene, required for its assignment (-fracOverlap 0. 1). Genes were counted as "NAT gene", when at least 1 NAT contig was identified with featureCounts. In order to specify parts of genes preferentially covered by NAT contigs, we split the genes in 5 equal regions and counting was performed on each region as well, allowing contig assignment to all of their overlapping parts (-O -f). The reads were aligned to the reference genome of E. histolytica, using Bowtie version 0. 12. 7  63  with the following parameters: maximum 2 mismatches were allowed (-n 2) and reads mapped to multiple locations (-m 50) were reported only once (-k 1).
Differential expression analysis. Reads sequences of each replicate were mapped to the reference genome of E. histolytica, using STAR v2. 5. 0  58  ) with a maximum intron length of 900 (-alignIntronMax), 2 mismatches maximum (-outFilterMismatchNmax), 10 locations maximum for a read mapping (-outFilterMultimapNmax), minimum overhang of 25 for spliced alignments. Reads counting for each gene was produced by featureCounts  62  separately on the direct (-s 1) and opposite strand (-s 2), allowing multi-mapping reads (-M). Normalization was first calculated on the sense counting, and size-factors were then applied to both sense and antisense counting. Transcript differential expressions were calculated on the merged normalized counting (sense and antisense) using DESeq2 v1. 24. 0 67 within the SARTools pipeline v1.
Acknowledgements The authors gratefully acknowledge the help of  Hugo Varet  ( Hub de Bioinformatique et Biostatistique -DBC, Institut Pasteur ) for his advice on differential expression experiments, to  Pascal Campagne  ( Hub de Bioinformatique et Biostatistique -DBC, Institut Pasteur ) for critical reading of the manuscript and to  Susanne Warrenfeltz (AmoebaDB)  for helping us in the search for gene pairs.
Data availability The data from experiments of TSS identification and heat-treated amoebae (dataset 2 and 3 in this paper) are available in the SRA database (https ://www. ncbi. nlm. nih. gov/sra/) under the accession number PRJNA615171. The data concerning virulence conditions (dataset 1) were already in the ArrayExpress database (www. ebi. ac. uk/ array expre ss) under accession number E-MTAB-4882.
Sequencing viral transcripts from HIV-1 producing cells To investigate the viral splicing pattern in HIV-1 producing cells, primary CD4+ T cells from three different donors were isolated and infected for 24 h with HIV NL4-3 viruses pseudotyped with the vesicular stomatitis virus glycoprotein (VSV-G). Total cellular RNA was extracted and was used to generate cDNA libraries for ONT sequencing. Between 575,784 and 741,005 reads were mapped to both the human and HIV NL4-3 genomes using Minimap2 software (Additional file 1: Table  S1 ). This alignment program works with both relatively short (> 100 nucleotides) and long reads, is compatible with high error rates generated by ONT sequencing and is faster than short-read aligners. By tolerating long insertions and deletions and allowing split-read alignments, it also proved to be adapted to splice alignment  [50].
4  Characterization of HIV-1 RNA populations in HeLa cells expressing either wild-type or U1 D2upEx snRNA by ONT sequencing. HeLa cells were transfected with either the wild-type U1 snRNA or the modified U1 D2upEx snRNA enhancing SS D2 usage. 48 h later, cells were harvested, RNA was extracted and cDNA libraries were prepared. ONT sequencing and analyses were performed as described in Fig. 1. a Usage of SD and SA sites are expressed as a percentage of the occurrence of all SS within each condition. Data are presented as mean (n = 3). P values were calculated using an unpaired t-test (*p < 0. 05, **p < 0. 01, ***p < 0. 001 and ****p < 0. 0001). b Isoform expression levels were calculated for both wild-type and U1 D2upEx snRNA conditions. Heatmap represents the fold enrichment of isoforms expressed in U1 D2upEx snRNA expressing cells over wild-type U1 snRNA condition according to the coloured scale.
) Fig. 7  HIV-1 alternative splicing program in primary T cells at the early times of infection. Usage of SD (a) and SA (b) sites during a time course of infection of CD4+ T cells from donor 4 was based on the number of reads harbouring these particular SS involved in a splice junction and normalized with DESeq2 included in the Eoulsan's pipeline. Usage of SD (c) and SA (d) sites was then expressed as % of the total number of viral annotated reads at each time point. Splice tree representations of HIV-1 alternative splicing regulation at 12 (e) and 24 (f) hpi were drawn on Cytoscape as in Fig. 5  based on ONT quantification of SS usage and spliced isoform levels at each time point. Only transcripts that were found at least 5 times in a sample and produced by usage of major SS were considered.
10. 23 and 1. 11. 5). Base-calling from read event data was performed by Albacore (v 2. 1. 10 and 2. 2. 7). To analyze viral RNA produced during a time course of infection, the protocol was upgraded as followed: 10 ng of total RNA were amplified and converted to cDNA using SMART-Seq v4 Ultra Low Input RNA kit (Clontech). Afterwards an average of 14 fmol of amplified cDNA was used to prepare library following SQK-PBK004 kit (PCR Barcoding kit; ONT). After the PCR adaptater ligation, a 0. 6X Agencourt Ampure XP beads clean-up was optimised and 2 fmol of the purified product was taken into PCR for amplification and barcodes addition with a 17 min elongation at each 18 cycles. Samples were multiplexed in equimolar quantities to obtain 100 fmol of cDNA and the rapid adapter ligation step was performed.
All the prepared library was used for loading on an R9. 4/ R9. 4. 1 flowcell according to the manufacturer's protocol. Sequencing was performed with the standard 48 h sequencing protocol run on the MinION MkIB, using the MinKNOW software (v 3. 3. 2) and 7. 5 million reads were obtained for the run. A mean of 1. 1 ± 0. 5 million passing ONT quality filter reads was obtained for each of the 5 samples. Base-calling from read event data was performed by Guppy (v 3. 1. 5).
Relative level of 9-kb transcripts was estimated at both D1 and D4 SS by dividing the number of reads, including partial sequencing reads encompassing D1 or D4 without splicing, by the total number of reads spanning this SS (Additional file 8: Figure  S3 ). For kinetics of donor 4, the read count of all samples were normalized using DESeq 2 (v 1. 8. 1)  [83]. Splice trees were built by integrating quantification data of spliced transcripts normalized to the total level of spliced transcripts into Cytoscape (v 3. 6. 1) [84]. For clarity only transcripts ≥ 5 copies and using the main splice sites were represented. Usage of D1 was set at 100%.
Acknowledgements We are grateful to all members of the  Berlioz and Emiliani lab  for helpful discussions. We thank  Pierre de la Grange ,  Noémie Robil et Agathe Delaune from GenoSplice technology  for their expertise in genomic analyses of transcriptomes,  Gordon Langsley  (Comparative cell biology of apicomplexan parasites,  Institut Cochin, Paris ) for his valuable input during the preparation of this manuscript and  Camille Marchand  for his assistance in Figures preparation. We greatly acknowledge  Franck Letourneur  from the  Genom'IC core facility  and  Karine Bailly  and  Muriel Andrieu  from the  Cytometry and Immunobiology core facility at Institut Cochin. The proviral plasmid pNL4-3 from  Dr. Malcolm Martin  was obtained from the  NIH AIDS Reagent Program, Division of AIDS, NIAID.
Funding This work was supported by  Agence Nationale de Recherche sur le Sida et les Hépatites (ANRS)  [to SGM and SE]; The  Ecole normale supérieure genomic core facility  was supported by the  France Génomique national infrastructure , funded as part of the  "Investissements d' Avenir" program  managed by the  Agence Nationale de la Recherche  ( ANR-10-IBNS-09 ). NNQ was a post-doctoral fellowship recipient from  ANRS. SG held a fellowship from the  "Ministère français de l'enseignement supérieur et de la Recherche ". MV was a recipient of an Engineer fellowship from  Sidaction. Funding for open access charges:  Institut National de la Santé et de la Recherche Médicale.
Microbiome In a murine model of FCCMs, it has been reported that the microbiome composition influences the progression of brain angiomas  [38]. Recent data in 75 patients go along the same line  [39]  and therefore it is of interest to characterize patients included in Treat_CCM at baseline as well as during disease evolution. Stools will be sampled for microbiome analysis at randomization, at 1 month, and at 1 and 2 years of follow-up.
Quality control and trial monitoring The safety profile of propranolol is reassuring and is documented by millions of patients of all ages, including infants treated mostly, but not only, for cardiovascular disorders over the last 40 years. Despite the expected very low probability of adverse effects related to propranolol, patients shall be controlled in particular for heart rate, blood pressure, and other adverse reactions possibly attributable to propranolol. Quality control activities will be applied by the Study Secretariat at IRCCS-Mario Negri (IRFMN) to each stage of data handling to ensure data are recorded and reported in compliance with the protocol. All of the electronic case report forms (e-CRFs) will be reviewed for completeness and accuracy at the Study Secretariat by trained staff; errors and omissions will be entered on data query forms returned to the investigator for resolution.
The Data Safety and Monitoring Board (DSMB) will monitor the safety data in the project in an ongoing basis. Serious and non-serious adverse events (SAEs and AEs) that coincide with primary and secondary endpoints will be collected on the e-CRFs and evaluated by the DSMB. Investigators shall report to the Study Secretariat (IRFMN) all SAEs suspected to be related to the study medications or any serious adverse drug reaction (SADR) within 24 h of learning of its occurrence. On-site monitoring visits will be performed at site opening and at regular intervals throughout the study. On average, four on-site monitoring visits are foreseen for each participating site. During the visit, a certified monitor will verify adherence to the protocol: completeness, accuracy, and consistency of the data; and adherence to Good Clinical Practice (GCP) and local regulations on the conduct of clinical research.
The investigator should guarantee access to source documents for the monitor. The clinical monitor will check participant medical records and other trial-related records (source documents) to verify the data reported to the sponsor on the e-CRFs and in all required reports. The clinical monitor will communicate deviations from the protocol to the investigators and will ensure that appropriate action designed to prevent recurrence of the detected deviations is taken and documented. Deviations from study protocol will be entered into the Protocol Deviation Log and communicated to the sponsor. All of the monitoring activities will be documented according to GCP rules and reported to the sponsor.
This Committee will report to the Steering Committee. Records of all adjudication decisions and of Clinical Event Committee minutes will be maintained. Data handling: at the Study Secretariat, identification of treatment allocation on the e-CRF will only occur where necessary in order to minimize access to treatment information during data handling. Steering Committee: the Steering Committee will include one representative for each participating clinical centre, the Principal Investigator (ED, IFOM, sponsor of the trial), one representative of the Patient's Association, and a representative responsible for the Study Secretariat and has the full responsibility for the planning, conduction, analysis, publication of the study protocol, and results. Clinical Event Committee (CEC): CEC members will be independent and will not have direct contact with patients randomized into this trial.
The main roles and responsibilities of the CEC are: to agree on definitions of the clinical endpoints and on standard procedures for assessing these endpoints, and to validate blindly the events recorded and reported by the Investigators as endpoints of the study. A Clinical Event Committee manual will be prepared and approved by the members of the CEC. Data and Safety Monitoring Board (DSMB): the roles and responsibilities of the Data and Safety Monitoring Board (DSMB) will be defined by the same DSMB with special focus on intensive monitoring of the safety aspects in the whole study population. Safety reports will be made available to the DSMB by the study statistician every 6 months. No specific interim analysis is foreseen for efficacy, unless required by the Steering Committee or DSMB because of safety concern.
Centralized laboratories MRI Core Laboratory (Policlinico MI): examinations will be sent as digital recordings to the MRI Core Laboratory, where a central reading will be performed. A users' manual for MRI will be made available to the participating centres to ensure common standard methods of image acquisition. Biobank (IRFMN): the coordination of the collection, long-term storage of biological samples in a centralized UNI EN ISO 9001: 2015-certified (for the following: collection, storage, and distribution of biological samples and related data for scientific research) biobank, and distribution of de-identified biological samples. In particular, blood samples will be collected from patients at the time of MRI (randomization, 12 months, and 24 months) and at the 4-week visit. Circulating biomarker assays laboratory (IFOM): serum/plasma samples from patients before the beginning of the treatment will be used to characterize the circulating miRNAs and identify a CCM patient-specific miRNA signature in comparison to age-matched and sex-matched healthy individuals.
Statistical analyses The main analysis will be performed according to an intention-to-treat (ITT) approach. Therefore, all patients randomized in the study will be included in the analysis. As we cannot conduct inferential statistics on the data collected in the pilot study, we will only compare the proportions of adverse events between the groups to assess the safety of propranolol  [42]. A per-protocol analysis is foreseen. Baseline characteristics will be presented by treatment groups, and descriptive statistics will be performed to assess the success of randomization. Kaplan-Meier curves will be constructed to illustrate the occurrence of adverse events in the study over time. Concerning the analysis of this pilot study, we are only interested in whether the treatment estimate is larger or smaller than zero.
Discussion To the best of our knowledge, from a search of Clinical-Trials. gov on 29 April 2019, at present only one interventional trial with long-term drug treatment of CCMs is registered, testing atorvastatin in 80 patients with CCMs (AT CASH EPOC; NCT02603328). This single-centre trial is double-blinded vs placebo, with 1:1 randomization  [43]. Considering the lack of effective pharmacological treatments of CCMs, and the safety and wide availability of propranolol, there is a risk that this drug will be prescribed off-label also in the absence of clear evidence of benefit. Therefore, it is imperative to maximize inclusion and perform randomized trials as soon as possible. Some limitations are to be acknowledged. Firstly, the non-blind design may cause biases in the reporting of subjective variables such as quality of life; however, the PROBE design should protect from major influences on endpoint variables.
Trial status The first patient was included in Treat_CCM in March 2018, and, as of the end of April 2019, 57 patients have been included. Recruitment is ongoing and, according to the study protocol, each patient will be followed up for at least 2 years. The current protocol is version 2. 5 created on 30 November 2018. Any protocol modifications will be communicated to relevant parties (e. g. trial participants) and published on relevant channels (e. g. Clinicaltrials. gov). The results of this trial will be published in an appropriate scientific journal.
Acknowledgements The authors are grateful to the members of the committees and the patients who participated in this study. TREAT_CCM collaborators:  Fondazione Policlinico Univ. A. Gemelli ,  Rome: R. Pallini ,  G. d'Alessandris ,  F. Pignotti ,  C. Sturiale ,  A. Albanese ;  Fondazione IRCCS Ca'Grande Osp Maggiore Policlinico , Milan:  S. Lanfranconi ,  E. Scola ,  G. A. Bertani ,  B. Zarino ,  G. Valcamonica ,  D. Ronchi ;  Fondazione IRCCS Istituto Neurologico Carlo Besta ,  Milan: M. R. Carriero ,  E. Scelzo ,  G. Faragò ,  S. Pogliani ,  U. de Grazia ,  C. Bossi ;  IRCCS Centro Neurolesi "Bonino Pulejo ", Messina:  E. Mazzon ,  S. Marino ,  R. Ciurleo ;  Ospedale Casa Sollievo della Sofferenza ,  San Giovanni Rotondo: M. Castori ,  C. Fusco ,  A. Petracca ,  L. D'Agruma ,  P. Raggi ,  A.