dataesr/scientific-paragraphs-categorization huggingface dataset:
Differential expression analysis was performed for selected groups using edgeR  [62, 63]. Furthermore, to analyze the expression of Ae. aegypti ABC transporters during different arboviral infections like WNV, YFV or DENV, we constructed a single nonredundant gene list of these transporters based on prior AaegL1. 2 and AaegL3. 3 and current AaegL5. 1 annotations. We analyzed the corresponding gene expression from the publicly available microarray datasets  [7, [46] [47] [48] , wherein the reported probe sets were used to identify genes in new genome assembly through BLASTn. Genes identified with a significant p-value <0. 05 were considered for expression analysis. The log 2 fold change in the expression was used to construct a heatmap using Morpheus software (https://software. broadinstitute.
Fold changes were calculated using the ∆∆ Ct method as described before  [65]. All the data were expressed as mean + standard deviation (SD). Statistical significance was analyzed by one-way ANOVA with Tukey's multiple comparison test for the differential gene expression using GraphPad Prism 8. 0 (GraphPad Software, La Jolla, CA, USA). The data with a p-value < 0. 05 were considered significant.
Identification of Promoter and Transcription Factor Binding Sites in ABC Transporter Genes To identify promoters, the 5 upstream sequence of each Ae. aegypti ABC transporter gene was analyzed using the online NPP2. 2 tool (http://www. fruitfly. org/seq_tools/ promoter. html, accessed on 29 March 2021), (organism selected = Eukaryotes and Minimum promoter score = 0. 8)  [66]. The upstream 5 UTR sequence to promoter of selected ABC transporter genes was analyzed with online JASPAR software (http://jaspar. genereg. net/ search?q=&collection=CORE&tax_group=insects, accessed on 31 March 2021) to predict the transcription factor binding (TFB) sites. Here the insect core database was selected, using Drosophila as a reference organism and a similarity threshold of 80%  [67].
Acknowledgments: V. K. , S. G. , L. G. , K. G. , S. K. and V. S. wish to acknowledge  BITS Pilani  for the infrastructural support. L. G. and S. K. also wish to acknowledge  CBLU, Bhiwani, Haryana. All authors acknowledge the helping staff from  BITS, Pilani  and  MIVEGEC ,  Univ. Montpellier, IRD, CNRS, France.
Amplifications and 16S rRNA sequence analysis Primers 16S1 and rp2 were used for amplification of approximately 1500 nucleotides of the 16S rRNA gene as previously described  [41]. Sequences were combined in an alignment with previously published sequences relevant for phylogenetic and epidemiological studies. The first 16S rRNA sequence obtained from sample 11-261_B096 was searched (BLASTN) against the NCBI non-redundant nucleotide database for identification. Other sequences were analyzed using the Bionumerics software package version 7. 6. 2 (Applied-Maths, Belgium). A similarity matrix was calculated using a pairwise alignment with a Jukes and Cantor correction. A phylogenetic tree was constructed using RAxML v8  [45] , with the GTRGAMMA model and 1000 bootstrap replicates based on an alignment of all 16S rRNA sequences using MAFFT v7  [19].
The 16S rRNA nucleotide sequences of samples 11-261_B96, 13-234 I89, 13-234 I94, 13-234 I99, 13-3212_BE60, 14-2711_R45, 14-2711_R47_isolate, 14-2711_R79, 15-2711_O50_isolate, 15-2711_O77, 15-2711_O99_isolate have been deposited under accession numbers LR777664 to LR777674 at the ENA database.
4. 8 with default parameters  [52]. Resulting contigs were subsequently blasted on the nucleotide database of NCBI to remove residual mammalian sequences from cell culture constituents. Assembled genomes were annotated using Prokka v1. 14. 5 [43]. Short reads from Illumina were mapped back to the assembly using BWA v0. 7. 12-5 [25] and long reads from Nanopore were mapped with minimap2 v2. 17-r941  [24]. Statistics of the assemblies were generated using Qualimap v2. 2. 2-dev  [10]. Genome sequences of all three isolates have been deposited in the ENA database under the following accession numbers: ERZ1300893 (14-2711_R47), ERZ1300894 (15-2067_O50), and ERZ1300895 (15-2067_O99). Illumina raw reads are available at ENA under accession numbers ERR3950885 (14-2711_R47), ERR3950886 (15-2067_O50), and ERR3950887 (15-2067_O99) and ONT raw reads under accession numbers ERR3950922 (14-2711_R47), ERR3950921 (15-2067_O50), and ERR3950920 (15-2067_O99).
For comparative genome analysis, the novel Roary ILP Bacterial Annotation Pipeline (RIBAP) was used (github. com/hoelzer-lab/ri bap). Based on annotations provided by Prokka, the pipeline combines various core gene sets calculated by  Roary [34]  with a newly adapted Integer Linear Programming (ILP) algorithm  [28]. The combination of Roary and ILP results led to the creation of the final interactive RIBAP table, which allows systematic searches for common and specific features in the genomes, as well as downloads of multiple-sequence alignments and phylogenetic trees. RIBAP is implemented using the workflow management system Nextflow [6] and all used tools are encapsulated in Docker images  [30]  to allow easy installation and execution of the pipeline (see GitHub for further instructions).
UpSet diagrams The UpSetR package is a scalable tool for visualizing intersecting sets and their properties  [4]. As an input for UpSetR, we selected the homologous gene groups identified with RIBAP for the 15 input genomes. The UpSet visualization of all 15 strains was restricted to the largest 40 intersecting sets. Real-time PCR for detection of the new genus and the two new species A new real-time PCR was designed to specifically detect the members of the new genus. Briefly, primers Chlfr_F (5 0 -ACCTGGTACGCTCAAGTTTTC-3 0 ), Chlfr_R (5 0 -GCGTGTGTGAT-GAAGGCTTT -3 0 ) and probe Chlfr_P (5 0 -FAM-TCCCTTATTCCC-AGGCGAAA -TAMRA-3 0 ) targeting the 16S rRNA gene locus were used in a TaqMan assay. Real-time PCR was performed using the 2x TaqMan Fast Advanced Master Mix supplemented with ROX (Applied Biosystems), 0.
8. 31 [8] , and a phylogenetic tree was built using RAxML v8 with the GTRGAMMA model and 100 bootstrap replicates. The plasmids of the three new strains were annotated manually by identifying the ORFs using Geneious v10. Each ORF was searched (BLASTX) against the NCBI non-redundant protein sequence database for annotation. Linear comparison of plasmids was constructed using Easyfig v2. 2. 3 [47] with the C. psittaci plasmid pCps6BC serving as a reference.
Pairwise comparison of genome sequences, percentage of conserved proteins (POCP), and average amino acid identity (AAI). The parameters of the tetranucleotide signature frequency correlation coefficient (tetranucleotide regression) and average nucleotide identity (ANIb) were determined as described previously [38] using the program JSpecies v1. 2. 1 with default parameters. The percentage of conserved proteins (POCP) was calculated using a Ruby implementation of the formula given by  Qin [37]  (https://github. com/hoelzer/pocp) and the average amino acid identity (AAI) was calculated using the MiGA program  [39]. Additional long-read-based analysis of major outer membrane protein (MOMP) and in-silico assessment Nanopore long reads spanning the region of interest with multiple copies of ompA-like genes were visualized from the long read mapped to the hybrid assembly using the matplotlib library  [16].
The underlying python script can be found in the OSF repository (https://osf. io/zwgc9/). Besides, we performed draft assemblies for all three flamingo strains with the ONT long-reads only using Flye  [20]  to reduce errors possibly induced by an initial shortread graph as used by Unicycler. First, the longest ONT reads were selected with Filtlong v0. 2. 0 (https://github. com/rrwick/Filtlong) to achieve a coverage of approximately 100X per strain and subsequently assembled with Flye using the parameters-plasmids,-m eta, and a genome size of 1 Mbp. The draft assemblies were then polished in one round of Racon v1. 4. 7 [50] using minimap2 v2. 17 to align the long reads followed by a final polishing with Medaka v1. 0. 3 (https://github. com/nanoporetech/medaka) using the r941_min_high_g360 model.
Genome data and comparison with other species of the Chlamydiaceae The three new flamingo isolates were subjected to Illumina and Nanopore sequencing. De novo hybrid assembly of raw reads produced a single scaffold representing the entire chromosome for each of the three isolates. Assembly parameters, such as percentage of mapped reads, error rate and coverage of chromosomes and plasmids are given in Table  S2. Basic parameters of whole-genome sequences as compiled in Table  3  are similar to those of the established Chlamydia spp. In particular, the number of CDS identified in isolates 14-2711_R47, 15-2067_O50 and 15-2067_O99 are 981, 980, and 983, respectively; G + C values are slightly above 40%, and only one rRNA operon and one plasmid were found. Genome sizes of the flamingo isolates are between 1182 and 1195 kbp and thus slightly larger than Chlamydia spp.
27%) proteins with their homologs in the genus Chlamydia. In contrast, isolate 14-2711_R47 shares low sequence similarities of the RpoN, PepF, Adk and FtsK proteins with the isolates 15-2067_O50 and 15-2067_O99, thus suggesting that they represent different species. Finally, the two nearly identical isolates 15-2067_O50 and 15-2067_O99 share 100% amino acid identity for most of the taxonomically relevant proteins, except for SucA and FtsK (99. 79% and 99. 76% identity, respectively), indicating that they belong to the same species. Further comparative genome analysis of the flamingo isolates and type strains of 11 members of the genus Chlamydia plus Candidatus C. ibidis included the use of UpSetR, a software package for visualization of shared and unique genomic items (Fig. 3 ). While the new strains share a core genome of 756 genes with the species of the genus Chlamydia, a set of 110 genes, roughly 11 % of their coding capacity, is exclusively encountered in the three flamingo strains.
Acknowledgements Sampling of flamingos was performed in collaboration with the research center  Tour du Valat  in the framework of annual catches for ring identification organized by  Arnaud Béchet. We are partic-ularly thankful to  Olivier Yvernogeau  ( Genindexe ) for sharing DNA samples from flamingos for initial analysis. The authors would also like to thank  Lisa Wolf  and  Jana Jagiela  for excellent preparation of samples for electron microscopy and  Wolfram Maginot  for his excellent photographic assistance. Martin Hölzer  appreciates the.
PLOS PATHOGENS The viral proteins Tax and HBZ control host RNA metabolism identification of the interactome networks between Tax/HBZ and cellular regulators of gene expression. We then measured the effects of Tax and HBZ on gene expression at both the transcriptional and post-transcriptional levels. Integration of these interactome and transcriptome datasets provides mechanistic insights into HTLV-1 infection-associated alternative splicing events and leukemogenesis.
2e -16 ). We next used 90 interacting pairs in a validation experiment using an orthogonal assay, the Gaussia princeps protein complementation assay (GPCA)  [28]. The validation rate was 83% (Fig 1B  and 1C ), demonstrating the high quality of our dataset (Y2H_2020) that represents an increase of 32% and 51. 6% of known TFs and RBPs interacting with Tax, respectively (Fig  1D ). Compared to Tax, fewer interactions were available in the literature for HBZ, and our result represents a substantial increase of 65. 5% and 93. 6% of TFs and RBPs interacting with HBZ, respectively (Fig  1D ). We expanded our catalog of systematically determined binary Tax and HBZ interactions with host proteins (Y2H_2020) with high quality interactions reported in the literature (Lit_2020) (Figs  1D  and S3 ). To classify Tax and HBZ interacting partners in functional categories we examined their repartition in the MSigDB hallmark gene sets  [29]  (Fig  1E ).
Impact of Tax and HBZ expression on cellular gene alternative splicing events Analyses of Tax and HBZ host cell perturbations have predominantly focused on transcriptional effects. However, from our interactome data, the interacting proteins annotated as "post-transcriptional regulators" represent 54% and 44% of Tax and HBZ partners involved in gene expression regulation, respectively (S3 Fig). To assess the impact of Tax and HBZ on the cellular splicing landscape, we statistically computed splicing events using the rMATS software (Fig 3A,  [46] ). We focused on 5 types of alternative splicing: Skipped Exons (SE) or cassette exons, Mutually Exclusive Exons (MXE), alternative 3' Splice Sites (3'SS), alternative 5' Splice Sites (5'SS) and Retained Introns (RI)  [46] , which were quantified as Percent Spliced In (PSI).
Lastly, this study identified genes such as IKZF1-5, CLK1, FRS2, HNRNPD, HSPH1, ITGAE, MKI67, RTKN2 and SAT1 that could be used as potential biomarkers for ATLL because they are shared between our study using, Jurkat cells, and a dataset from untransformed infected CD4+ T-cell clones and ATLL samples  [20, 81].
RNA extraction, RT-PCR and RT-qPCR Total RNA extraction from cell pellets was performed according to the manufacturer's protocol (Nucleo Spin RNA kit from Macherey-Nagel) and cDNA was obtained by reverse transcription with random primers using the RevertAid RT Reverse Transcription Kit from Thermo Fisher. One or half a microgram of total RNA was used to make cDNA, which were diluted 100 times to perform PCR amplification with specific primers, using Taq polymerase (Thermo Fisher). PCR products were migrated by SDS-PAGE and revealed with Gel Star (Lonza) under UV light. Bands were quantified using ImageJ software  [84]. Validation of ASEs detected in Jurkat cells was performed by quantifying bands obtained after PCR amplification of regulated exons. The primers used target flanking exons and allowed detection of inclusion/exclusion of the alternatively spliced exon.
RNA-seq data analysis Libraries were prepared with the Illumina Truseq stranded mRNA sample prep kit and pairedend sequencing was performed with the Illumina NextSeq500 PE2X75 system by the Genomics platform at GIGA, University of Liege. Three replicates were made for each condition analysed (Jurkat iTax +Dox, Jurkat iTax -Dox, Jurkat iHBZ +Dox and Jurkat iHBZ -Dox). Sequence reads were aligned to the human genome hg19 (UCSC) using STAR  [85]. Differential expression analysis was performed with DESeq2  [86]  on read counts from STAR quant Mode. Genes were considered significantly up-or down-regulated if their base 2 logarithm fold change was >1 or <-1 and their adjusted p-value was <0. 01. Analysis of Alternative Splicing Events (ASEs) was performed with the rMATS software (v3. 2. 1 for the Japanese cohort and iTax iHBZ and v4.
, 2015). For the purpose of this study, we selected: 35 ATLL patient samples (17 acute, 11 chronic, 4 lymphoma, 2 smoldering, 1 unknown), 3 samples from asymptomatic individuals infected with HTLV-1 (ACs) and CD4+ T-cells from 3 healthy volunteers. RNA-seq data from the Afro-Caribbean cohort were previously described  [8, 55, 56]. We selected 29 ATLL cases (all of the acute subtype), 5 HTLV-1 infected asymptomatic carriers (ACs) and 4 CD4+ T-cell samples from healthy volunteers.
Gene enrichment analysis Gene enrichment analysis on differentially expressed genes was performed with GSEA 3. 0 (Gene Set Enrichment Analysis) in pre-ranked mode using the hallmark gene sets from the Molecular Signatures Database  [29]. GO enrichment analysis was performed on alternatively spliced genes with Gorilla  [87]  using all detected ASEs as a background. Other enrichment analyses were performed by hypergeometric tests or by calculating empirical p-values in R.
RNA-Binding motif enrichment analysis Sequences of regulated exons detected by rMATS (SE events only) and up to 200 bp of their flanking introns were retrieved using bed tools v0. 9. 1. Control sequences consisted of detected ASEs with low ΔPSI values and high FDR. The FASTA files generated were then interrogated for known RNA-binding motifs from the literature  [91]  using the AME software from MEME suite 5. 0. 2 with default settings  [57]. Motifs with an adjusted p-value <0. 05 were considered significant.
Acknowledgments We thank  Dr. Clara L. Kielkopf  ( University of Rochester Medical Center, USA ) and  Dr. Yves Jacob  ( Institut Pasteur, Paris, France ) for DNA constructs. We thank the following University of Liege core facilities: Cell Imaging, Genomics and Viral vectors for their services. We thank  Patricia Piscicelli  for technical assistance in TEM. In memory of  Prof. Renaud Mahieux  who made a profound impact on the advancement of molecular understanding of the roles of Tax and HBZ. He was a member of the evaluation committee for C. V's. PhD thesis behind this work.
Funding: Computational resources have been provided by the  Consortium des E ´quipements de Calcul Intensif (CE ´CI) , funded by the  Fonds de la Recherche Scientifique (FRS-FNRS, Belgium)  under Grant No. 2. 5020. 11and by the  Walloon Region. This work was primarily supported by the  FRS-FNRS  grants  PDR 14461191  and  Televie 30823819  to J-C. T;  Fund for Research Training in Industry and Agriculture  grants  24343558  and  29315509  to C. V. ;  Flanders Research Foundation  grant #  G0D6817N  and  KU Leuven  grant (" Vaast Leysen Leerstoel ") to J. V. W; and  National Institutes of Health  grants  P50HG004233  to M. V. and  U41HG001715  to M. V. , D. E. H. , and M. A. C. ; M. V. and F. D. are Chercheurs Qualifies Honoraires, and J. C. T. a  Maitre de Recherche of the F. R. S. -FNRS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Liberzon A, Birger C, Thorvaldsdottir H, Ghandi M, Mesirov JP, Tamayo P. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 2015; 1: 417-425. https://doi. org/10. 1016/j. cels. 2015. 12. 004 PMID: 26771021.
An additional challenge is the treatment of cHCL patients with active infection and a high risk of SARS-CoV-2 infection. In these patients with the current extent of the pandemic, alternatives to nucleoside analogs are being considered: -BRAF inhibitors Vemurafenib induces rapid responses without myelosuppression  [34, 35, 42]. Responses are expected to be of shorter duration than with nucleoside analogs. Thus, treatment with BRAF inhibitors may be considered as bridging to a later treatment with one of these chemotherapeutic agents. However, additional therapy with other agents should be added when cytopenias develop indicating relapse after stopping the BRAF inhibitor. Other BRAF inhibitors (dabrafenib, encorafenib) are also expected to be effective, but the database is smaller. The duration of response to vemurafenib relapsed or refractory cHCL was related to the quality of remission achieved.
Acknowledgements This work was supported by the  Hairy Cell Foundation. Administrative support was provided by its executive director  Anna Lambertson. Compliance  with ethical standards Novartis; research funding- BMS ,  Amgen, Xencor, Macrogenics, Orsenix, Abbvie, Taiho, Prelude, Astex. TR: research funding:  AstraZeneca ,  Medimmune, Roche, Janssen, Abbvie, Advisory Board; AstraZeneca, Jassen, Abbvie ; travel grant:  Roche ,  Janssen ;  Honoraria: Abbvie ,  AstraZeneca ,  Janssen. KAR: received research funding from  Genentech ,  AbbVie ,  Janssen , and Novartis all paid to her institution, consulted for  Acerta Pharma ,  AstraZeneca ,  Innate Pharma ,  Genentech ,  AbbVie , and  Pharmacyclics , received travel funding from  AstraZeneca. AS: consultant and  Advisory Boards: AstraZeneca  and  Innate Pharmaceuticals ;  Speakers Bureau :  AbbVie and Pharmacyclics.
A mosaic mode acquisition (slide explorer from μManager) was used to scan the slice in a low-resolution mode, each picture composing the mosaic corresponding to 150 × 65 μm 2 (more than 1000 pictures/map). Power measured on the sample was close to μW/ cm 2. The whole system was controlled via μManager  (49). ImageJ software (50) was used to quantify the Trp/tyrosine fluorescent intensity of each ROI and to calculate the mean fluorescence normalized to the surface area of the ROI. Synchrotron FT-IR microspectroscopy sFT-IR microscopy was performed at the SOLEIL synchrotron (Gif-sur-Yvette) using the SMIS beamline (SOLEIL Synchrotron). All spectra were recorded in transmission mode on a Nicolet Continuum XL microscope (Thermo Fisher Scientific). The microscope comprises a motorized sample stage and a liquid nitrogen-cooled mercury cadmium telluride detector (50 μm element size).
The microscope operates in confocal mode using a ×32 infinity corrected Schwarzschild objective (numerical aperture = 0. 65) and a matching ×32 condenser. All IR spectra were recorded using a dual mask aperture of 10 × 10 μm 2. Individual spectra were saved in log (1/R) format at 4 cm -1 spectral resolution, with 128 coadded scans encompassing the mid-IR region from 4000 to 800 cm -1. All IR spectra were preprocessed and submitted to multivariate data analysis (Matlab, version 9. 5; Mathworks). Spectra showing a strong baseline distortion because of dispersion artifact have been carefully removed and classified as outlier spectra. Second derivatives of the spectral data were assessed (nine-point Savitzky-Golay filter) to enhance the spectral resolution of the absorption bands and remove baseline variations inherent to the analytical environment and the optical or physicochemical properties of the heterogeneous samples.
In vivo luminescence measurements Bioluminescence measurements of the NF-κB luciferase transgenic mice were measured using the IVIS 200 imaging system (PerkinElmer). Mice were anaesthetized, and luminescence was measured 5 min after intranasal injection of 50 μl of PBS containing D-luciferin (0. 75 mg kg -1 ; Sigma). Living Image software (version 4. 0; PerkinElmer) was used to measure luciferase activities. Bioluminescence images were acquired for 1 min with f/stop = 1 and binning = 8. A digital false-color photon emission image of the mouse was generated, and photons were counted within the whole-body area. Photon emission was measured as radiance in p. s -1. cm -. sr -. ELISA CXCL1 (KC) and IL-1β concentrations in mice BAL fluids were determined using DuoSet ELISA kits obtained from R&D Systems.
Acknowledgments-We thank  Gwladys Meesemaecker ,  Laetitia Guedeville ,  Mathilde Bauducel ,  Marlène Hery ,  Charline Pontlevoy ,  André Tiffoche , and  Jerôme Pottier  ( Unité Expérimentale Animalerie Rongeurs, INRAE, Jouy-en-Josas ) for technical assistance with animal experiments. We thank  Pr. Richard Flavell  ( Howard Hughes Medical Institute, Yale University School of Medicine ) for his gift of NF-κB luciferase transgenic mice. This work has benefited from the facilities and expertise of the protein purification platform under the direction of  Dr Mohammed Moudjou  (VIM,  INRAE , Jouy-en-Josas). We are grateful to the MIMA2 platform for access to the  TEM (Christine Péchoux)  and  IVIS200  ( Stéphane Biacchesi ), which was financed by the  Region Ile-de-France (SESAME). We thank  Jean Millet  ( VIM, INRAE, Jouy-en-Josas ) for critical reading of the article.
This work was supported by the  REACTIV   ANR  project ( ANR-17-CE35-0007 ). We acknowledge SOLEIL for provision of synchrotron beamtime (proposals no. 20121092). Author contributions-C. C. , F. J. , and R. L. G. conceptualization; C. C. , O. L. , F. J. , and R. L. G. data curation; C. C. , O. L. , F. J. , and R. L. G. formal analysis; C. C. and M. R. supervision; C. C. and R. L. G. funding acquisition; C. C. and R. L. G. validation; C. C. , O. L. , L. S. , B. D. C. , P. M. , F. J. , and R. L. G. investigation; C. C. , P. M. , M. R. , F. J. , and R. L. G. methodology; C. C. and R. L. G. writing-original draft; C. C. and  R. L. G. project administration; O. L. and F. J. writing-review and editing; B. D. C. , C. -A. R. , M. R. , and F. J. resources.