dataesr/scientific-paragraphs-categorization huggingface dataset:
43. 1  98  , which quantifies transcripts by pseudo-aligning raw sequencing reads to a transcriptome reference. The transcriptome reference was derived from the reference FASTA and annotation GFF3 data using gffread (https://github. com/gpertea/gffread). Raw sequencing reads from each of the lifestages were pseudo-aligned to the reference using kallisto quant (parameters: -bias -bootstrap-samples 100 -fusion), before the differential expression between pairwise combinations of life-stages was determined using sleuth  99. For each transcript, differential expression between life-stages was tested using a Wald test, for which a q-value less than 0. 05 was deemed significant. To explore coordinated gene expression throughout the life-stages, we used clust v1. 8. 4  100  to automatically define and group transcripts by expression level profiles.
Transcripts per million (TPM) counts per life-stage were generated from the kallisto analysis described above and used as input to clust. To explore putative regulatory sequences that may be shared by co-expressed genes, we searched in each group of clustered transcripts for conserved motifs in the 100 bp region immediately upstream of the start codon for each transcript using DREME 101 , after which motif-containing sequences were identified using FIMO 102 (MEME v5. 0. 4). To perform gene set analyses, subsets of H. contortus genes together with associated C. elegans orthologs were obtained using WormBase ParaSite BioMart  17. Functional enrichment analyses for both species were performed using g:Profiler (version e97_eg44_p13_d22abce), with a g:SCS multiple testing correction method applying a significance threshold of 0.
05 103. OrthoFinder v2. 2. 7  104  was used to determine orthologous relationships between protein sequences inferred from the H. contortus V1 and V4, the McMaster H. contortus, as well as H. placei and C. elegans protein-coding gene annotations. Trans-splicing: characterisation of splice leader usage and distribution. We characterised the number and distribution of genes for which mapped RNA-seq transcripts contained the canonical H. contortus SL1 (GGTTTAATTACC-CAAGTTTGAG) and SL2 (GGTTTTAACCCAGTATCTCAAG) sequences. For each splice leader sequence, cutadapt v1. 13  105  was used to detect (allowing up to 10% divergence in sequence match [-error-rate = 0. 1] and minimum match length of 10 bp [-overlap=10]) and subsequently trim the leader sequences from raw RNA-seq reads from each of the life stages, after which all trimmed reads were collated and mapped to the genome using hisat2 v2.
1. 0  106. The relative coverage of the sequence between the trimmed splice leader site and (i) the start codon (using a window 200 bp upstream and 50 bp downstream of the start codon) and (ii) internal exons (excluding the 1st exon), was determined using bedtools coverage. Weblogo 107 was used to visualise sequence conservation in the genomic sequence surrounding the splice leader site, and in the splice leader sequences themselves. The spliced leader sequence splice sites for all life stages were combined into a single dataset for plotting and quantitative analyses. Cis-splicing: differential isoform usage using Leafcutter. To analyse the extent of differential spliced isoform usage between life-stages, we used leafcutter 108 (see http://davidaknowles. github. io/leafcutter/ for installation and scripts described below) to quantify the proportion of reads that span introns and then subsequently quantify differential intron usage across samples.
The visualisation was performed using run_leafviz. R. Population genetic analyses. To understand how genetic variation was distributed throughout the chromosomes, we collated sequencing data from our recent global analysis  27  together with in-house archival sequencing data to form the most complete sample cohort of H. contortus whole-genome data to date. Reanalysis of the global cohort data was necessary, as the previous analysis was performed using the V3 assembly. In total, 338 samples from 46 populations in 17 countries were analysed; the samples together with geolocation, mitochondrial phylogeny describing genetic connectivity between samples, and ENA references to individual sequencing datasets are available to explore using Microreact 109 and can be visualised here: https://microreact.
org/project/hcontortus_global_diversity. Raw sequencing data were mapped to the V4 reference using bwa mem 87 (parameters: -Y and -M options were used to use soft clipping for supplementary alignments, and to mark shorter hits as secondary, respectively), followed by identification of duplicate reads using Picard (v2. 5. 0; https://github. com/ broadinstitute/picard). Samples for which multiple sequencing lanes were generated were mapped independently and subsequently combined into a single dataset using samtools merge. Mapping statistics were generated using samtools (v1. 3) flagstat and bamtools (v 2. 3. 0) stats and compared using MultiQC v1. 3  110. Genetic variation within and between samples was determined using GATK (v 4. 0. 3. 0). GVCFs were first created using HaplotypeCaller; to improve efficiency, this process was performed in parallel for each chromosome for each sample separately, and subsequently combined by chromosome using CombineGVCFs.
205  112  , and from which maximum likelihood phylogenies were generated using iqtree v1. 6. 5  113  (parameters: -alrt 1000 -bb 1000 -nt 1) using automated ModelFinding  114. Principal component analysis (PCA) of the mitochondrial DNA variation was performed using the poppr 115 package in R. Sequencing coverage variation between the autosomes and X chromosome for each BAM file was performed using samtools 116 bedcov, using a BED file of 100 kbp windows generated using bedtools 85 makewindows. Statistics and reproducibility. All statistical analyses, data exploration, and visualisation were performed using R (v3. 5. 0; https://www. R-project. org/) unless otherwise stated.
Acknowledgements We would like to acknowledge members of the  BUG Consortium and Parasite Genomics group  at the  Wellcome Sanger Institute  for insightful discussions over the course of this project. We also thank  Pathogen Informatics  and  DNA Pipelines (WSI)  for their support and expertise, and the  Biosciences Division  at the  Moredun Research Institute  for expert care and assistance with animals. Work described here was supported by  Wellcome 's core funding of the  Wellcome Sanger Institute  (grant  WT206194 ), a  Wellcome Trust Project Grant  to JSG (grant  067811 ), and the  Biotechnology and Biological Sciences Research Council (BBSRC)  grants ( BB/M003949/1 ,  BB/P024610/1  and  BB/K020048/1 ). JDN was supported by a  Graduate Mobility Award  from  McGill University.
Data availability The raw sequence data is available under the ENA accession PRJEB506, with reference to specific sequencing libraries described throughout the text, and/or in Table  S3  of  Laing et al. 2013. RNA-seq data is available from the ENA study ID PRJEB1360. The genome assembly has been made available at ENA (assembly accession: GCA_000469685. 2) and WormBase ParaSite (https://parasite. wormbase. org/Haemonchus_contortus_prjeb506/ Info/Index). A static version of the genome annotation used in this paper is available at ftp://ftp. sanger. ac. uk/pub/pathogens/sd21/HCON_V4_GENOME/ (signoff date: 25th Jan 2019), however, the most up-to-date version of the annotation can be accessed at WormBase ParaSite (https://parasite. wormbase. org/Haemonchus_contortus_prjeb506/ Info/Index/  17  ).
The levels of NCKAP1 mRNA, within their natural fluctuations, were significantly associated with MFS (p=0. 012, Table  S2 ). Indeed, we previously reported that high levels of NCKAP1 were associated with poor MFS  [18]. Levels of CYFIP2 and NCKAP1L might be associated with MFS, although the significance was much lower, p=0. 138 and 0. 288, respectively. Our goal when measuring expression levels of all WAVE subunits in the cohort was to examine whether a particular combination of subunits would create a specific WAVE complex conferring invasive properties to tumor cells. This is why we chose to perform highly accurate measurements by qRT-PCR in our cohort of 527 patients, even if global transcriptomic analyses using cDNA microarrays or RNAseq were already available in public databases containing a larger number of patients.
012. Importantly, in this multivariate model, as in the initial univariate models, NCKAP1 and 6 CYFIP2 have opposite coefficients, indicating that up-regulation of NCKAP1, but downregulation of CYFIP2, are associated with poor prognosis. In the model, the higher the CYFIP2 value, the better the MFS, for a given value of NCKAP1. To illustrate how the second order predictor CYFIP2 modulates the MFS, we ran the model with expression levels found in patient tumors populating the outskirts of the distribution (Fig. 1A ). The extreme values of NCKAP1 dominate the predicted MFS when CYFIP2 values are intermediate (Fig. 1B ). In contrast, extreme values of CYFIP2 significantly oppose the effect of NCKAP1, when NCKAP1 values are not extreme. To validate our prediction concerning the roles of NCKAP1 and CYFIP2 in controlling MFS, we used a public database of breast cancer patients, where the transcriptome of more than 2500 tumors was analyzed by Affymetrix chips  [22].
DISCUSSION 10 Here we have systematically analyzed the expression levels of WAVE complex subunits in a cohort of breast cancer patients. Ad hoc statistical modeling, taking into account assembly rules among paralogous subunits, increased the statistical power of the analysis and revealed the unique role of the CYFIP2 subunit, whose overexpression is associated with good prognosis for metastasis-free survival. These findings were validated using an independent cohort of breast cancer patients available in public databases. CYFIP2 had previously been implicated in pathologies, since it is mutated in children affected with intellectual disability and epileptic encephalopathy (MIM #618008)  [33, 34]. In zebrafish, CYFIP2 loss-of-function mutations result in defective axonal pathfinding in retinal ganglion cells  [35].
Antibodies and Western blot Cells were lysed in RIPA buffer and analyzed by Western blot. SDS-PAGE was performed using NuPAGE 4-12% Bis-Tris and 3-8% Tris-Acetate gels (Life Technologies). Nitrocellulose membranes were developed with horseradish peroxidase (HRP) coupled antibodies (Sigma) and SuperSignal West Femto chemiluminescent substrate (Thermo Fisher Scientific). Home-made rabbit polyclonal antibodies CYFIP1, ABI1, WAVE2 were previously described  [27]. The mouse monoclonal antibody, 231H9, targeting BRK1 was previously described  [49]. The antibodies targeting CYFIP-2 (Sigma SAB2701081), NCKAP1 (Bethyl A305-178A) and Tubulin (Sigma T9026) were purchased. Quantification of Western blot was performed by densitometry using the ImageJ software.
Migration assays and live cell microscopy Transwell migration assays were performed using FluoroBlok inserts with 8 µm holes (Corning, 351152), covered with 20 μg/ml fibronectin (Sigma, F1141). MDA-MB-231 cells were plated in serum-free medium and allowed to migrate towards serum-containing medium for 16 h, incubated with 4 μg/ml calcein AM (Sigma, C1359) for 1 h, and images of fluorescent cells were acquired and quantified using ImageJ software. Wound healing was performed using the lifting of Ibidi silicone inserts as previously described  [56]. A picture was taken every 10 min for 18 h. 2D migration was performed using 8 chamber Ibidi dishes (Biovalley 80826) covered with 20 μg/ml fibronectin. 3D migration was performed in 2 mg/ml collagen gel polymerized at 37°C (rat tail collagen type I, Corning 354263), with the cells sandwiched between two layers of collagen.
An inverted Axio Observer microscope (Zeiss) equipped with a Pecon Zeiss incubator XL multi S1 RED LS (Heating Unit XL S, Temp module, CO 2 module, Heating Insert PS and CO 2 cover), a definite focus module and a Hamamatsu camera C10600 Orca-R2 was used to perform videomicroscopy. Pictures were taken every 5 min for 24 h for 2D migration, and every 20 min for 48 h for 3D migration. Random migration of single cells and migration persistence, based on the angular shift between frames, was analyzed as previously described  [57]  using DiPer programs  [58]. The stable MDA-MB-231 cell line expressing GFP-CYFIP2 was plated on fibronectin-coated slides. Ten min videos (with one frame every 10 sec) were obtained to study GFP localization, while F-actin was labeled using the vital SiR-actin labeling reagent (Cytoskeleton, Inc).
For cell migration quantification, embryos were injected at the one-cell stage with 1. 5 nl of a solution containing Histone2B-mCherry mRNA (30 ng/μl) and either control morpholino (0. 1, 0. 2 or 0. 8mM), MoCYFIP1 (0. 2mM), MoCYFIP2 (0. 1mM) or MoNCKAP1 (0. 8mM), with or without mRNAs encoding either human CYFIP1 (10ng/μl), human CYFIP2 (10ng/μl) or human NCKAP1 (10ng/μl). Injected embryos were mounted in 0. 2% agarose in embryo medium and imaged between 60% and 80% epiboly (6. 5-8. 5 hpf) under an upright TriM Scope II (La Vision Biotech) two photon microscope equipped with an environmental chamber (okolab) at 28°C using a 25x water immersion objective. Visualization of 3D movies and nuclei tracking were done using Imaris (Bitplane). Cell migration parameters were extracted using custom Matlab (Math Works) code and autocorrelation was computed using DiPer  [58].
Acknowledgements We thank  Nelia Cordeiro  for technical support,  Theresia Stradal  for sharing shRNA plasmids targeting  NCKAP1  and  CYFIP2 , and  Chuang Yu  for his help with statistical tools. This work was supported by grants from the  Agence Nationale de la Recherche  ( ANR-15-CE13-0016-01  for AMG and NBD), from the fondation  ARC pour la Recherche sur le Cancer  ( PGA120140200831  for AMG and IB and  DOC20170505494  to NM), from  Institut National du Cancer  ( INCA_6521  for AMG and IB, and  INCA_11508  for AMG, IB and ML).
Even though extreme NCKAP1 values drive MFS in the red and purple patients, the extreme values of CYFIP2 rule the outcome of the green and turquoise patients at intermediate values of NCKAP1. The model thus predicts that high levels of NCKAP1 are associated with poor prognosis, whereas high levels of CYFIP2 are associated with good prognosis. (C) Validation of the prediction using a public database containing 2519 breast cancer patients.
These ambiguous connections raise the question of whether caspase activity in a whole tumor is linked in any way to its immune status. A universal role for apoptotic caspase activity in antagonizing inflammatory processes would predict that high levels of active caspases should negatively correlate with inflammation markers and immune infiltration. Exploration of public databases comprising matched proteomic and RNA expression data indicate that this is not always observed (personal computation based on data sets from the Cancer Genome Atlas 2012  [123] ). It is intriguing to note that, instead, in breast cancers, ovarian serous cystadenocarcinoma, colorectal adenocarcinoma, or skin cutaneous melanoma, active caspase 7 levels (measured by Reverse Phase Protein Array) positively correlate with numerous molecular markers of cytotoxic immune cell activation (including STAT1, Interferon type II, or granzyme genes) and immune checkpoints (including CTLA4, CD274, or PDCD1), suggesting tonic immune reactions in caspase 7 high tumors.
Funding: This research was funded by  SIRIC ILIAD, INCa-DGOS Inser-12558. Acknowledgments: This manuscript was prepared in the context of the  SIRIC ILIAD program  supported by the  French National Cancer Institute national (INCa) , the  Ministry of Health  and the  Institute for Health and Medical Research (Inserm). We thank  Laurent Maillet  for fruitful discussion.
Acknowledgments and funding The authors would like to acknowledge the  INRA -Institut National de la Recherche Agronomique  for the support of this work and  CAPES -Brazil. This work was supported by  Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG) ,  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) ,  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)  and, Finance Code 001 and by grants from  INRA -Institut National de la Recherche Agronomique, France.
In conclusion, this standardized IFA could be a relevant tool to reveal innate and adaptive immune dysfunction in patients suffering from immune-related disorders. not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which was this version posted July 30, 2019. ; https://doi. org/10. 1101/718627 doi: bioRxiv preprint  Gene data from the MI study was obtained from Gene Expression Omnibus (GEO) DataSets (GSE85176)  [12]  and protein data extracted from Duffy et. al supplementary information [15]. Spearman's correlation coefficient (rho) was calculated for each condition and indicated on each graphical representation.
i. (C) Infected cells were treated with 10 µM Nevirapine (NVP) throughout the experiment and fixed at 7 days p. i. (D) Infected cells were exposed to NVP for 3 days p. i. , then NVP was washed out and cells were cultured for another 4 days and fixed at 7 days p. i. Arrows in (A-D) show the position of selected vRNA foci. E Boxplot shows the size of DNA foci in untreated infected cells from donor 2 imaged at 7 days p. i. (B), measured by the FWHM of n = 24 foci. The red line defines the median, top, and bottom limits define upper and lower quartiles, respectively. Whiskers show the full data range, except for outliers. Gray dots are individual data points. F Boxplot shows Pearson's r, measuring colocalization of DNA with RNA foci in the same cells (n = 20). Center line defines the median, top, and bottom limits define upper and lower quartiles, respectively.
Only spots located within the detected ª 2020 The Authors The EMBO Journal 40: e105247 | 2021 nuclei were considered. Colocalization was implemented as a linear assignment problem (Python function linear_sum_assignment from SciPy), where each spot from the first channel is assigned to one spot in the second channel. Each spot can be only assigned once, and only assignments below a user-defined distance threshold are permitted. Spot intensities for colocalized spots were measured as the maximum intensity in a AE 1 pixel window. To calculate P-values for the reported colocalization, the colocalization analysis was repeated 100 times after randomly moving ("jitter") detected spots in one channel by 1,000 nm in XYZ. The reported P-values are the proportion of jitter iterations where a higher colocalization was obtained than in absence of jitter.
Acknowledgements We thank  Mickaël Lelek  and  Andrey Aristov  for help with microscopy and  Benoît Lelandais  for help with image analysis. We thank  Marie-Anne Welti ,  Edouard Bertrand ,  Xavier Darzacq ,  Olivier Schwartz ,  Arnaud Echard , and  Michaela Mueller  for useful discussions and/or comments on the manuscript.
We thank Fabrizio Mammano for kindly providing the modified IN-HA HIV-1 virus and  Stella Frabetti  for technical support. We also thank the  UtechS Photonic BioImaging ,  C2RT ,  Institut Pasteur , which is supported in part by the  French National Research Agency (France BioImaging ;  ANR-10-INSB-04 ; Investments for the Future). We thank the  NIH   AIDS Reagents program  for reagents. We also acknowledge  Investissement d'Avenir grant   ANR-16-CONV-0005  for funding computing resources used for simulations. ER was funded by a  Fondation pour la Recherche Médicale en France (FRM)  grant to CZ ( DEQ 20150331762 ), an  Agence Nationale de la Recherche sur le Sida  et les hépatites virales fellowship to ER (ANRS,  ECTZ74440 ) associated with an  ANRS  grant to FDN ( ECTZ4469 ), and a fellowship by  Institut Carnot Pasteur MS  to ER associated with CZ laboratory.
Financial support This work was supported by institutional grants from the University of Angers and Inserm and by grants from the Ligue contre le Cancer (comité du Maine et Loire, Angers, France) and the regional council of Pays de la Loire (Canceropole Grand-Ouest; projects PlasTICO and Concerto). Léa Paolini is supported by a grant from Angers Loire Métropole and Clément Adam is a recipient of a grant from the French Ministry of Higher Education and Research.
After membrane permeabilization, cells were incubated with anti-IL1β, anti-CXCL8, anti-VEGF or anti-CD14 and related control mAbs (all from BioLegend, San Diego, CA). Data were acquired using a FACSCanto II flow cytometer (BD Biosciences, San Jose, CA) and analyzed with the FlowJo software (Tree Star, Ashland, OR). Results are expressed as relative of fluorescence intensity (RFI) defined as the ratio of specific fluorescence (MFI values with specific mAb) over non-specific fluorescence (MFI values with isotype control mAbs) or as a percentage of labeled cells. Antibodies are listed in Supplementary Table  S2.
NMR analyzes were performed at 298 K (25°C) without tube spinning, using a proton-decoupled carbon pulse program (zgig) with 90° pulses (10 µs, 50 W). Acquisition parameters: decoupling sequence waltz16, 0. 9 s acquisition time, 65 k size of FID, and a relaxation delay (D1) of 1. 2 s. 12,500 scans were done, representing about 10 h analysis per sample. 13 C-quantitation was performed by peak integration with TopSpin 3. 5 pl 7 using maleate (125 µmol per sample) as an internal standard. Frozen cell pellets were resuspended in cold methanol (MeOH) and vortexed for 1 min. Samples were extracted with MeOH:acetonitrile:H 2 O (2:2:1; v:v) and the mixture was then vortexed for 30 sec and centrifuged (8,000×g, 10 min, 4°C). The resulting supernatant was evaporated to dryness using a miVac duo concentrator (Genevac Ltd, Ipswich, UK).
Acknowledgments The authors acknowledge  Muriel Gervois  ( University Hospital of Rennes, Rennes, France ),  Catherine Guillet  and  Jérome Cayon  ( PACeM facility; University of Angers ) for expert technical assistance. This work was realized in the context of the  LabEX IGO program  ( National Research Agency ;  ANR-11-LABX-0016-01 ) and (ii) of  SIRIC ILIAD program  ( National Cancer Institute, Ministry of Health and the Institute for Health and Medical Research ;  INCa-DGOS-Inserm_12558 ).
Slides were observed under a confocal microscope (LSM 880, Carl Zeiss, Oberkochen, Germany) and data were analyzed using Zeiss ZEN software. Western blot analysis. Proteins were extracted as described previously  25. Western blotting was carried out using 40 μg aliquots of protein extract, as previously described  12. The following primary antibodies were used: rabbit polyclonal anti-NTSR2 (1:400, #ANT-016, Alomone Labs), mouse monoclonal anti-BDNF (1:1,000, #MAB648, R&D Systems), rabbit polyclonal anti-TrkB (1:500, #orb214339, Biorbyt), mouse monoclonal anti-CD68 (1:500, #333801, BioLegend), mouse monoclonal anti-CD163 (1:500, #326502, BioLegend), mouse monoclonal anti-CD206 (1:500, #32502, BioLegend), rabbit polyclonal anti-phospho-Src Tyr-416 (1:1,000, #2,101, Cell Signaling Technology, Ozyme, France), rabbit polyclonal anti-Src (1:1,000, #2,108, Cell Signaling Technology, Ozyme), rabbit monoclonal anti-Bcl-2 (1:1,000, #2,870, Cell Signaling Technology, Ozyme), anti-BAFF (1:1,000, #orb76960, Biorbyt), anti-APRIL (0.
2 µg/mL; #AF884, R&D Systems) and mouse monoclonal anti-βactin (1:10,000, #A5441, Sigma-Aldrich). Protein expression was quantified using ImageJ software (NIH). Fulllength Western Blot membranes are provided in the Supplementary Data. Because primary cultures provided low protein yield, several patient's samples could only be analyzed once. Hence, by cutting membranes we optimized data collection and analysis of several proteins at different sizes without repeating membrane stripping. As a result, whole membranes are not available for all but the full-length of truncated membranes are provided in Supplementary Informations.