dataesr/scientific-paragraphs-categorization huggingface dataset:
Library preparation and sequencing RNA sequencing was performed by the GenomEast platform, a member of the 'France Genomique' consortium (ANR-10-INBS-0009). Library preparation was performed using TruSeq Stranded mRNA Reference Guide -PN 1000000040498. RNA-Seq libraries were generated from 300 ng of total RNA using TruSeq Stranded mRNA Library Prep Kit and IDT for Illumina -TruSeq RNA UD Indexes (96 Indexes, 96 Samples) (Illumina, San Diego, USA), according to manufacturer's instructions. Briefly, following purification with poly-T oligo attached magnetic beads, the mRNA was fragmented using divalent cations at 94°C for 2 minutes. The cleaved RNA fragments were copied into first strand cDNA using reverse transcriptase and random primers. Strand specificity was achieved by replacing dTTP with dUTP during second strand cDNA synthesis using DNA Polymerase I and RNase H.
Genome mapping and differential gene expression analyses Reads were filtered and trimmed to remove adapter-derived or low-quality bases using cutadapt v 3. 2 and checked again with FASTQC v 0. 11. 7. Illumina reads were aligned to mouse reference genome (mm10) with Hisat2 v 2. 2. 1. Read counts were generated for each annotated gene using R function "SummarizeOverlaps()" and RPKM were calculated for each gene. Differential expression analysis with multiple testing correction was conducted using the R Bioconductor DESeq2 package v 1. 34. 0. Raw (fastq) and processed data were deposited on Gene Expression Omnibus under accession # GSE202940.
Reanalysis of single cell sequencing of adult mouse adrenals The Seurat R package  (72)  was used to perform clustering analysis of single-cell data from Lopez et al. (51) , available in the Gene Expression Omnibus GSE161751 (control adrenals from 10 weekold male mice). Raw sequencing data and annotated gene-barcode matrices were used for the input. Cells with more than 20 genes and genes expressed in more than 3 cells were selected for further analysis. After studying the distribution of count depth, number of genes, and mitochondrial read fraction, low-quality cells with less than 1000 counts, less than 400 genes detected, and percentage of mitochondrial gene counts higher than 25% were removed. Gene expression in each cell was then normalized by the total number of counts in the cell, multiplied by 10000 to get counts per 10000 (TP10K) and log-transformed to report gene expression as E = log(TP10K + 1).
TCGA adrenocortical carcinoma data TCGA gene expression and clinical ACC data were extracted from the TCGA database (The Cancer Genome Atlas). Distribution in the good (C1B) and poor prognosis (C1A) groups were previously defined, based on unsupervised clustering  (27). Expression data were standardized by the Relative Standard Error of the Mean (RSEM) algorithm and transformed into log2 in order to refocus and symmetrize values' distribution. The macrophage signature was defined as the mean expression (Zscore) of CD74, CXCL2, CCL4, APOE, CCL3, CTSS, C1QA, C1QB, C1QC and AIF1. These were found as highly up-regulated genes in macrophages in single cell RNA sequencing analyses of adult mouse adrenals (51) (see above). For gene set enrichment analyses, TCGA ACC patients were dichotomized based on the expression of a phagocytic signature (mean expression (Z-score) of TYROBP, TREM2 and CD68) with patients classified as high (expression above median) or low (expression below median).
Gene Set enrichment analyses Gene set enrichment analyses were conducted on gene expression data from mouse models and TCGA ACC patients, using GSEA 4. 1. 0 with gene sets from the MsigDB and MGI Gene Ontology databases and with custom curated gene sets (Supplementary Table  5   (32). To reduce the gene expression matrix into simple gene identifier lists for GSEA, genes in each of the lists were attributed to their cognate immune cell type based on their maximum of expression across all cell types. This resulted in gene signatures for each immune cell type that were then used in GSEA (Table ). M0, M1 and M2 macrophages gene sets were further concatenated to result in global LM22 and ImmuCC macrophages gene sets. The mouse adrenal macrophages gene set was defined as the 100 most significantly upregulated genes within the two macrophages clusters (compared to all other clusters) in our reanalysis of the single-cell sequencing study of adult mouse adrenals by Lopez et al.
(51). The cytokine gene set was curated from an extensive search of the literature. NFκB and DNA replication gene sets were extracted from MsigDB C2, Hallmarks and C5 datasets. GSEA output was either displayed as dot plots or enrichment curves. Dot plots represent the normalized enrichment score (NES) and FDR (size of dots defined as -log10(FDR)) and were drawn using the ggplot2 library in R. Enrichment curves were drawn by feeding GSEA output to the.
Gene ontology analysis using the C5 GO database (MsigDB) showed that the top 35 positively enriched gene sets were all related with immune response and inflammation in patients with high expression of the phagocytic signature, suggesting that this subgroup was mounting a more profound immune response than patients with low expression of the signature (Fig S7D). Deconvolution of RNAseq data using CIBERSORTx showed that macrophages were the most prominent immune cell population in the two groups of patients, consistent with mouse adrenals (Fig 8H). Interestingly, it also showed that enrichment of macrophages signatures in the phagocytic-high subgroup of patients was associated with increased cytotoxic CD8 + T lymphocytes signatures (Fig 8H). However, this was also correlated with lower B cells, plasma cells and NK cells and higher T regulatory cells infiltration (Fig 8H), suggesting that the phagocytic-high subgroup of patients had a broad alteration of the immune tumour microenvironment.
). Permutations were set to 1000 and were performed on gene sets. Phagocytosis gene sets were curated from an extensive search of the literature, including papers by Park et al. (73), Lecoultre et al. (74) and Janda et al (75) and extracted from the MGI gene ontology database. Senescence gene sets were extracted from papers by Eggert et al. (37), Kuilman et al. (76), özcan et al. (77), Acosta et al. (78), Fridman et al. (79), Coppé et al. (80, 81), Buhl et al. (82) and Saul et al. (83). LM22 and ImmuCC gene sets were derived from gene expression signatures published in Newman et al. (84) and Chen et al.
Acknowledgments We thank  Sandrine Plantade ,  Khirredine Ouchen  and  Philippe Mazuel  for animal care and  Dr Laura Bousset  ( LMS, London ) for fruitful discussions. Znrf3 fl/fl mice were kindly provided by  Hans Clevers' lab (Ubrecht Institute, Utrecht). The TREM2 antibody was supplied from the  Haass Lab at Ludwig Maximilians University Munich , specifically by  Alice Suelzen  and  Dr Kai Schlepckow. A. B. C. E. F. G. D. H. I. J.
1B ,D; Fig. 2A, B ), and a second, unexpected protein fold that did not match any of the known sheath components of Leptospira (Fig. 1B ,D; Fig. 2C, D ). While neither of these sheath proteins have previously been structurally characterized experimentally, FlaA2 could be identified based on excellent correspondence between its density features and a structure generated by the AlphaFold2 prediction software (Fig. S1D )  (Jumper, Evans et al. 2021). Lower resolution limited the ability to trace and sequence the second sheath component directly from the cryo-EM maps. In order to pinpoint the identity of this second component, a mass spectrometry-based proteomics approach was followed, analyzing purified fcpA-flagellar filaments (Table  S2 ; see Methods). Approximately 100 uncharacterized proteins were thus identified in triplicate replicas from independent purifications, apart from the known filament proteins.
Data Collection Initial micrographs of fcpA -filaments were collected through the program SerialEM (Mastronarde 2005) on the 200kV Thermo Scientific Glacios containing a K2 detector. Images were collected with a pixel size of 0. 896 Å. Wild-type, flaA2 -, and additional fcpA -micrographs were collected on the 300kV Titan Krios microscope, containing a K3 detector, through the program SerialEM. fcpA -and flaA2 -images were collected with super-resolution pixel size of 0. 534 Å and a magnification of 81000x. A magnification of 130000x was used for the wild-type sample, with a super-resolution pixel size of 0. 525 Å. A defocus between -1. 5 μm to -3. 2 μm was used for the fcpA -samples, and a defocus of -1. 5 --2. 6 μm was used for the flaA2 -samples, and a defocus of -3. 0 μm was used for the wild-type sample.
Initial Krios reconstruction 11906 micrographs were collected on the Krios. The initial 4461 micrographs were also processed with Relion 3. 0  (Zivanov, Nakane et al. 2018). Motion correction and CTF correction were performed with MotionCor2  (Zheng, Palovcak et al. 2017)  and Gctf  (Zhang 2016) , respectively. Filament selection was performed with crYOLO (Wagner and Raunser 2020), using filaments manually selected from 20 random micrographs with EMAN2  (Tang, Peng et al. 2007)  for training, and was run with a box size of 320 pixels and a box distance of 56 pixels. This procedure yielded 807,689 image segments. Misselected particles were removed with 2D classification, yielding 803,924 image segments. The pf_smooth program  (Debs, Cha et al. 2020 ) was used to remove discontinuities from the filament trajectories, with the following filament parameters: a rise per subunit of 4.
2% of asymmetric subunits), three bare classes (totaling 48,371 particles, or of asymmetric subunits), and a class possibly corresponding to a part of the sheath that was cut off by the mask (11,823 particles, or 16. 9% of asymmetric subunits). These classes were then used as a seed for the subsequent 3D classification on the full ~4 million particle dataset, utilizing the same parameters for classification. This run converged after 16 iterations, and resulted in one decorated sheath class (416,720 particles, or 8. 8% of asymmetric subunits), three bare classes (totaling 4,011,067 particles, or 84. 7% of asymmetric subunits), and a final class that may represent part of the sheath that was cut off by the mask (310,518 particles, or 6. 6% of asymmetric subunits).
Reconstruction of flaA2 -filaments The flaA2 -micrographs were initially processed with Relion3. 1  (Zivanov, Nakane et al. 2020) , with motion correction and CTF correction performed by MotionCor  (Zheng, Palovcak et al. 2017 ) and gctf  (Zhang 2016) , respectively. Filaments were selected with crYOLO  (Wagner, Lusnig et al. 2020) ; the program was trained using filaments manually selected from 20 micrographs with EMAN2  (Tang, Peng et al. 2007). All flagellar filaments (including those skinny and thick, straight and curved) were selected; resulting in 251,215 particles from 9670 filaments. The crYOLO filament selections were manually separated into a straight/thick, intermediate/curved, or skinny/curved group. The straight, thick filaments (~24 nm in diameter) were used for all subsequent analysis, consisting of 36,151 image segments.
Reconstruction of wild-type filaments Gain referencing was performed in IMOD  (Kremer, Mastronarde et al. 1996)  using the program 'clip', and the particles to a pixel size of 2. 19 Å using the IMOD program 'newstack'. All further processing and refinement steps were carried out in cryoSPARC v. 3. 3. 1  (Punjani, Rubinstein et al. 2017). The Patch Correction tool  (Rubinstein and Brubaker 2015)  was used for motion correction, followed by multi-frame patch CTF estimation; default parameters were used for each. The filament tracer tool (in template-free mode) was used for filament selection, using a 300 Å diameter and a fractional separation distance of 0. 173. 40,061 particles were extracted, with a box size of 192 pixels. Per-particle local motion correction was applied, and misselected particles were removed with 2D classification; this left 36,638 particles.
1% formic acid. Three biological replicates of flagella purifications were analyzed using a nano-HPLC (UltiMate 3000, Thermo) coupled to a Q-Orbitrap mass spectrometer (Q Exactive Plus, Thermo). Tryptic peptides (5 μg) were separated into a 75 μm × 50 cm, PepMapTM RSLC C18 analytical column (2 μm particle size, 100 Å, Thermo) at a flow rate of 200 nL/min using a 90 minutes gradient (from 1% to 35% of acetonitrile in 0. 1% formic acid). Mass analysis was performed in a data-dependent mode (full scan followed by MS/MS of the top 12 m/z in each segment) using a dynamic exclusion list. PatternLab for Proteomics (Version V, http://www. patternlabforproteomics. org/) was used for protein identifications  (Carvalho, Lima et al. 2016). Briefly, raw data were searched against a target decoy database including Leptospira biflexa serovar Patoc_UP000001847 sequences downloaded from Uniprot (December, 2021) and 127 most common mass spectrometry contaminants.
Search parameters were set as follows: enzyme: trypsin; enzyme specificity: full specific; oxidation of methionine as variable modification and carbamidomethylation as fixed modification; 35 ppm of tolerance from the measured precursor m/z. Peptide spectrum matches were filtered using the Search Engine Processor (SEPro) using the following parameters: acceptable FDR: 1% at the protein level; a minimum of two peptides per protein and 10 ppm precursor mass tolerance. Patternlab for proteomics´s Venn Diagram module was used to identify proteins present in all replicates. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (Perez-Riverol, Csordas et al. 2019) partner repository with the dataset identifier PXD030741.
Docking of the FlaA2 and FlaAP models into the wild-type density The program Situs  (Wriggers 2012 , Kovacs, Galkin et al. 2018 ) was employed to dock our FlaA2 and FlaAP models into the wild-type density. The inner-curvature density (which remained unidentified in the tomographic wild-type structure) was used for the docking. A model consisting of one FlaA2 and one FlaAP monomer was used for the docking, which was performed with the program colores, using the inner core wild-type map, an angular sampling of 10°, and an anisotropy factor of 4. Manual docking of these sheath factors in the wild-type density and model, using a model consisting of one core repeat (11 protofilaments), one FlaA2 monomer, and one FlaAP monomer, was consistent with the Situs fittings. A, Fit atomic model of FlaA2 highlighting probable sugar-binding sites.
Acknowledgments This work was supported by  National Institutes of Health  grants  R01 GM110530  (to C. V. S. ),  T32GM8283  (to M. R. B) and  U01 AI 088752 ,  R01 TW009504 ,  R01 AI052473 ,  R0 AI121207  (to A. K. ). ;  Agencia Nacional de Investigacion e Innovacion  grant  FCE_3_2016_1_126797  and  Agence Nationale de la Recherche  grant  ANR-18-CE15-0027-01  (to A. B). Cryo-EM data was collected at the Yale CryoEM Resource that is funded in part by the  NIH  grant  1S10OD023603-01A1. We also thank the  Center for Cellular and Molecular Imaging Electron Microscopy Facility  at the  Yale Medical School  for assistance with this work and thank the  Yale Center for Research Computing  and the  Yale High Performance Computing facility  for guidance and use of the research computing infrastructure.
4 months)  [13]. Combination approaches are required, and studies are planned or ongoing of BRAF and MEK inhibitors  [71] ]). In newly diagnosed and recurrent pHGG, there has been a retrospective, multiinstitutional review of patients with BRAF-mutant pHGG treated off-label with BRAF inhibitors with or without MEK inhibitors, confirming activity  [72]. PBTC-029 a (NCT01089101)  [21, 116]  2010e2025 Recurrent or refractory pLGG Phase 1/2 -Selumetinib NYU 10e00561 b (NCT01338857)  [52, 117]  2011e2013 (dabrafenib þ trametinib [NCT02684058 [68], NCT03919071 [69]], binimetinib þ encorafenib [18years] [NCT03973918 [70]], dabrafenib þ trametinib þ hydroxychloroquine [NCT04201457 Recurrent or progressive LGG (including NF-1) Phase 2 -Sorafenib Novartis 116540 c (NCT02124772)  [118]  2015e2020 Recurrent or refractory malignancies with V600 mutations Phase 1/2 e Trametinib alone or trametinib plus dabrafenib a Active, not recruiting (i.
MAPK pathway inhibitors in leukaemia Approximately 150 children present each year in North America and Europe with JMML, where an excessive production of the monocytes infiltrate other organs including the spleen, liver, lung and gastrointestinal tract. Currently, allogeneic haematopoietic stem cell transplant is considered the only curative treatment and is usually delivered following antecedent chemotherapy for cytoreduction and disease control. Virtually, all patients with JMML have a MAPK pathway mutation  [34]. In ADVL1521 (Phase II study of MEK inhibition with trametinib in children with relapsed or refractory JMML) (NCT03190915  [86] ), 5 of 10 patients enrolled have had an objective response (1 complete response, 4 partial responses) and 2 patients have had prolonged stable disease  [24].
RAS mutations can appear or disappear from diagnosis to relapse, and their prognostic significance remains unknown. Patients with RAS mutations have a higher risk/incidence of CNS relapse, so this is an area of medical need. The ongoing SeluDex trial (NCT03705507)  [89]  is evaluating the role of MEK inhibition with selumetinib in combination with dexamethasone in relapsed or refractory acute lymphoblastic leukaemia  [89, 90]. Recruitment to this trial has proven challenging in the present era of available chimeric antigen-receptor T-cell therapy, commercially or clinical trials  [90] ; however, responses have been noted and the early reports show reasonable tolerability and feasibility. Finally, 43% of paediatric patients with AML have a MAPK pathway mutation at diagnosis  [91]. It has been reported that RAS mutation variant allele frequency often increases at relapse, suggesting a role as a driver or disease modifier.
In the absence of adult data, the design of such trials would be optimised if there were a randomised comparison between monotherapy and combination therapy rather than a retrospective comparison of response rates (e. g. the ROAR trial of dabrafenib plus trametinib in adult patients with BRAFV600E-mutant low-grade and high-grade glioma; NCT0203411  [104] ). To accelerate drug development cross-company, cross-product combined analyses of toxicity would be invaluable. Generally, MAPK inhibitors with higher CNS penetration are preferred for diseases affecting the brain, e. g. CNS tumours or metastatic disease to the brain from other cancers, and this is an important attribute of any inhibitor. Better brain penetration should reduce peripheral toxicity, as less systematic exposure is required to deliver sufficient drug to the brain/target tissue.
Furthermore, determining the optimal duration of therapy is important; treatment for an empirical, but well defined duration with integrated ancillary biological studies should facilitate establishing the rational duration of treatment. Accelerating the introduction of MAPK inhibitors into front-line studies is a priority, as is ensuring that these studies generate data appropriate for regulatory purposes. Early discussion with regulators is crucial, particularly if randomised control trials are challenging to perform. Additional end-points of function and quality of life (as these outcomes are often more reflective of benefit for lower grade tumours such as paediatric low-grade glioma and plexiform neurofibroma) should be included in initial study designs for pLGG and agreed upon prospectively with regulators.
Melanoma (BRAF V600-mutant): Measure (model-ling and simula-tion) to demonstrate that PK, PD, and effi-cacy in adoles-cents (12e18 y) in adults are similar to that agent, dose escala-tion trial / safety, tolerability, PK, PD in R/R solid malignant tumours (1 mo-18 y) Relative bioavail-ability study in adults. Multiple dose 2-stage trial to evaluate PK, safety and activity of cobimetinib (6 months-18 y) (GO29665/ NCT02639546) Multicentre, open-label / PK, safety, tolerability, and preliminary evidence of activity of binimetinib þ encorafenib (12e18 y) DAY101 (6m-efficacy of MEK/ERK pathway-activated tumours: dose finding study Relapsed or progressive low-grade gliomas harbouring BRAF fusions: open0label, single-arm trial/PK, safety, and activity of DAY101 (6m-18y) Newly diagnoses unresectable or sub-totally resected low-grade glioma harbouring BRAF fusions: randomised trial /safety and A.
15 Å (SI Appendix, Fig. S4 , Fig. S5 , Fig. S6  and Table  S2 ). In this map, we were able to unambiguously dock two RIP2K molecules, arranged as an antiparallel dimer, as in RIP2K crystal structures (PDB ID: 5NG0, 4C8B), bound with one molecule of XIAP BIR2 (PDB ID:4J3Y)  (Pellegrini et al, 2017; Lukacs et al, 2013; Canning et al, 2015)  (Fig. 3  and SI Appendix, Fig. S7 , Table  S2 ). We immediately observed that one of the BIR2 mutations (the C213G) known to improve protein quality, is at the interaction interface (Fig. 3F ). Therefore, we did not pursue the structure determination of the RIP2K-XIAP BIR2 AG complex. Both kinase chains are in the active-like conformation based on their αC-helix position (IN conformation) and density can be observed for nucleotide in its active site (Fig.
Consistent with our NMR results, only the folded BIR2 domain binds to RIP2K. Importantly, the bound XIAP BIR2 domain engages both monomers, highlighting the importance of the RIP2K dimer as scaffold for downstream ubiquitination. Binding is driven by the XIAP BIR2 loops and the resulting interface is different from the one described for the caspase 3 and 7 binding sites (PDB Ids: 1I3O,1l51 and SI Appendix Fig. S9A  and 9B )  (Riedl et al, 2001; Chai et al, 2001). All the residues previously described to affect to the RIP2K-XIAP BIR2 interaction contribute directly to protein-protein interaction, except I212, which is located deeper in the Kinase B structure  (Damgaard et al, 2013; Hrdinka & Gyrd-Hansen, 2017; Cavallari et al, 2017). In particular, the two RIP2K patches suggested to be part of the interaction, the R36-R41 basic patch  (Hrdinka et al, 2018)  and the I208-K209 patch  (Heim et al, 2020) , which are 40Å apart in the monomer, are in fact close to each other in the dimer because of the antiparallel kinase arrangement.
In agreement with the biophysical data, we could not identify any 2D classes with double XIAP BIR2, suggesting that the presence of a second copy of XIAP BIR2 in the RIP2K-XIAP BIR2 AG 124-240 sample might be related to the ability of XIAP BIR2 domain to homodimerise  (Lukacs et al, 2013). Interestingly, the complex reconstruction does not show any antiparallel β-sheet interaction of the kinase N-termini, which appear to be flexible. Moreover, the two kinase monomers are closer in comparison to the original active structure (PDB ID: 5NG0, SI Appendix Fig. S8  )  (Pellegrini et al, 2017). From these observations, we speculate that binding of XIAP BIR2 perturbs the kinase dimer, which might be the reason for which only one XIAP BIR2 molecule can be bound. Based on the RIP2K-XIAP BIR2 structure, we can now provide a molecular explanation for the mechanism of XIAP BIR2 and RIP2K inhibitors that prevent complex formation.
Several papers reported that SMAC mimetic compounds (SMCs), which bind in the so called BIR IBM (IAP-binding motif) binding groove, abolish the XIAP BIR2 interaction with RIP2 resulting in impaired ubiquitination of RIP2 protein and diminished NF-κB signalling  (Krieg et al, 2009; Damgaard et al, 2013; Goncharov et al, 2018). These compounds mimic the IBM, a 4 residue linear motif (AVPI) belonging to the SMAC/DIABLO protein (second mitochondria-derived activator of caspases/direct IAP-binding protein with low pI), which antagonises the binding of caspases to IAP proteins  (Gyrd-Hansen & Meier, 2010). The XIAP BIR2 domain binds to the IBM of activated caspase-3 and caspase-7  (Scott et al, 2005). Comparison between our structure and that of XIAP BIR2 in complex with peptide AVPI (PDB ID: 4J46,)  (Lukacs et al, 2013) , clearly shows that BIR2 inhibitor prevents complex formation with RIP2K occluding the IBM groove (SI Appendix Fig.