dataesr/scientific-paragraphs-categorization huggingface dataset:
TLR3 expression levels correlate with favorable prognosis in patients The prognostic value of TLR3 expression has been reported in several cancers  [17]. A high level of expression of this receptor has been associated with good  [18]  and poor  [19]  prognosis, depending either on the propensity of TLR3 to initiate cell death or to drive inflammation. Using publicly available transcriptomic datasets, we analyzed the prognostic impact of TLR3 expression in cohorts of patients with sarcomas. As shown in Fig. 1A , TLR3 expression level is lower in pediatric RMS than in their nontumoral skeletal muscle counterpart, which has a rather low level of expression of the receptor compared to other normal tissues (Supplementary Fig. S1A ). We then stratified RMS according to their fusion-status. In pediatric FP-RMS, the level of TLR3 expression is positively correlated with patient prognosis in two independent cohorts (Fig.
We obtained two clones, in which we successfully replaced one of the two F412 alleles by the L412 one at the endogenous locus. As shown in Fig. 3C, D , introduction of the L412 correction in both RD-KI-L412F clones (clones 1 and 2) is sufficient to restore cell death in response to Poly(I:C). We then questioned the clinical value of L412F status in RMS patients. Because of its high population frequency this variant is filtered out during the processing steps of somatic variant calling, making it difficult to analyze from public databases. However, we were able to perform an analysis of its frequency in patients with FN-RMS, thanks to the raw data available on the St. Jude cloud. As shown in Fig. 3E , the number of FN-RMS patients presenting the homozygous L412F allele was significantly higher than expected compared to the general population (two-sided Fisher's exact test, p = 0.
DISCUSSION In addition to its well-characterized role in the activation of the immune response via the IRF3/NF-KB pathway  [21, 22] , TLR3 is emerging as a potential anti-tumor target in cancers, due to its Fig. 1  High TLR3 expression is associated with a better prognosis in pediatric and AYA sarcomas. A TLR3 expression is decreased in rhabdomyosarcomas (RMS) compared to their non-tumoral counterpart, i. e. normal skeletal muscles (p-value = 0. 0016, unpaired two-sided Wilcoxon test). Publicly available transcriptomic data from Schäfer and Welle's cohort were downloaded from R 2 cancer. B TLR3 expression is associated to a better overall survival probability in pediatric Fusion Positive-RMS samples (p-value = 0. 041, log-rank test). Publicly available E-TABM-1202 transcriptomic data were downloaded from ArrayExpress.
C TLR3 expression is associated with a better metastasis-free survival probability in a cohort of pediatric and AYA osteosarcomas (p-value = 0. 0059, log-rank test). Publicly available transcriptomic data from Kuijjer's cohort were downloaded from R 2 cancer. Until now, the clinical value of TLR3 has mainly been studied in adult cancers, and mostly in carcinomas  [23] , although the beneficial effect of a high level of TLR3 expression has also been established in Neuroblastomas  [24]. The data presented here clearly demonstrate that the clinical potential associated with TLR3 activation is not restricted to the adult population but could also be a promising avenue for the treatment of children and adolescent's cancers. By integrating transcriptomic data, we established here the prognostic value of TLR3 expression in several pediatric, adolescent and young adult sarcomas.
The implementation of this parameter at the time of diagnosis could be useful to improve the risk stratification of young patients with sarcomas, considering the complexity and heterogeneity of these cancers. Moreover, our results reveal that it is possible to restore TLR3 expression in aggressive tumor cells via IFN-1 pre-treatment, and that the addition of Poly(I:C) dsRNA is then sufficient to trigger TLR3induced cell death in vitro and in vivo, notably in osteosarcoma models. IFN-1 was already included in a randomized clinical trial in combination with chemotherapy to compare its benefit in treating patients with osteosarcoma (NCT00134030). Although it was not associated with a substantial advantage in this study, it however shows the feasibility of using a combination of IFN-1 and TLR3 agonists, since such compounds are currently evaluated in some clinical trials  [23].
Correction of L412F polymorphism by CRISPR-Cas9 To perform a single-base substitution from an annotated polymorphism to the reference L412 allele in RD cells, we applied a genome editing strategy based on the use of a mix composed of a crRNA (Alt-R®CRISPR-Cas9 crRNA; 5′-GGTTAGGTTGAATATGTGTA -3′), a tracrRNA (Alt-R®CRISPR-Cas9 tracrRNA, ATTO™550, 1075927), the nuclease from idtDNA (Alt-R® S. p. HiFi Cas9 Nuclease V3, 1081060) and a plasmid encoding L412 TLR3 allele from Addgene (pcDNA3-TLR3-CFP plasmid #13641, gift from Doug Golenbock; http://n2t. net/addgene:13641; RRID:Addgene_13641). sgRNA were found with CRISPOR program (http://crispor. tefor. net/) [35]. 3 µL of 200 µM of crRNA and tracrRNA were annealed by heat at 95 °C for 5 min, then cooled at room temperature for 30 min, to allow complex formation.
Patients cohorts and survival analyses Patients cohorts. Publicly available transcriptomic datasets from Kuijjer (osteosarcoma)  [37] , Dirksen (Ewing sarcoma)  [38] , TCGA 2022-v32 Sarcoma  [39] , and Schafer-Welle (RMS and normal muscle) laboratories were downloaded from R2 Cancer. The Kuijjer and TCGA 2022-v32 Sarcoma datasets were filtered to keep only respectively biopsy samples from children and AYA (<35 years old), and leiomyosarcomas (LMS). Missiaglia dataset  [6]  (accession code ETABM-1202) was downloaded on Array express. The Khan (RMS) dataset was kindly provided by Javed Khan (Center for Cancer Research, National Institutes of Health, Bethesda)  [40]. Survival analysis. R version 4. 2. 0 with the packages survminer (v 0. 4. 9) [41]  and survival (v 3. 3-1)  [42]  were used for Kaplan-Meier survival analysis.
For the Kuijjer and Dirksen datasets that were downloaded from R2, the optimal TLR3 expression cutoff was determined independently for each subgroup according to the KaplanScan method implemented by R2. For the TCGA 2022-v32 Sarcoma dataset, as online R2 sample annotations differed with the annotations downloaded, the R package maxstat (v 0. 7-25)  [43]  was used to identify the optimal TLR3 expression cutoff. Optimal TLR3 expression cutoff was also inferred using maxstat for Missiaglia and Khan datasets, separately for each fusion status (FP-RMS/FN-RMS). All cutoff values are available in Supplementary Table  1. p-values were computed using the log-rank test for all survival analyses.
Evaluation of TLR3 L412F frequency in pediatric RMS Reference TLR3 L412 and L412F allele proportions in healthy individuals were retrieved from the GnomAD v 3. 1. 2 population (https:// gnomad. broadinstitute. org/). We used the genomic raw data from St Jude to evaluate the frequency of the L412F allele, both at homozygous and heterozygous states in RMS samples (https://platform. stjude. cloud/) [44]. L412F variants were retrieved from germline gVCF files using tabix (v 1. 15. 1) [45]  based on the following genomic variant: chr4:g. 186082920C>T using the GRCh38 genome assembly. When several samples (gVCF files) were available for a subject, only one sample was selected to prevent overor under-estimating the number of L412F alleles/samples. Enrichment in L412F genotype was assessed using two-tailed Fisher's exact test.
ACKNOWLEDGEMENTS We thank the patients and their families who consented to participate in this study, as well as the  Ligue Nationale contre le Cancer , the  Ligue Contre le Cancer de Haute-Savoie , the  Ligue Contre le Cancer de l'Ain , the  Fondation ARC , and the  Fondation LEEM (Les Entreprises du Médicament) , which provided the funding for this project. We also thank clinical teams from  IHOPe (Institut d'Hématologie et d'Oncologie Pédiatrique) and HFME (Hôpital Femme Mère Enfant -HCL)  for their support and contributions, as well as all the facilities from the  CLB  and CRCL, and notably the Ex-Vivo platform. We are grateful to  Séverine Tabone-Eglinger ,  Loïc Sebileau  and  Anne-Sophie Bonne  for their help with the management of regulatory procedures, and to  Kathrin Weber  for technical advices on experiments.
DATA AVAILABILITY All data generated or analyzed during the study are included in this published article. Requests for material should be made to the corresponding author. Schafer-Welle (RMS and normal muscle), Kuijjer (Osteosarcoma), Dirksen (Ewing sarcoma), TCGA 2022-v32 Sarcoma (Leiomyosarcoma) are available online on the R2 website (https:// hgserver1. amc. nl/cgi-bin/r2/main. cgi). The Missiaglia (RMS) dataset is available on ArrayExpress under the identifier E-TABM-1202. The Khan (RMS) dataset can be requested by contacting M. D. Javed Khan (khanjav@mail. nih. gov). Scripts used for the analysis made in this manuscript are available at the following link: https://doi. org/ 10. 5281/zenodo. 7852570.
We termed this approach split-iCRAC. After HRV-3C cleavage, most of the RNA bound by Npl3 was detected with the RRMs (Fig. 1B ), and a smaller fraction of RNA was detected bound to the RGG/RS domain. iCRAC libraries were then prepared with RNA isolated from the full-length protein (FL) and the RRM1/2 or RGG/RS domains. After mapping the sequencing reads to the S. cerevisiae genome, unique cDNA sequences from all replicates per sample were merged and used for cluster definition. De novo motif search was done using the HOMER software  34  with sequences containing ±5 nucleotides around the identified clusters. Among all identified consensus motifs (Supplementary Fig. 1A ), only three were present in both FL and RRM1/2 and absent in the negative control (same experiment without the expression of tagged protein in cells) (Supplementary Fig.
Isothermal titration calorimetry ITC experiments were performed on a VP-ITC instrument (Microcal), calibrated according to the manufacturer's instructions. Protein and RNA samples were dialyzed against the NMR buffer. Concentrations of proteins and RNAs were determined using optical-density absorbance at 280 and 260 nm, respectively. 10 µM of each RNAs were titrated with 200-600 µM of protein by 40 injections of 6 µl every 5 min at 40 °C. Raw data were integrated, normalized for the molar concentration and analyzed using the Origin 7. 0 software according to a single site model. A correction of the heats of dilution was only done for the complex formed with Npl3 RRM2 as the effect observed with control experiments was negligeable in the context of the RRM1 and RRM12. All measurements were performed in duplicate.
NMR experiments All the NMR spectra were recorded at 313 K using Bruker AVIII-500 MHz, 600 MHz, 700 MHz, AVIIIHD-600 MHz, 900 MHz equipped with a cryoprobe, and AVIII-750 MHz spectrometers. Topspin 3. 6. 2 (Bru-ker®) was used for data processing and Sparky 3. 133 (http://www. cgl. ucsf. edu/home/sparky/) for data analysis. Protein backbone assignment was achieved using 2D 1 H-  15  N HSQC and 3D HNCACB, while side chain assignments were achieved using 2D 1 H-  13  C HSQC, 3D HcccoNH TOCSY, 3D hCccoNH TOCSY, 3D NOESY 1 H-  15  N HSQC and 3D NOESY 1 H-13 C HSQC aliphatic. Aromatic protons were assigned using 2D 1 H-1 H TOCSY and 3D NOESY 1 H-  13  C HSQC aromatic  58. RNA resonance assignments in complex with Npl3 RRMs were performed using 2D 1 H-1 H TOCSY, natural abundance 2D 1 H-  13  C HSQC and 2D 13 C 1F-filtered 2F-filtered NOESY in 100% D 2 O.
Structure calculations AtnosCandid 2. 1 software 61 was used for peak picking 3D NOESY (15 Nand 13 C-edited) spectra. Preliminary structures and a list of automatically assigned NOE distance constraints were generated through 7 cycles using CYANA noeassign 61. Additionally, intra-protein hydrogen bond constraints were added based on hydrogen-deuterium exchange experiments on the amide protons. For these hydrogen bonds, the oxygen acceptors were identified based on preliminary structures calculated without hydrogen bond constraints. Intramolecular RNA and RNA-protein intermolecular distance restraints were manually assigned and added to the calculation with 62 RDCs restraints in the case of Npl3 RRM1/2. Calculations with the RNA were done using CYANA 3. 98. 4 in which seven iterations were performed, and 500 independent preliminary structures were calculated at each iteration step.
These 50 structures were refined with the SANDER module of AMBER 14. 0 62 by simulated annealing in implicit water using the rna. ff12SB force field 63. The 10 best structures based on energy and NOE violations were analyzed with PROCHECK  [64] [65] [66] [67]. Figures were generated with MOLMOL 2 K 68 and Pymol 4. 6. 0. The Ramachandran plot of the Npl3 RRM1 in complex with RNA indicates that 86. 9% of the residues are in the most favored regions, 12. 9% in the additional allowed regions, 0. 1% in the generously allowed regions and 0% in the disallowed regions. The Ramachandran plot of the Npl3 RRM1/2 in complex with RNA indicates that 71. 2% of the residues are in the most favored regions, 25. 4% in the additional allowed regions, 3. 3% in the generously allowed regions and 0. 1% in the disallowed regions.
UV melting experiments UV melting experiments were recorded on a CARY 100 Bio UV spectrophotometer (Varian) equipped with a temperature-controlled heating unit. The heating rate was 5 °C/min from 15 to 85 °C. Data from control experiments (buffer and protein alone) were subtracted from the data presented in the Supplementary Fig. 10E. The fit of the curves was performed with SigmaPlot13 using a sigmoidal equation with 3 parameters: f = a/(1 + exp(-(xxo)/b)).
To increase the reliability of identifying significant crosslinking sites, the deduplicated reads from the replicate samples were merged and subsequently used for peak calling using iCount  74. A False Discovery Rate (FDR) cut-off of 0. 05 was used to identify significant crosslinking sites. Identified crosslinking sites that were less than three nucleotides apart were merged into one cluster. Clusters that overlapped with ones identified in the negative control sample were not included in subsequent analysis steps. HOMER software  34  was used for de novo motif discovery using the identified clusters flanked by five nucleotides on each side because of the expected short motif. Motif predictions were made for sizes ranging from 2 up to 10 nucleotides. The HOMER motifs were validated by plotting the density of the top motifs in the different samples and the negative control in a window of ±50 nucleotides around the crosslink cluster centers.
Acknowledgements We thank  Dr. Alvar Gossert ,  Dr. Simon Rudisser  and  Dr. Fred Damberger  for the maintenance of the biomolecular NMR spectroscopy platform and  Dr Ina Meuskens  for the access to the CARY instrument. We also thank the SNF (grant numbers:  310030B-189379  and  31003A-170130 ) and  SNF-NCCR RNA and Disease  (grant number:  51NF40-182880 ). V. G. P. acknowledges financial support from the  Swiss National Science Foundation ,  NCCR RNA & Disease, Novartis Foundation ,  Olga Mayenfisch Stiftung  and a  Starting Grant Award  ( EURIBIO260676 ) from the  European Research Council.
Data availability The coordinates of the structures have been deposited in the Protein Data Bank (PDB), under the accession code 7QDD for Npl3 RRM1-AUCCAA and under the accession code 7QDE for Npl3 RRM1/2-AUCCAGUGGAA. The split-iCRAC data have been deposited at ArrayExpress using this accession number E-MTAB-11736. Source data are provided with this paper.
TGFa mRNA was also significantly increased in the granular and upper spinous layers of PC-lesional skin (Figure  1a  and b ). HB-EGF and amphiregulin expression also appeared to be upregulated in PC-lesional skin but at a lower level (Supplementary Figure  S2a  and b ). We next investigated the pattern of HER1-4 expression by immunostaining. We observed that both HER1/EGFR and HER2 expression but not the expression of HER3 and HER4 (data not shown) was increased in all spinous layers of PC-lesional skin compared with those of nonlesional skin and HC (Figure  2a  and b ). To determine whether EGFR was activated, we assessed the phosphorylation of the RPS6, a downstream target of mTOR kinase, and the extracellular signal-regulated kinase 1/2. Immunostaining showed a strong increase in both phosphorylated RPS6 and phosphorylated ERK1/2 in the spinous layers of PC-lesional skin, whereas only a few cells were detected in nonlesional skin and HC (Figure  2c ).
ACKNOWLEDGMENTS We are grateful to  Janice Schwartz  and  Holly Evans  from the pachyonychia congenita project for the genetic testing of the patients with pachyonychia congenita and for their constant support. We are indebted to  Emmanuelle Bourrat  for insightful discussions. We thank  Michael McGrath  and  Kavita Subramaniam  for critical reading of the manuscript, the  SFR Necker Cell Imaging Platform  for technical support, and  Benoit Blanchet laboratory from Cochin Hospital (Paris, France)  for measurement of erlotinib trough levels.
Statistics Data were presented as mean ± SEM. Unpaired two-tailed Mann-Whitney rank tests were used for two group comparisons. One way ANOVA with Tukey's multiple comparison post-test or Kruskal-Wallis with Dunn's multiple comparisons post-test were used for comparison of more than two groups as appropriate. Data were analyzed with Prism 9. 2. 0. 332 (Graphpad Software, San Diego, CA) and a p < 0. 05 was considered statistically significant.
While TNFα clearly inhibited IL-10-induced STAT3 phosphorylation, pre-treatment of human monocytes with pervanadate or NSC-87877 both prevented the inhibition of STAT3 phosphorylation by TNFα (Fig. 3B ), while not affecting that induced by IL-10 (Fig. 3C ). Similar data were obtained with NSC-87877 in the HL-60 cell line differentiated into monocytes with 1α,25 D3 (Fig. S4A  and S4B ). To confirm these observations, confocal microscopy experiments were performed to analyze the phosphorylation status of STAT3 simultaneously to its subcellular localization. Fig. 3D  shows that stimulation of the monocytic cell line for 30 min with IL-10 induced phosphorylation of STAT3 and its localization within the nucleus. TNFα did not induce phosphorylation of STAT3, but inhibited phosphorylation and translocation to the nucleus of STAT3 induced by IL-10.
Acknowledgements The authors wish to thank  Dr. Martine Torres  for her editorial help,  Philippe Letteron  for help with animal experiments,  Edouard Guenzi  for helpful discussion with immunohistochemistry,  Olivier Thibaudeau  for preparation of the paraffin sections, and  Samira Benadda  for the confocal microscopy. MBK is a recipient from the  French Research Ministry.
88 to 5. 4 pct. From the same micrographs the critical defect sizes were estimated with an automated tool to be in the range of 200 to 1800 lm. The simulated shrinkage porosity for the same part, was predicted to be in the range of 1. 67 to 2. 33 pct. This study shows that the scaling factor between the pore Feret diameter and the critical pore size is equal to 2. 9, in accordance to the proportionality of critical pores size clusters calculated from a 2D and 3D dataset. Finally, the simulated casting porosity was compared to that measured on cast blades in critical regions and the predictive accuracy is discussed in detail.
II. METHODS This study investigates porosity in a given low pressure turbine blade cluster (LTP) made of the alloy IN100 with a nominal chemical composition outlined in Table  I. The LPT blades were cast under vacuum. Usually these parts are subsequently hot isostatically pressed to reduce the critical defect sizes and reduce porosity. However, for the purpose of this work the blades were analyzed immediately after casting and without further processing. Firstly, four cast blades from the cluster were cut and analyzed using light microscopy. Secondly, the micrographs obtained from the different cuts were analyzed and then processed with an automated script written in Pythonä(Python Software Foundation) to characterize the observed porosity. The Python script was validated by comparing the accuracy of the 2D metallographic cuts with a serial sectioning technique conducted by Robo-Met.
The shell was created with a constant thickness of 9 mm. The insulation system consists of an inner layer between the two rows and three outer layers of 12 mm each. The materials properties of the alloy were calculated with Computherm, using the chemical composition outlined in Table  I. For the calculation with Computherm, the chemical elements with a content lower than 1 wt pct (B, C, Zr) were not included. The material properties (e. g. thermal conductivity, density etc. ) of the ceramic and insulating material were taken from the ProCAST material database. [16, 27]  The overall process was modeled consisting of the following three main steps: transport of the mold from the pre-heating chamber to the casting furnace filling of the mold under vacuum with the alloy solidification of the alloy.
B. Metallographic Analysis After the manufacturing process was completed, blades number 3, 6, 7, and 10-indicated by shading in Figure  8 -were cut up for the subsequent metallographic porosity analysis. For the investigation, the optical microscope Axio Imager. M2m (Carl Zeiss AB, SE) was used. Each blade was cut in nine different positions as shown in Figure  5  (left). Samples were cold mounted in EpoFix resin and EpoFix hardener and surfaces were polished with silica paper up to 1 lm finish. For each cut, micrographs with an overall magnification of Â2:5 and Â5 were taken. Then, images where analyzed with the post processing software IMAGIC IMS Ò(Imagic Bildverarbeitung AG, CH) to measure the pores-on all micrographs for both magnifications-and the maximum Feret diameter  [28]  only on micrographs taken at Â2:5 magnification.
D. 3D Porosity Evaluation Casting pores can present itself as highly irregular, yet interconnected structures (see Figure  2 right ). The true interconnectivity of such a pore structure can be hidden when performing a 2D metallographic cut [compare the white dashed region in Figure  2  left (2D) to the right (3D)]. Consequently a 3D quantitative analysis is required to validate the 2D results. [39]  The verification of the 2D automated tool described in Section III was performed using the fully automated materials characterization system Robo-Met 3D. [38]  This technique is able to recreate 3D microstructural data from serial sectioning. For the purpose of the verification, three different cuts consisting again of the alloy IN100 were selected and sent to Robo-Met for serial sectioning and 3D rendering (see O ¨ztu¨rk  [31]  for further details of the study parameters).
The last cut of the turbine airfoil, slice F seems to exhibit the lowest amount of porosity overall. The cuts G-I, which are taken across the blade root, appear to exhibit similar porosity levels to the B-E airfoil cuts. Roskosz  [40]  detected a comparable level of porosity in non HIP-ed IN713C with maximum porosity levels located at the relative airfoil height corresponding to cuts B-E in Figure  5. However, the root cuts seem to also show the highest amount of eutectic phases. The porosity percentage of the cuts was measured using the IMAGIC IMS software and the values for blade 3 are presented in Table  II. Measured porosity was found to be highest in cut G (blade root) and lowest in cut F (airfoil shroud). Further, porosity is found to be lower in the central area of the airfoil (cuts C and D).