dataesr/scientific-paragraphs-categorization huggingface dataset:
All patients carried monoallelic rare variants in STAT6 (NM_001178079. 2). The consensus negative selection score of STAT6 reveals a negative selection score that overlaps with known AD IEIs  (Rapaport et al. , 2021) , consistent with the AD inheritance pattern observed in Kindreds C, F, and J (Fig. 1 B ). In addition, and also consistent with an AD disorder, by sequencing both healthy parents (when available) we established that the STAT6 mutation was de novo in Patient 2 (P2), P5, P10, and P12 (from Kindreds B, D, G, and I, respectively; Fig. 1 A ). The disease was fully penetrant in the families studied, as all STAT6 variant carriers were affected. None of the variants have previously been reported in population databases (Fig. 1 C;  i. e. , gnomAD). All the variants were private to the studied kindreds, except the p.
D419G variant which was common in Kindreds A and E. Pathogenicity prediction models identify all of these variants to be pathogenic, evidenced by high pathogenic CADD  (Rentzsch et al. , 2021) , SIFT  (Sim et al. , 2012) , and  Polyphen-2 (Adzhubei et al. , 2010)  scores (Tables  S4  and S5 ). Remarkably, nine patients from six kindreds carried a variant affecting amino acid D419. Importantly, variants leading to amino acid changes at p. D419, p. D519, and p. P643 can be found in the Catalogue of Somatic Mutations in Cancer (COSMIC) database as recurrent somatic variants in lymphoma with some experimental evidence for a GOF phenotype for variants at p. D419  (Yildiz et al. , 2015; Zamò et al. , 2018 ; Fig. S1 A ). The reported variants lie in different protein domains of STAT6, including the DNA-binding domain (p.
Functional analysis of the STAT6 variants confirms their GOF activity To assess the functional impact of the STAT6 variants, we selected HEK293 cells as our model system, as these cells lack endogenous STAT6 but express other components of the IL-4R pathway (Fig. 3  A;  Mikita et al. , 1996). HEK293 cells were transfected with each of the 10 patient STAT6 variants along with three different controls: WT STAT6, a predicted damaging STAT6 population variant found in the gnomAD healthy population database (p. A321V), and a biochemically inactive STAT6 variant (p. Y641F;  Wick and Berton, 2000). To investigate STAT6 transcription factor activity, we conducted luciferase assays with three different reporter plasmids containing STAT6 binding sites  (Li et al. , 2016). While there were some difference related to the specific combinations of reporter plasmids and patient variants, there was evidence of GOF activity with all STAT6 patient variants resulting in higher promoter activity at baseline and after stimulation compared to the controls (Fig.
(C) Frequency and CADD score for missense (black) and predicted LOF (pLOF, blue) STAT6 variants reported in a public database and STAT6 variants reported in our patient cohort (red). The dotted line corresponds to the mutation significance cutoff (MSC). (D) Schematic illustrating the protein domains of STAT6. Amino acid location of the variants shown are highlighted, with the length of the bar corresponding to the number of patients reported with variants at that site. (E) Structural model of the DNA-STAT6 homodimer complex showing location of the different STAT6 variants in relation to the DNA-binding interface. S6 ). This suggests that the altered activity of both p. E382Q and p. D419G is not restricted to enhanced activity of known STAT6 targets alone. Further investigation through gene set enrichment analyses (GSEA) showed increased enrichment in IL-4-STAT6 target genes at baseline (Fig.
Ethical considerations All procedures performed in the study were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. All study participants and/or their parents/guardians provided written informed consent. Research study protocols were approved by local institutions, specifically: The University of British Columbia Clinical Research Ethics Board (H09-01228, H15-00641), University College London Research Ethics Committee (04/ Q0501/119, 06/Q0508/16), University of Hong Kong Institutional Review Board (UW 08-301), National Institutes of Health Institutional Review Board (NCT01164241), Children's Hospital of Philadelphia Institutional Review Board (19-016617), Children's Hospital Bambino GesùEthical Committee (1702_OPBG_2018).
Structural modeling The effects of the STAT6 variants on the protein function and structure were analyzed using three-dimensional models. SWISS-MODEL  (Waterhouse et al. , 2018)  was used to model the variants based on a template structure of the human STAT6 transcription factor solved as a homodimer and in complex with DNA (PDB: 4Y5W, resolution: 3. 1 Å, chains A, C, M, and N;  Li et al. , 2016). Structures were visualized with UCSF Chimera  (Pettersen et al. , 2004).
Tables  S6  and S7  are gene lists from GSEA analysis shown in Fig. 3. Supplemental methods are listed at the end of the PDF. (E) Key genes, previously described to be biomarkers for allergic disease  (Lemonnier et al. , 2020)  in whole blood RNA are presented for the patient samples. Gray shaded area is the range for the expression of these genes in five healthy controls. Olink high-throughput proteomics Serum proteins for P5 were analyzed using a multiplex technology based on proximity-extension assays  (Lundberg et al. , 2011). We measured specific proteins using the Olink panel of inflammation. Briefly, the kit consisted of a microtiter plate for measuring 92 protein biomarkers in 88 samples, and each well contained 96 pairs of DNA-labeled antibody probes. To minimize inter-and intra-run variation, the data were normalized using both an internal control (extension control) and an inter-plate control, and then transformed using a pre-determined correction factor.
The pre-processed data were reported in arbitrary units as normalized protein expression (NPX) that enables individual protein analysis across a sample set analyzed in log 2 scale, wherein a higher NPX correlates with higher protein expression. The data were pre-processed using the NPX Manager Software and OlinkAnalyze R package (v1. 3. 0). The data for two key T H 2 cytokines, IL-4 and IL-13, are highlighted in Fig. S4 E. CD23 and IL-4Rα staining on lymphocytes Expression of IL-4Rα and CD23 was studied on primary patient B cells (within PBMCs). Briefly, primary patient cells were incubated in RPMI with or without IL-4 (10 ng/ml). After 20 h, the cells were washed and stained with the following antibodies: BV510 mouse anti-human CD3 (Cat# 563109; BD; clone: UCHT1 Luciferase reporter assays with 4× STAT6 binding sites A luciferase reporter plasmid encoding a 4× STAT6 binding site (TTCCCAAGAA) was used to assess WT and variant STAT6 promoter activity  (Li et al.
The fragmented mRNA was then applied as template to synthesize the first-strand cDNA using random hexamer-primer and reverse transcriptase. In the second strand cDNA synthesis, the mRNA template was removed and a replacement strand was generated to form the blunt-end double-stranded (ds) cDNA. The ds-cDNA underwent 39 adenylation and ligation with xGen Dual Index UMI Adapters (Integrated DNA Technologies). The adaptor-ligated libraries were enriched by 10 cycles of PCR. The libraries were denatured and diluted to optimal concentration. Illumina NovaSeq 6000 was used for pair-end 151 bp sequencing. De-multiplexed read sequences were aligned to a reference sequence and transcript expression were estimated using the DRAGEN RNA pipeline on Illumina Basespace. Expression data was normalized to reads between samples using the edgeR package in R (R Foundation).
Normalized counts were filtered to remove low counts using the filterByExpr function in edgeR  (Chen et al. , 2016). PCA was done on log 2 (normalized counts+0. 25) in R using the PCA function. Differential expression was conducted using Limma  (Ritchie et al. , 2015) , on patient pre-treatment sample against 5 healthy controls. Differentially expressed genes (adjusted P value <0. 05) were visualized using pheatmap in R. Cell type abundance was estimated using the LM22 signature matrix file in Cibersort  (Newman et al. , 2015). Cell types were reduced to nine immunologically relevant cell types: neutrophils, eosinophils, mast cells, dendritic cells, monocytes, NK cells, CD4 T cells, CD8 T cells, memory B cells, and naive B cells. Raw data are deposited in the Gene Expression Omnibus with accession number GSE215390.
Jurkat RNAseq RNA was extracted in triplicate as previously described  (Lu et al. , 2021)  using a RNeasy Mini Plus Kit (Qiagen) according to the manufacturer's recommendations. RNA was prepared following the standard protocol for the NEBNext Ultra II Stranded mRNA (New England Biolabs) and sequenced on the Illumina NextSeq 500 with Paired End 42 × 42 bp reads. De-multiplexed read sequences were aligned to a reference sequence using RNA-Seq Alignment app (v1. 1. 1) on Illumina Basespace, using Spliced Transcripts Alignment to a Reference (STAR) aligner and Cufflinks 2 for assembly and estimation of gene expression. Expression data were normalized to reads between samples using the edgeR package in R (R Foundation). Normalized counts were filtered to remove low counts using the filterByExpr function in edgeR  (Chen et al.
, 2016). PCA was done on log 2 (normalized counts+0. 25) in R using the PCA function. Differential expression between unstimulated and stimulated samples for all three STAT6transduced Jurkat T cells was accomplished using Limma  (Ritchie et al. , 2015). Differentially expressed genes were defined as those with fold change (FC) >1. 25 and adjusted P value <0. 05. Pathway analysis was done by first performing GSEA with 1,000 permutations using the Molecular Signatures Database Hallmark module. Signal-to-noise ratio was used for gene ranking and the obtainednormalized enrichment scores and P values were further adjusted using the Benjamini-Hochberg method. Pathways with an adjusted P value <0. 05 were considered significant. Leading-edge genes from significant pathways between STAT6 WT and STAT6 c.
Enrichment of this gene set was determined at both baseline and after IL-4 stimulation between WT and p. E382Q STAT6, and WT and p. D419G STAT6 Jurkat T cells. IL-4-STAT6 T H 2 skewing targets were previously reported  (Elo et al. , 2010)  STAT6 target genes that are significantly upregulated in response to IL-4 treatment and that lead to T H 2 skewing. Raw data are deposited in the Gene Expression Omnibus with accession number GSE222646.
Acknowledgments We thank the  National Institute for Health and Care Research (NIHR) BioResource  volunteers for their participation, and gratefully acknowledge  NIHR BioResource centers, National Health Service Trusts , and staff for their contribution. A list of  NIHR BioResource Rare Diseases Consortium  members is available at the end of the PDF. We would also like to acknowledge the  Biomedical Research Centre Sequencing Core  for their assistance with RNAseq and processing. We acknowledge the extended clinical care team at the  National Institute of Allergy and Infectious Diseases  who helped in the care and evaluation of Patient 11. We also thank the extended clinical team at the  Department of Paediatrics and Adolescent Medicine, Queen Mary Hospital, Hong Kong, China  for providing expert care and support.
by a  CIHR   Frederick Banting and Charles Best Canada Graduate Scholarships Doctoral Award  and  University of British   Columbia Four Year Doctoral Fellowship  ( 4YF ). H. Y. Lu is supported by a  Canada Graduate Scholarship ,  4YF ,  Killam Doctoral Scholarship ,  Friedman Award  for Scholars in Health, and a  BC Children's Hospital Research   Institute Graduate Studentship. M. Vaseghi-Shanjani  is funded by the  Vanier Canada Graduate Scholarship  and 4YF. The work by  J. Heimall  was supported by the  Elizabeth Paige Lavin Endowed Chair fund. This project has also been funded in part with federal funds from the  Division of Intramural Research of the National Institute of Allergy and Infectious Diseases, National Institutes of Health. The content of this publication does not necessarily reflect the views or policies of the  Department of Health and Human Services , nor does mention of trade names, commercial products, or organizations imply endorsement by the  US Government.
The Laboratory of Human Genetics of Infectious Diseases  is supported by the  Howard Hughes Medical Institute , the  Rockefeller University , the  St. Giles Foundation , the  French National Research Agency (ANR)  under the  "Investments for the Future" program  ( ANR-10-IAHU-01 ),  ANR CARMIL2  ( ANR-21-CE15-0034 ),  Instituts Thématiques Multiorganismes (ITMO) Cancer of Aviesan , and  Institut National du Cancer (INCa)  within the framework of the 2021-2030 Cancer Control Strategy (on funds administered by the  Institut National de la Santé et de la Recherche Médicale) , the  Integrative Biology of Emerging Infectious Diseases Laboratory of Excellence  ( ANR-10-LABX-62-IBEID ), the  French Foundation for Medical Research  ( EQU201903007798 ), the  Square Foundation ,  Institut National de la Santé et de la Recherche Médicale , and  Paris University Cité.
The ds-cDNA went through enzymatic fragmentation and adapter ligation. Index PCR and SPRIselect size selection were then done as per the manufacturer's protocol. Library size and concentration are determined by Qubit, quantitative PCR, and Bioanalyzer assays. The libraries were denatured and diluted to optimal concentration. Illumina NovaSeq 6000 was used for pair-end 151 bp sequencing with No-vaSeq 6000 S4 Reagent Kit v1. 0 and v1. 5  (#20012866 and #20028312; Illumina). The raw FASTQ data was obtained, and the cellranger count function was then applied to generate single cell feature counts for every single library (Cell Ranger v5. 0. 1). The output of filtered feature-barcode matrix containing only detected cell-associated barcodes was used for further downstream analysis. The default parameters and options were applied for cellranger count after providing the input FASTQ data and the relevant prebuilt references of the Human dataset (GRCh38) downloaded from the 10X Genomic website (https://support.
10xgenomics. com/single-cell-gene-expression/software/downloads/latest/). The filtered expression matrix generated by Cell Ranger for each sample was analyzed and processed with the R package Seurat (v3. 2. 0). Quality control involved first filtering for quality based on the following criteria: mapping to over 200 unique genes, the fraction of mitochondrial transcripts was <10%, and the fraction of unique hemoglobin transcripts was <1%. Scaling and clustering was performed on PBMCs and enriched T cells separately. Dimensionality reduction using PCA was done on the 2,000 most variable genes and UMAP was based on the first 20 PCs. Cell identities were first annotated with SingleR using fine labeling from the Novershtern hematopoietic dataset  (Novershtern et al. , 2011; Aran et al. , 2019)  and Monaco Immune dataset  (Monaco et al.
, 2019)  for PBMCs and enriched T cells respectively. The annotation was refined manually based on the UMAP clustering patterns, grouping the SingleR labels for PBMCs into 10 main populations: naive B cells, memory B cells, naive CD4 + , memory CD4 + , naive CD8 + , memory CD8 + , monocytes, dendritic cells, NK cells, and others. For differential gene expression analyses, we utilized the Seurat implementation of negative binomial test, assuming an underlying negative binomial distribution in RNAseq data  (Robinson and Smyth, 2007; Hafemeister and Satija, 2019). All the computational resources were obtained from the cluster of High Performance Computing Facility of the Centre for PanorOmic Sciences, University of Hong Kong. Raw data are deposited in the Gene Expression Omnibus with accession number GSE215405.
Atomic force microscopy (AFM) Tapping mode AFM images were collected using a multimode microscope controlled by Veeco Nanoscope V controller. The AFM cantilever (silicon, spring constant of 40 N/m, oscillation frequency of 300 kHz) had a radius curvature under 10 nm. Images of the sample surfaces were collected in air under ambient conditions. Sample roughness was averaged from 12 measurements over 1 μm × 1 μm images. AFM images were analyzed with Gwyddion software and its Fast Fourier Transform (FFT) function was utilized to convert AFM images into mathematically defined frequency space. FFT images contain gray-scale pixels and emphasize the degree of alignment present in the AFM images. A round FFT pattern corresponds to a lack of orientation in the initial AFM image, whereas elongated FFT pattern indicates an orientation.
The average thickness of CS/ NC layer was calculated for each film, from 5 images (8 to 10 measurements per image) with ImageJ software. The resulting thicknesses (Table  1 ) are slightly above the theoretical values, due to the limitation of the resolution of SEM images. To overcome the lack of accuracy in the thickness determination with SEM, a second characterization was performed with atomic force microscopy (AFM). Table  1  and Fig. 4  present the CS/CNC thicknesses determined by AFM using the height scratching protocol adapted from  Cranston and Gray (2008). Fig. 4  shows the effect of scratching on the surface and the corresponding height profile of the scratched area. The thicknesses of CS/NC layers, from the height scratch method, were close to the theoretical thicknesses, in the case of CNF and CNF TEMPO coatings.
We found a significant increase in liver macrophages in HBV/HDV mice in comparison to HBV mice, that were further increased when mice were pretreated with RIPK1edit (Fig 3A  , B ). In order to determine the role of the macrophage population in the exacerbation of liver damage due to the absence of RIPK1, they were removed using chlodronate-loaded liposomes (CLL) and liver injury was evaluated. CLL was administered i. v. Next, we assessed the role of TNF-α in the increased liver damage in RIPK1-edited mice due to HBVHDV by employing C57BL/6 mice lacking TNF-α. TNF-α KO mice receiving HBV/HDV showed lower transaminase levels than wt animals 21 dpi (Fig 3F ), in line with our previous published data  [7]. However, transaminase levels and the number of apoptotic hepatocytes in RIPK1-edited mice were similar in TNF-α KO and wt mice (Fig.
CRISPR/Cas9 vectors and gRNA design The pX602-AAV-TBG::NLS-SaCas9-NLS-HA-OLLAS-bGHpA;U6::BsaI-sgRNA plasmid that contains the Staphylococcus aureus Cas9 (SaCas9) expressed under the TBG promoter, the sgRNA under U6 promoter and ITR sequences for AAV vector production was a gift from Feng Zhang (Addgene plasmid # 61593). sgRNAs targeting exonic regions of the Ripk1 gene were designed and selected using Benchling software (www. benchling. com). The selected 21-nt sequences upstream of the 5'-NNGRRT-3' PAM sequence of SaCas9 are shown in Table  2. Annealed oligonucleotides coding for the guide RNA sequences (Sigma) were cloned into BsaI site of pX602 vector using standard molecular cloning techniques.
Peroxidase activity was revealed using DAB+ and sections were lightly counterstained with Harris hematoxylin. Finally, slides were dehydrated in graded series of ethanol, cleared in xylene and mounted with Eukitt (Labolan, 28500). Image acquisition was performed on an Aperio CS2 slide scanner using ScanScope Software (Leica Biosystems). All images were stored in uncompressed 24-bit color TIFF format and image analysis was performed using a plugin developed for Fiji, ImageJ (NIH, Bethesda, MD). Samples from patients included in the study were provided by the Biobank of the University of Navarra and were processed following standard operating procedures approved by the Ethical and Scientific Committees (Reference: 2020-011).
Acknowledgments We particularly acknowledge the patients for their participation and the  Biobank of the University of Navarra  for its collaboration. We thank  Professor John Taylor ,  Professor Frank Chisari  and  Professor Feng Zhang  for providing us with essential reagents for our study. We are grateful to  Elena Ciordia ,  Alberto Espinal , and  CIFA  staff for animal care and vivarium management and to  Laura Guembe  and the imaging department at  CIMA  for technical assistance.
| Data collected We retrospectively collected anonymized data on epidemiological, clinical and histopathological features and treatment received. All prechemotherapy tumor specimens were reviewed by pathologists with experience in NETs and classified according to the 2019 WHO classification based on differentiation and grade. 21 The most common adverse events were graded according to the CTCAE 4. 0 classification. All CT scans were centrally reviewed to measure the maximum variation in tumor size, in order to determine the best morphological response to the FOLFOX-bevacizumab chemotherapy and the date of progression according to the Response Evaluation Criteria in Solid Tumors (RECIST), version 1. 1. 22.
| Patient characteristics Overall, 75 consecutive patients with neuroendocrine neoplasms received FOLFOX-bevacizumab between 2013 and 2020 at the two institutions. After histological and clinical review, we excluded 18 patients with neuroendocrine carcinoma, locally-advanced NET without distant metastases or unavailable data (Figure  1 ). Overall, we included 57 patients. In contrast, a total of 430 patients with metastatic well-differentiated NET were treated in the two institutions during the study period. These 57 patients were characterized by male predominance (65%) and median age of 61. 7 years (Table  1 ). Most patients had a performance status of 0 or 1 (91%). Most NETs originated from the digestive system (88%), pancreas being the most frequent primary NET (67%). Twelve patients (21.
18, 20 Hence, we believe that adding bevacizumab to chemotherapy might increase its efficacy on NETs. Although we did not have comparison groups in our study, FOLFOX alone or bevacizumab alone were previously reported to be less effective, with ORR of 10%-29%  [9] [10] [11] 28  and 13%, respectively. 17 Due to its high response rate, the FOLFOX-bevacizumab combination could be particularly relevant in patients for which the main therapeutic objective is to reduce tumor bulk, such as those potentially secondarily eligible for surgical resection of primary NET and/or metastases. The prospective evaluation of bevacizumab combined to chemotherapy is underway within the BETTER-2 study (NCT03351296), which compares the efficacy of a fluoropyrimidinealkylating chemotherapy with or without bevacizumab in patients with metastatic pancreatic NETs.
8 months). 30 Conversely, another study reported higher ORR but shorter PFS with FOLFOX (56% and 6. 9 months, respectively) in comparison with temozolomidecapecitabine (27% and 12 months, respectively) in first-line. 31 ere is currently no validated biomarker to select the best candidates for either alkylating-or platinum-based chemotherapy. 3] [34]  In contrast, MGMT expression does not appear to influence the efficacy of platinum-based chemotherapy. 34 Hence, we believe that the FOLFOX-bevacizumab combination could be an even more relevant option for patients whose NET shows strong MGMT tumor expression, and thus who are not good candidates for alkylating chemotherapy. The role of MGMT as a predictive marker to either type of chemotherapy is currently under evaluation within the MGMT-NET prospective trial (NCT03217097).