dataesr/scientific-paragraphs-categorization huggingface dataset:
Sequencing quality control and read alignment were obtained using the FastQC software to assess the quality of the fastq files [Andrews S. (2010). FastQC: a quality control tool for high throughput sequence data. Available online at: http://www. bioinformatics. babraham. ac. uk/projects/fastqc]. Reads were aligned to the mouse genome, version mm10, using STAR_2. 5. 3  [63]  and the quality of the alignment assessed with bamtoools  [64]. The Rsubread package  [65]  was used to assign read counts to the genes of the GENCODE basic gene model, version M13 (https:// www. gencodegenes. org), with all isoforms of each gene considered together. Differential expression of RNA-seq datasets. Genes with at least 1 cpm (count per million) in at least the number of samples of the group having the minimum (e.
Gene set enrichment analysis (GSEA). GSEA  [68]  (http:// www. broad. mit. edu/gsea/) was used to analyze the expression profile of a gene signature associated to cholesterol metabolism as reported in  [32]. Comparing transcriptomes of monocyte-DCs and Ly6C low cells with public datasets. Gene expression profiles derived from RNA-Seq experiment were integrated with 4 datasets present in GEO (Gene Expression Omnibus) Database: GSE149619  [24] , GSE90471  [23] , GSE74427  [25]  and GSE49358  [29]. Probeset-level data obtained from GEO were summarized at the gene level, then the gene expression profiles of 10,080 genes evaluated in both the public datasets and expressed in our RNA-seq experiments was considered for the integrative analysis using R and Bioconductor packages (https://www. R-project.
org/) [69]. Data have been quantile normalized using limma  [66]  and samples clustered using hierarchical clustering algorithm with Spearman correlation-based distance in R Principal Component Analysis (PCA) has been performed on scaled data using  [70]. Uniform Manifold Approximation and Projection (UMAP) has been done on the integrated dataset analyzed with Seurat  [71]. Ten PCA components have been used in graph-based clustering, evaluated at different resolutions to identify stable clusters, then fixed at the value of 1 and represented in a bidimensional UMAP plot. Extraction of the lipidic and polar fractions from tumor tissues. Frozen tissue of LLC-Mock, LLC-SULT2B1b and LLC + PFM037 tumors (n = 5 tumors/group) were cut to an appropriate size (mean weight: 10 mg) and placed in microcentrifuge tubes.
1 H NMR spectra acquisition and data analysis. All the lipidic and polar samples were analyzed using Nuclear Magnetic Resonance (NMR). The lipidic fractions were dissolved in 700 µL of deuterated chloroform (CDCl 3 ) and 0. 03 volume percentage of tetramethylsilane (TMS) and inserted in NMR tubes whereas the polar fractions were dissolved in 630 µL of PBS-D 2 O with the pH adjusted to 7. 20, and 70 µL of trimethylsilylpropanoic acid (TSP) (1% in D 2 O). TMS and TSP were used as the internal standards. A 600-MHz Bruker Avance spectrometer with a TCI cryoprobe was used to acquire 1 H spectra on all the lipidic and polar fractions at 300 K for 512 transients using the excitation sculpting pulse sequence to suppress water resonance in the case of polar fractions. All 1 H NMR spectra were manually phased and baseline-corrected and referenced to the CH 3 resonance of TSP or TMS at 0 ppm.
The spectral 0. 50-8. 60 ppm region of 1 H-NMR spectra was integrated in buckets of 0. 04 ppm by AMIX package (Bruker, Biospin, Germany). In details, we excluded, in the case of the polar spectra, the water resonance region (4. 5-5. 2 ppm) during the analysis and normalized the bucketed region to the total spectrum area using Pareto scaling by MetaboAnalyst v4. 0 tool  [72]. The partial least squares-discriminant analysis (PLS-DA) algorithm was applied to explain the maximum separation between the defined class samples in the data. PLS-DA is a multivariate regression method that extracts linear relationships from two data block, X (NMR bucket variables) and Y (information data), by reducing the structured noise and highlighting the maximum correlation between the two matrices. Score, Loading and Variable Importance in Projection (VIP) Plots were used to highlight and assess the role of X-variables (NMR signals) in the classification models and, hence, to prioritize the discriminating peaks for identification.
We show in Loading and VIP plots the top 10 more different NMR signals obtained comparing the different groups. Metabolite assignments were based on the comparison of chemical shifts and spin-spin couplings with reference spectra present in the human metabolome database (HMDB)  [73]. The normalized proton signals of the statistically significant metabolites were reported by box-plot graphs. Tumor challenge experiments. C57BL/6 mice were injected subcutaneously with LLC-Mock (1 × 10 6 ), LLC-SULT2B1b (1 × 10 6 ), RMA (2 × 10 5 ), or B16F1-OVA (2 × 10 5 ) tumor cells. Mock-and SULT2B1b-transduced tumor cells have been described in  [7, 74]. We evaluated tumor size measuring perpendicular diameters by a caliper every 2-3 days. Data are reported as the average tumor volume ± SD. In selected experiments tumors were injected in Ccr2 -/-mice.
Blocking experiments with anti-CCR2 mAb. Mice bearing 7 day-LLC-SULT2B1b tumors were treated every day i. p with 20 μg anti-CCR2 antibody  [34]  or rat IgG control antibody. Mice were sacrificed 12 days after tumor injection and analyzed for tumor weight and for the content of tumor-infiltrating monocyte-DCs Single-cell RNAseq data analysis. Two published datasets were considered in this study: E-MTAB-6149 and E-MTAB-6653 for lung tumor microenvironment and GSE120575 for CD45 + infiltration in melanoma. For lung dataset, tSNE projections were obtained uploading loom files in SCope focusing first on all cell types profiled and then only on myeloid clusters. Moreover, the expression of the cholesterol synthesis signature genes (FADS1, FADS2, HMGCR, FDFT1, FDPS, GGPS1, HMGCS1, DHCR7, DHCR24, CYP51A1, HSD17B7, IDI1, MVK, MVD, NSDHL, MSMO1, SCD5, SCD, TM7SF2, SQLE, LSS, ABCA1, FAS, SULT2B1) was investigated at the population level using normalized-UMI counts only for tumor samples, focusing first on immune-infiltrating population (B cells, T cells and myeloid cells) and then on tumor-associated clusters of myeloid cell type (clusters 2, 3, 4 and 12).
The melanoma dataset was processed retrieving normalized gene expression data, cluster and patient information from GEO database and Supplementary Material of the original paper. Then the expression matrix was imported and processed in Seurat package only for data visualization. Cluster and patient information (response to treatment with immune checkpoint blockers) was added to the dataset using "AddMetaData" function. Also in this case, the dataset was investigated for the expression of the cholesterol synthesis signature genes considering both the percentage and level of expression of the cells belonging to a specific cluster. Wilcoxon rank sum test has been adopted with FindMarkers function (logfc. threshold = 0. 5, min. pct = 0. 2, only. pos = TRUE) to verify if some cholesterol synthesis signature genes could be differentially expressed between different clusters of interest.
Cells were then stained, run by FACScanto (BD) and analyzed with FlowJo software. For the analysis of draining lymph nodes, ccr2 -/-mice (CD45. 2 + ) were injected with 5 × 10 6 tumor cells. After 3 days, mice received CD45. 1 + monocytes and 48 hours later lymph nodes were collected and treated as reported in immunofluorescence staining and confocal analysis section. Antigen presentation assays. Splenic OT-I or OT-II were purified with CD8a and CD4 (L3T4) microbeads (Miltenyi), respectively. Enriched OT-I or OT-II were labeled with CFSE (5 μM) and plated at a density of 1 × 10 6 /ml. Monocyte-DCs and Ly6C low cells were FACS-purified from 10-day established LLC-SULT2B1b tumors and loaded with 10μg/ml of OVAspecific MHC-I and -II peptides to stimulate OT-I and OT-II T cells, respectively. Then, 1 × 10 4 monocyte-DCs or Ly6C low cells were co-cultured with 1 × 10 5 OT-I or OT-II in RPMI complete medium.
Cells were cultured at 37 °C for 3 days and analyzed by flow cytometry for CFSE dilution. Supernatants collected 3 days later, were tested by ELISA to measure the content of mouse IFN-γ in agreement with manufacturer's instructions. T cell proliferation was expressed as proliferation index and CFSE-diluted cells. Proliferation index was analyzed and calculated by FlowJo software. OT-I and OT-II CFSE-diluted cells were presented as the percentage of OT-I and OT-II cells undergoing multiple rounds of proliferation. The percentage of CFSE-diluted OT-I and OT-II cells following activation with peptideloaded Ly6C low cells is relative to the percentage of CFSE-diluted OT-I and OT-II cells activated with peptide-loaded monocyte-DCs (set as 100%). Reporter assay for nuclear receptor transcriptional activity.
1 software. The number of animals per experimental group considers the need to reduce the use of animals to the minimum to obtain statistically significant results for the objectives of the study. Consistently with the underlying experimental design, we assumed a type I error equal to 5% (α = 0. 05) and a power equal to 80% (1-β error prob = 0. 80). In the various comparisons we chose to apply a Student's t test evaluating the differences between two independent groups or an ANOVA test evaluating the differences among three or more groups. Data are expressed as mean ± s. d. and were analyzed for significance by ANOVA with Dunnet's, Bonferroni's or Tukey's multiple comparison test, or by Student's t test. Statistical comparison of overall survival was performed by the log-rank test. The analysis was performed with Prism software.
ACKNOWLEDGEMENTS We thank  Luigi Naldini ,  TIGET San Raffaele Scientific Institute  and  University Vita-Salute San Raffaele  for providing us with the lentiviral vector encoding the mCherry fluorescent protein. We thank  Marco Bianchi ,  Matteo Bellone  and  Giovanni Tonon ,  Vita Salute University and San Raffaele Scientific Institute , for helpful discussion and suggestions. We thank  Stefania Bruzzì ,  Scientific Institute San Raffaele , for technical help. L. R. received a fellowship from the  "Fondazione Umberto Veronesi ". J-A G was supported by a grant from  Robert A. Welch Foundation  ( E-0004 ). This work was supported by the  Italian Association for Cancer Research (AIRC)  ( IG 19016 ,  IG 15452  and  IG 26305 ),  AIRC 5 × 1000 IG 22737  and from  Regione Campania  ( POR FESR 2014/ 2020 -Progetto Campania Onco-Terapie CUP: B61G18000470007.
We also filtered out false-positive rare variants, which were absent or rare in public databases but had a frequency above 1% in our in-house cohort (the ''blacklist''). 40  We found no homozygous candidate copy number variants (CNVs) in known MSMD genes. 41 We identified 26 rare homozygous single-nucleotide variants (SNVs) in 25 genes in P1, and 17 homozygous SNVs in 17 genes in P2 (Figure  1F  and Table  S1 ). In test of the hypothesis of genetic homogeneity, we found only one common gene: IRF1. Both patients displayed homozygous transitions predicted to be nonsense in the IRF1 canonical transcript (NM_002198. 2, 42 GRCh37): c. 385C>T (p. R129*) in P1, and c. 103C>T (p. Q35*) in P2 (Figure  1G ). Sanger sequencing confirmed that P1 was homozygous for p. R129* and P2 was homozygous for p.
Both the c. 385C>T and c. 103C>T variants are predicted to be loss-of-function (pLOF) and are rare or private in public databases (Figures 1E-1I and supplemental information). We studied the impact of the IRF1 variants by transiently transfecting human embryonic kidney (HEK)293 T cells with plasmids encoding wildtype (WT) IRF1 (NM_002198. 2), mutants p. R129* and p. Q35*, or p. A67P cDNAs. We used two previously described LOF mutants isolated from human tumors as negative controls: (1) the missense mutant p. W11R  63  and (2) a frameshift mutant resulting in the deletion of exons 7 and 8  64  (hereafter referred to as D7-8). The proteins encoded by the constructs were left untagged or were tagged with DDK at the C terminus (Figure  S1E ). Immunoblotting of cell extracts showed that the WT-DDK, p.
Both the c. 385C>T and c. 103C>T variants are predicted to be loss-of-function (pLOF). Indeed, both result in the creation of a premature stop codon, p. R129* and p. Q35*, respectively (Figure  1G ). The CADD score of the two variants is 38, well above the 99% MSC  38, 180  of IRF1 at 8. 2 (Figure  1I ). An analysis of public databases, including ExAC, gnomAD v2. 1. 1 or v3. 1. 1, 181 BRAVO/TOPmed freeze 8,  182  UK Biobank,  183  ATAV,  184  the Greater Middle East variome,  185  and the Turkish variome  186  containing WES or whole-genome sequencing (WGS) data from more than 250,000 individuals showed that the p. Q35* variant is private, whereas p. R129* is found in the heterozygous state in two individuals. All the non-synonymous coding or structural variants of IRF1 listed in public WES and WGS databases are present in the heterozygous state, with the exception of one homozygous missense variant (c.
199G>C, p. A67P, rs137993322) in the TOPmed  182  database with a gnomAD MAF of 1. 2e-4 and a CADD score below that of p. R129* and p. Q35* (Figure  1I ). We identified no other homozygous or compound heterozygous non-synonymous IRF1 coding variants in our in-house database of 20,000 WES or WGS from patients with severe infectious diseases. The cumulative frequency of heterozygous pLOF variants in gnomAD is also low (4. 3x10 À5 ) (Figure  1I ). Consistently, the consensus negative selection (CoNeS) score of IRF1 is low, suggesting that this gene is under negative selection, consistent with either recessive or dominant inheritance  187  (Figure  S1D ). These findings further suggest that homozygosity for the pLOF private IRF1 variants identified here was causal for MSMD in P1 and P2. Impaired development of lymphoid and myeloid cells in Irf1 À/À mice IRF1-knockout mice (Irf1 À/À ) were first described in 1993.
The peptide fractions were separated by HPLC for 2 h with a linear gradient on a 250 mm*75 mm EasySpray column connected to an Easy nLC 1200 HPLC machine, with an orbitrap Fusion Lumos mass spectrometer operating in DDA MS2 positive mode used for analysis. Spectra were queried against the human proteome (downloaded from http://uniprot. org/ on 2019/02/12) at an FDR of 1% with Sequest HT through Proteome Discoverer v. 2. 5. A spectral purity of 75% was required to ensure adequate confidence in quantification. Protein abundances were processed further in the Perseus statistical software environment. Abundances were log 2 -transformed and normalized against the median intensity for each sample. The mass spectrometry proteomics data have been deposited with the ProteomeXchange Consortium via the PRIDE  197  partner repository with the dataset identifier PXD037759.
Human IgG from serum (Sigma-stimulated by overnight incubation with 10 3 IU/mL IFN-a2a (#11343506, Immunotools), or 10 or 10 3 IU/mL of IFN-g (Imukin, Horizon Pharma). Cells were washed and then incubated with infected viral supernatants in the presence of 1 mg/mL protamine. Two days later, the GFP expression of living cells was assessed on a BD FACSVerse flow cytometer. The SARS-CoV-2 NYC isolate (GenBank OM345241) was obtained from a de-identified patient in July 2020. The virus isolate was amplified though six-to seven-day passages in Caco-2 cells at 37 C. After each passage, virus-containing supernatant was harvested, clarified by centrifugation (3,000 x g for 10 min), and filtered through a 0. 22 mm-mesh disposable vacuum filter system. The passage 3 stock, used in this study, had a titer of 3.
rRNA and mitochondrial RNA (mRNA) were depleted. RNA-seq libraries were prepared with the Illumina RiboZero TruSeq Stranded Total RNA Library Prep Kit (Illumina) and sequenced on a NovaSeq machine in the 100-or 150-nt, paired-end configuration. The RNAseq FASTQ files were first inspected with FastQC (https://www. bioinformatics. babraham. ac. uk/projects/fastqc/) to ensure that the raw data were of high quality. For each subject, the two FASTQ files generated were then mapped onto the human reference genome (Ensembl GRCh37 release 75) with STAR v. 2. 7. 3a, in the two-pass mode. 173 The mapping quality of each BAM file was then evaluated with RSeQC. 174 Reads were quantified to generate gene-level feature counts from the read mapping, with HTSeq-count v. 0. 11. 2. 175 We normalized the datasets with the functions DGEList and calcNormFactors from the edgeR version 3.
26. 8 package  176  implemented in R v. 3. 6. 3. We retained only genes with at least 1 count-per-million (CPM) in at least two samples. We considered a gene to be differentially expressed between two sets of conditions if the log 2 -fold-change between the two sets of conditions was greater than 1 (absolute value) and the adjusted p value was below 0. 05, according to the calculations made with the DE-Seq function of the DESeq2 package version 1. 24. 0 167 implemented in R. Differential gene expression data were plotted on heatmaps with heatmap. 2 implemented in R. We used the findMotifs. pl script from HOMER v4. 11  177  with the human v. 6. 3 database and the parameter -len 12. Generation of THP1 cells with knockouts of IFN-gR1 or STAT1 THP1 KO cells were prepared as previously described.
The cells were then treated with trypsin, and fixed by incubation with 4% paraformaldehyde (#30525-89-4, Santa Cruz) for 20 min at room temperature. Cells were acquired on a Beckman Coulter Gallios flow cytometer and analyzed with FlowJo Software. The infection of MDMs (differentiation with M-CSF and IL-4 (#216-MC-010 and #204-IL-010, respectively, R&D Systems)) with M. abscessus-tdTomato  212  and intracellular CFU determination were performed as previously described. 213 Infected MDMs were acquired on a Beckton Dickinson Fortessa flow cytometer and analyzed with FlowJo Software.
Two additional control PBMC scRNA-seq datasets were obtained from another study that we performed. 214 For the analysis, we included scRNA-seq for P1. 1 and 2 controls, and CITE-seq for 3 controls and the P1. 2 and P2 samples. The analysis was performed as previously described. 214 Briefly, sequence read quality was assessed with BVAtools (https://bitbucket. org/mugqic/bvatools). Cell Ranger (v3. 0. 1 for scRNA and v6. 0. 1 for CITE-seq) was used to map reads to the hg38 human reference genome assembly, to perform filtering, and to count barcodes and unique molecular indices (UMIs). Genes that were not expressed in any of the datasets were discarded. Cells with >20% mitochondrial genes were excluded. We filtered out low-quality cells and doublets, by excluding cells falling outside the [-1SD; +2.
5SD] interval from the UMI and gene count distributions. We identified and removed any remaining doublets by manually removing cells that co-expressed markers for different cell types (CD14, CD79A, TRBC1, HBA2 and LILRA4). We then used DoubletFinder package v2. 0. 3 to identify and filter out any remaining cell doublets. 169 Once dead cells and doublets had been removed, the patient and control samples were analyzed together with the Seurat v4. 0. 2 R package  170  and cell clustering was performed by the Uni-form Mani-fold Approximation and Projection dimension reduction method  168  applied to the most variable genes, but excluding mitochondrial and ribosomal protein genes, together with sex-related genes as both patients were female. Based on the clustering pattern obtained, we performed subclustering for the large immune cell lineages separately (myeloid/ pDC, T/NK, and B cells) to identify any remaining doublets.
Every significant differentially expressed gene (absolute FC R1. 5 and adj-p value %0. 001) was classified as down-or upregulated, extracted and used for downstream analysis. All the differential expression results are provided in Table  S6. Enrichment analyses for GO biological processes, KEGG and Reactome pathways and Transfac motifs were performed separately on up-and downregulated differentially expressed genes and for each cell subpopulation, with the gProfiler R package and the g:SCS threshold method 172 ; all the enrichment results are provided in Table  S6. We evaluated the effects on IRF1-binding genes in the cells of the patients by estimating module scores with Seurat's AddModuleScore function, using the default settings. Module score was determined for each cell by calculating the mean transcriptional activity of the genes from the module and then subtracting a mean expression value for a set of control genes.
The scRNA-seq and CITE-seq datasets have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE216489. Generation of single-cell suspensions from murine blood and tissues for flow cytometry All mice were kept in specific pathogen-free conditions and handled according to the guidelines and regulations of the Canadian Council on Animal Care. Experimental protocols were approved by the McGill University Institutional Animal Care Committee (protocol number 2018-8014). Groups of male C57BL/6 (B6) and Irf1 À/À mice (originally from The Jackson Laboratory, reared in-house) were killed, and their blood was collected by cardiac puncture and transferred to heparin-containing tubes (Sarstedt). Blood was diluted 1:5 in PBS, centrifuged in a benchtop centrifuge and the red blood cells in the pellet were lysed with Red Blood Cell Lysing Buffer Hybri-Max (Sigma-Aldrich) according to the manufacturer's instructions.
Flow cytometry on mouse cells Pelleted cells were resuspended in 2. 4G2 hybridoma supernatant diluted in FACS buffer and the resulting suspensions were incubated for 15 min on ice to block Fcg receptors. Surface markers were then stained by incubation with the corresponding fluorochrome-labeled antibody dilutions in FACS buffer for 30 min on ice. Dead cells were excluded by staining with Fixable Viability Dye eFluor 780 (Thermo Fisher Scientific) according to the manufacturer's instructions. Intracellular staining was performed with the FoxP3/Transcription factor staining kit (Thermo Fisher Scientific), in accordance with the manufacturer's protocol. Stained cell suspensions were acquired on a BD LSRFortessa Cell Analyzer (BD Biosciences) and data were analyzed with FlowJo v. 10 software (BD Biosciences).
QUANTIFICATION AND STATISTICAL ANALYSIS Unless otherwise indicated in the figure legends, the statistics provided correspond to independent experiments. Mann-Whitney or Student's t tests were performed. In the relevant figures, n. s. indicates not significant, p > 0. 05; ****p < 0. 0001; ***p < 0. 001; **p < 0. 01; and *p < 0. 05. Analyses were performed with GraphPad software. (F and G) Data representative of two independent experiments are shown. Heatmaps of genes differentially expressed (log 2 FC) in primary fibroblasts from controls or patients, after (F) 2 or (G) 8 h of stimulation with 10 3 IU/mL IFN-g. We show the genes differentially expressed in patients relative to the control group at both time points (2 or 8 h), for the genes differentially expressed at both time points relative to non-stimulated fibroblasts in the control group, i.
A) Cells involved in the production of and response to IFN-g. Proteins for which a mutation of the corresponding gene has been recognized to cause isolated MSMD are depicted in blue, those responsible for syndromic MSMD are shown in red, and those that can cause either isolated MSMD or syndromic MSMD are depicted with crossed lines. (B) Relative clinical severity of isolated MSMD forms vs. relative residual IFN-g activity. (C) Principal component analysis of WES data from the two patients and our in-house WES database. (D) Consensus negative selection (CoNeS) of IRF1. (E) Diagram of the various IRF1 cDNA constructs used for transient overexpression experiments. (F) RT-qPCR on HEK293T cells without (NT) and with transfection with empty vector (EV) or various IRF1 cDNAs. The bars represent the mean.