dataesr/scientific-paragraphs-categorization huggingface dataset:
This identified three IAV PPI factors that regulated SARS-CoV-2 infection: COPB1, AHNAK and RUVBL2 (Fig. 5F , Supplementary Data 4). While we report these three proteins as IAV  A Schematic representation of sample collection and data analysis for identifying genes with pLOF variants associated with severe influenza disease from an influenza patient cohort. Genes with pLOF variants plotted against the false discovery rate (-log10(FDR)) from the severe-disease association test for (B) each of the AB, PH and PPI datasets or (D) the average FDR across the three AB, PH, and PPI datasets (purple and green cir-cles=genes with significant pLOF variants (FDR < 0. 1); grey=genes with pLOF variants below threshold (FDR > 0. 1); large circles=genes with significant protein AB or PH changes (adjusted p-value < 0.
05; two-sided t-test); small circles=genes detected in AB or PH proteomic datasets with no significant changes). C Venn diagram of the overlap of proteomic datasets with significant changes in AB (top, left) or PH (bottom, left), and significant genes with pLOF variants (corresponds to the total number of purple circles in B). Heatmap of log2FC in infection vs mock (log2FC (IAV/Mock)) from NHBE and THP-1 cells at 18 hours (pH1N1, H3N2) and 12 hours (H5N1) post-IAV infection (reported in Supplementary Data 2) for (E) AB of 23 significantly changing proteins, and (F) PH of 52 changing phosphorylated proteins, that have significant pLOF variants (from the union in C) (red=increase, blue=decrease, grey=not detected; red box with black circle=only detected in IAV infection, blue box with black circle=only detected in mock infection; black box outline=significant change (adjusted p-value < 0.
AHNAK was profiled as an RNA binding protein whose RNA binding kinetics peak early in SARS-CoV-2 infection  86. No siRNA knockdowns from the PH dataset passed our log2 fold change thresholding criteria for SARS-CoV-2 (Supplementary Fig. 6B ), however, 7 out of 10 PH targets mildly decreased SARS-CoV-2 infection with significance (p-value < 0. 02) (Supplementary Data 4). Collectively, we classified 54 regulators of IAV infection, three of which act as pan-respiratory virus regulators of IAV and SARS-CoV-2 infection (Fig. 5G ).
Cells were pelleted and incubated with 100 μL 1:1000 mouse anti-Influenza A nucleoprotein [HT103] antibody (Kerafast, EMS010) in block and permeabilization buffer for 1 hour at room temperature. Cells were pelleted, washed once with 200 μL wash buffer (1% BSA in 1X PBS) and incubated in 100 μL 1:1000 goat antimouse IgG (H + L) Alexa Fluor Plus 488 (Fisher, A32723) in block and permeabilization buffer for 1 hour at room temperature in the dark. Cells were washed twice with 200 μL wash buffer and fixed in 150 μL 1% formaldehyde in 1X PBS. Samples were run in 96-well format on an Attune NxT flow cytometer (Thermo Fisher) with accompanying Attune NxT Software (version 3. 2. 0). Voltages were set at: (1) forward scatter 80 and side scatter voltage at 275 (R1, A549 singlet cells); and (2) forward scatter 120 and side scatter 280 (NHBE cells).
In both experiments, Alexa Fluor Plus 488 signal was quantified by blue laser 1 at voltage 205 (percent NP + ). 100 μL of cells were acquisitioned and all events recorded at 1000 μL/min. Final cell gating and quantification of %NP+ cells (percent IAV infectivity) was performed with FlowJo software (version 10. 7. 1). %NP+ cells (percent IAV infection) are reported in technical singlet (Supplementary Fig. 5B, D ). IF staining against AHNAK and IAV NEP and microscopy. Fixed, permeabilized cells in 24-well plate format were blocked in 300 μL 1% BSA in 1X PBS for 30 minutes at room temperature with gentle rocking, and then incubated in 300 μL 1:500 anti-AHNAK mouse monoclonal (EM-09) (Thermo Fisher, MA1-10050) and 1:500 anti-influenza A NS2 (NEP) rabbit polyclonal (Thermo Fisher, PA5-32234) primary antibodies diluted in 1% BSA in 1X PBS at 4 °C overnight with gentle rocking.
One static modification was assigned for carbamidomethyl cysteine. LFQ was enabled. Match between runs was enabled with a 1. 5-minute matching time window and 20-minute alignment window. All other MaxQuant settings were left at the default. Peptide ion intensities from the output of MaxQuant were summarized to protein intensities using the R Bioconductor package MSstats (version 3. 19. 4)  69  , specifically the function dataProcess, with default settings except that the noise-filtering 128 was turned on by setting featureSubset = "highQuality" and remove_uninformative_fea-ture_outler = TRUE. For phosphopeptide data, the peptide ion intensities were similarly summarized to a single intensity per unique observed single-peptide combination of phosphorylated sites by relabeling the protein of each feature as the combination of protein name and observed phosphorylated sites.
The Bioconductor package artMS (version 1. 3. 9) (https://doi. org/10. 18129/B9. bioc. artMS) was used for this relabeling. The differences in log2-transformed intensity between infected and mock samples were scored using the MSstats function groupComparison, which fits a single linear model for each protein with a single categorical variable for condition. From these models, MSstats reports pairwise differences in means between conditions as log2 fold change (log2FC) with a p-value based on a t-test assuming equal variance across all conditions, and reports adjusted pvalues using the false discovery rate (FDR) estimated by the Benjamini-Hochberg procedure. One single time point per virus was selected for both cell types based on high viral protein abundance in the abundance data: at 18 hours post-infection for pH1N1 and H3N2, and 12 hours post-infection for H5N1.
Computational analyses methods Gene Ontology (GO) enrichments. For PPI GO enrichments, the human interacting proteins of each IAV protein were collated across all strains and cell types, and tested for enrichment of GO Molecular Function terms. The over-representation analysis was performed using the enrichGO function of clusterProfiler package (version 3. 18. 0) in R with default parameters. GO terms were obtained from the R annotation package org. Hs. eg. db (version 3. 12. 0). Significant GO terms were defined as those with p-value < 0. 002. Terms with overlapping genes in each set were compared and the most significant term (lowest p-value) with the largest gene set size was selected as the non-redundant term. PPI enrichments were subject to further manual curation, with a maximum of the top three significant non-redundant GO terms listed and visualized for each IAV protein (Supplementary Data 1).
For PH GO enrichments, proteins with significantly up-and downregulated phosphorylation events (defined as adjusted p-value < 0. 05, absolute(log2FC) > 1, and observed in infected and mock samples) of each virus strain were collated at 18 hours post-infection (pH1N1, H3N2) and at 12 hours post-infection (H5N1) across all cell types, and tested for enrichment of GO terms from among all three ontologies: Biological Process, Molecular Function and Cellular Component. The over-representation analysis was performed using the enricher function of clusterProfiler package (version 3. 12. 0) in R with default parameters. GO terms were obtained from the R annotation package org. Hs. eg. db (version 3. 12. 0). Significant terms were defined as those with adjusted p-value < 0. 05. We selected a set of non-redundant terms following an automated clustering procedure.
We first constructed a term tree based on distances (1-Jaccard Similarity Coefficients of shared genes in KEGG or GO) between the significant terms. The term tree was cut at a specific level (h = 0. 99) to identify clusters of nonredundant gene sets. For results with multiple significant terms belonging to the same cluster, we selected the most significant term (i. e. lowest adjusted p-value) (Supplementary Data 2). Network visualizations. All networks were generated and visualized in Cytoscape (version 3. 8. 2) 129. For the IAV-human PPI network, IAVhuman PPIs were represented by strain and collated across all cell types. In cases where one human protein is shared between two virus proteins, the maximum MiST score from either IAV protein in any cell type was reported for each IAV strain. Human-human PPIs were annotated as reported in the comprehensive resource of mammalian protein complexes (CORUM) database  130.
Manual annotations to the network include human-human PPI protein complex name and biological process. Briefly, human prey proteins for each IAV protein were subjected to gene set enrichment analysis using either GO Biological Process terms or CORUM protein complex annotations. Genes that were members of enriched biological processes or protein complexes were labeled in the network using Adobe Illustrator software (v24. 1). Labels were manually curated to simplify and generalize terms to facilitate interpretability. Genes mapped under a GO Biological Process term were manually investigated to ensure the term represented each gene's canonical function; genes that clearly possessed multiple functions, or genes that were otherwise difficult to classify, were excluded to reduce the appearance of misleading annotations.
For the PPI-phosphorylation overlay network, proteins were selected and visualized if they were identified in the PPI data above MiST scoring thresholds and in the phosphorylation data at the restricted time points (18 hours post-infection pH1N1, H3N2; 12 hours post-infection H5N1) with an absolute(log2FC) > 1 and adjusted p-value < 0. 05 in any cell type. If a site was detected across multiple cell lines, the maximum absolute value, non-infinite fold-change was used. Kinase enrichment analysis. Kinase activity predictions were generated on the full, non-infinite unfiltered log2FC phosphorylation data (i. e. no thresholds) using the ProtMapper resources available in OmniPath  71. ProtMapper is a comprehensive catalog of kinasesubstrate relationships that includes six databases (PhosphoSitePlus, SIGNOR, HPRD, NCI-PID, Reactome and the BEL Large Corpus) and three text-mining tools (REACH, Sparser and RLIMS-P).
Kinase activity predictions were reported as a Z score that was calculated using the mean log2FC of phosphorylated substrates for each kinase in terms of standard error (Z = [M -u] / SE), comparing fold changes in phosphorylation site measurements of the known substrates against the overall distribution of fold changes across the sample. A p-value was also calculated by this approach using a two-tailed Z-test method, and thresholds were set at a FDR (by Benjamini-Hochberg method) <0. 05. This approach has been previously shown to perform well at estimating kinase activities  19, 79, 131, 132. In our study, this approach produced annotations for 314 kinases with available data across all collected time points, virus strains and cell types (Supplementary Data 2). Highconfidence kinase activity annotations were generated by: (1) limiting analysis to 18 hours post-infection (pH1N1, H3N2) and 12 hours postinfection (H5N1) in NHBE and THP-1 cell types, and (2) requiring each kinase possess two known phosphorylation sites detected in the PH dataset.
We assessed the libraries for quality with a high sensitivity DNA ScreenTape assay on the 2200 TapeStation System (Agilent) and quantity with KAPA Library Quantification Kits for Illumina platforms (Kapa Biosystems). The libraries were diluted to 2 nM and clustered using an Illumina cBot with a HiSeq 3000/4000 paired-end cluster kit on a patterned flow cell and a HiSeq 3000/4000 SBS kit (300 cycles, Illumina v2. 5 reagents) on the HiSeq 4000 sequencing platform. Data processing. Each individual's WES data were mapped to the human reference genome (build hg19) using the Burrows-Wheeler Aligner (v0. 7) 133. After marking duplicates using Picard (http:// broadinstitute. github. io/picard/), the Genome Analysis Toolkit (GATK, v3. 1) 134 was used to remove duplicates, perform local realignment, and map quality score recalibration to produce a BAM file.
Single nucleotide polymorphism (SNP) calls were made by the Haplotype-Caller (v3. 4), filtering poor calls by the Variant Quality Score Recalibration (VQSR) filter from GATK. We sorted the aligned reads based on genome position using Picard (http://broadinstitute. github. io/picard/) and recalibrated the base quality score using default parameters. Each sample's final gvcf files were streamed to Illumina DRAGEN Bio-IT Platform (v3. 1, Illumina), applying the default parameters in the '--vcenable-gatk-acceleration true' option to identify and remove lowquality variants. We identified 3,621,267 genetic variants from the exome sequencing data after quality control, of which 3,256,844 had MAF < 1%. We performed quality control on our samples based on the number of total variants, number of singletons, missing rate, heterozygote vs homozygote ratio, transversion vs transition ratio, race inconsistency, sample relatedness, and missing phenotype.
6A ) and quantification for data analysis of %Ghost 710+ cells (percent dead cells) and %NP+ cells (percent IAV infectivity) was performed with FlowJo software (version 9. 3. 2). For cell viability, %Ghost710+ cells (percent dead cells) and %Ghost 710-cells (percent alive cells) for each siRNA knockdown are reported as calculated with FlowJo (Supplementary Data 4). For IAV infectivity, singlet cell count and %NP+ cells for each experimental siRNA are reported as calculated with FlowJo (Supplementary Data 4). Singlet cell count was used as a readout for cell viability of A549 cells with siRNA knockdown and IAV infection. Singlet cell count and %NP+ cells for each experimental siRNA was normalized to the mean cell count or %NP+ cells of non-targeting (NT) control siRNA corresponding to each set of 24-well plates transfected, infected, collected and stained concurrently.
Specific primers targeting the N gene (5′-TAATCAGACAAGGAACTGATTA-3′ [forward] and 5′-CGAAGGTGTGACTTCCATG-3′ [reverse]) were used as previously described 141. RT-qPCR was performed under the following cycling conditions in an Applied Biosystems QuantStudio 6 thermocycler: 55 °C for 10 minutes, 95 °C for 1 minute, 40 cycles of 95 °C for 10 seconds, followed by 60 °C for 1 min. The number of viral genomes in the supernatant was calculated by performing a standard curve with RNA derived from a viral stock with a known viral titer, and is expressed as PFU equivalents per mL. These data were then used to compute log2 fold changes for experimental siRNA normalized to replicate-matched non-targeting controls. The log2 fold changes were computed separately for each replicate, and the median and median absolute deviation (MAD) were then calculated for each sample across all its replicates in the screen set (six replicates for PPI and nine replicates for PH).
Antiviral compound screen methods Compound treatment, cytotoxicity and IAV antiviral assays. PPItargeting and kinase-targeting compounds were manually curated by target-specific literature search performed by specialists within our group. IAV PB2-targeting compound Pimodivir (VX-787) was included as a positive control due to its antiviral activity against multiple H1N1-, H3N2-and H5N1-subtype IAV in human cell and mouse models and in patients  [142] [143] [144]. Compounds were purchased from vendors specified in Supplementary Data 5. Drug antiviral assays were performed as compounds and IAV strains were received and available, and each set of compounds was performed alongside the Pimodivir control. For drug antiviral assays, A549 cells were seeded at 8,000 cells per well in DMEM growth media described above in 96-well plates (Falcon, 353072) 24 hours before IAV infection.
Percent infection was quantified as ((Infected cells/Total cells) -Background)*100, and the DMSO control was then set to 100% infection for analysis. Data analysis was performed in GraphPad Prism (version 9. 3. 0), using nonlinear regression fit and fit hill functions to identify IC50, IC90, CC10 and CC50 values (Supplementary Data 5). Selectivity index (SI) for each compound was calculated as CC50/IC50 (Supplementary Data 5), and compounds with a SI > 2 were reported as antiviral.
Acknowledgements This work represents a highly collaborative, multi-institutional research endeavor, and we would like to thank and acknowledge members of the  National Institutes of Health (NIH) National Institute of Allergy and Infectious Diseases (NIAID) U19 FluOMICS  team and the  University of California San Francisco (UCSF) Quantitative Biosciences Institute (QBI) Coronavirus Research Group (QCRG). This research was funded by  NIH NIAID U19 FluOMICS  ( AI106754 ) and  FluOMICS Next Gen  ( AI135972 ) awarded to E. -Y. K. , L. P. , M. L. S. , S. K. C. , R. A. A. , S. M. W. , A. G. -S. and N. J. K. This work was also partially supported by the following awards to A. G. -S. : by the  Center for Research on Influenza Pathogenesis and Transmission (CRIPT) , an  NIAID -funded  Center of Excellence for Influenza Research  and Response ( CEIRR , contract number  75N93021C00014 ); by  NIH NIAID  grant  U19AI142733 ; by the  Department of Defense  grant  W81XWH-20-1-0270 ; and by the  Defense Advanced Research Projects Agency (DARPA)  grant  HR0011-19-2-319-0020.
Data availability PPI, global AB, global PH and targeted (PRM) mass spectrometry data generated in this study have been deposited to the ProteomeXchange Consortium via the PRIDE 145 partner repository with the following dataset identifiers: 1) PPI data: PXD036077; 2) AB and PH data: PXD035900; and 3) PRM data: PXD041663. WES genotype data have been deposited to dbGaP and are available with the following accession: phs003407. v1. p1. Supplementary Data 1-5 provide data for graphs and/or full analyses for proteomic (PPI, AB, PH and PRM) data, pLOF data, siRNA screens and drug screens. IAV protein expression vectors are available from the authors upon request. Source data are provided with this paper.
Code availability package source materials for MiST and MSstats are available through the Krogan Lab Github (https://github. com/kroganlab). Permanent reference versions used in this study are available for MiST (version 1. 0. 1) (https://doi. org/10. 5281/zenodo. 8034496) 146 and our slightly customized version of MSstats (version 3. 99) (https://doi. org/10. 5281/ zenodo. 8035059) 147. All other sources of code for computational analyses were derived from publicly available websites and previous publications, and are cited in the corresponding Methods sections.
SNV calling and quality control The 2b-Rad sequencing data were first demultiplexed using process_radtag in STACKS  (Catchen et al. 2013) , then filtered for the presence of the Bcg1 restriction site and the primer W nucleotides, and trimmed to 36 bp using a custom R script (see trimming. R on GitHub repository). The genetic linkage maps were constructed using the est. map 81 function of the R/qtl package, using Haldane's mapping function 82  (Broman et al. 2003). RIAILs from reciprocal crosses were pooled, 83 and the two intercross-specific maps were constructed for rec-1 84 wild-type and mutant separately (Figure  S4 ). Consensus maps 85 were generated for both rec-1 alleles, separately, without account-86 ing for the type of intercross (Figure  1 ). Note that some genetic 87 distances were estimated indirectly for consensus maps because 88 SNV were not shared between the two types of intercrosses.
Hatchability was calculated as the number of adult progeny relative to the number of embryos counted during the first day of embryo laying. The number of males was measured in the adult progeny of each hermaphrodite. The total progeny of 22,196 from 406 hermaphrodites was scored in 8 independent blocks, each corresponding to a different thaw and/or assay date. Hatchability was measured in a subset of 154 mothers in 6 blocks, for 8,480 embryos counted (Table  S3 ). Fertility, hatchability, and male numbers data was analysed with generalized linear models using the glmer function in R, accounting for the fixed effects of parental strain and the random block effects (lme4 textitR package,  Bates et al. 2015). Contrasts were done with the emmeans function of the emmeans R package  (Lenth 2021).
The R code this figure can be found in our GitHub repository. Dots above the identity lines show that more adults than embryos were counted. There are no differences between the two rec-1 alleles (GLM; P-value EEV1401: 0. 69; P-value EEV1402: 0. 42). However, the EEV1401 genetic background has lower hatchability than the EEV1402 genetic background (GLM: p= 0. 034).
1 and chromosome non-disjunction to explain the genetic diversity 2 found among natural populations of C. elegans. results can be found at our GitHub repository. Supplementary 6 materials include six tables and eight figures. Dillmann, M. -A. Félix, T. Lenormand, F. Mallard, M. Rockman, D. 11 Roze, A. Le Rouzic, and J. Yanowitz for discussion. 12 Funding 13 This work was funded by a Labex Memolife fellowship (ANR-10-14 LABX-54) to TP, a Marie Skłodowska-Curie Actions fellowship 15 (H2020-MSCA-IF-2017-798083) to LN, and a research project grant 16 from the Agence Nationale pour la Recherche (ANR-18-CE02-0017-17 01) to HT. tool for discovery in caenorhabditis nematodes. Genetics. 220. 31 Barber LJ, Youds JL, Ward JD, McIlwraith MJ, O'Neil NJ, Petal-32 corin MI, Martin JS, Collis SJ, Cantor SB, Auclair M et al.
Functional data analysis approaches applicable to morphometrics Functional data analysis (FDA) is a rapidly growing branch of statistics that analyses data in the form of functions, continuous curves or shapes. The study of FDA in shape analysis addresses the issue of registration as shapes formed by curves in 3D landmarks are also functions  (Srivastava and Klassen 2016). To ensure that the functions are well-aligned for geometric features (peaks and valleys), curve registration  (Ramsay and Li 1998; Srivastava et al. 2011)  or functional alignment  (Ramsay 2006 ) are applied to warp the temporal domain of functions  (Guo et al. 2022). Epifanio and Ventura-Campos (2011)  proved that the use of the FDA framework supersedes other approaches such as the landmark-based approach or even the set theory approach on PCA using a well-known database of bone outlines.
1(c) ). These three univariate functional datasets composed a multivariate functional data, with 𝑛 outlines, each yielding a vector of 𝑛 observations defined as a 3 -dimensional functional domain. The MFPCA (Happ-Kurz 2020) package is used to perform the conversion to functional data  (Ramsay and Silverman 2005). The multivariate landmark functional data, which is used to perform principal component analysis, is described in the next section. (b)  (a) (i) (ii) (iii) (v) (iv) (vi) (vii) (viii) (c).
Linear discriminant analysis on functional landmark data The multivariate functional principal component (MFPC) scores from the landmarks are then applied in LDA to distinguish among the categories studied and the results are compared with the PCA of the GM approach. LDA is a dimensionality reduction technique that reduces the number of variables in a dataset by creating a linear combination of the features that best separates the different classes in the data. Functional linear discriminant analysis (FLDA) can be used to classify new curves, estimate the discriminant function between classes, and provide a one-or twodimensional representation of sets of curves. The first three PC scores are used in LDA for GM and FDGM to compare rates of classification to achieve low classification errors despite a major data reduction  (Howley et al.