dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors would like to thank  Dr. Luis L. Fonseca  for insightful comments and discussions about the present work. This work was in part supported by a center Grant ( UID/MULTI/04046/2013 ) from  FCT , Portugal, awarded to  BioISI. DO  was a recipient of a PhD fellowship from the BioSys PhD programme (Ref:  SFRH/BD/52486/2014 ), from  FCT , Portugal. This work was supported in part by Grants  MCB-1517588  (PI: EOV) and MCB-1615373 (PI:  D. Downs ) of the  U. S. National Science Foundation. The funding agencies are not responsible for the content of this article.
Sequencing and phylogenetic analyses The PCR products of the expected length were purified from the agarose gel using the QIAquick® Gel Extraction Kit (Qiagen®, Germany), following the manufacturer's instructions. The purified amplicons were subjected to nucleotide sequencing using the same primers as in RT-PCR by the GATC Biotech Platform (Germany). Consensus DNA sequences were constructed from forward and reverse sequences in a DNA Baser v4. 12. 0  [20]. Nucleotide (nt) and aa sequence alignments were carried out using ClustalW in BioEdit Sequence Alignment Editor v7. 2. 5. The Blast software programs (https://blast. ncbi. nlm. nih. gov/Blast. cgi) were used to determine the sequence similarity of the Algerians strains. Phylogenetic trees were constructed by Maximum likelihood method in a MEGA X 10.
1. 8 program  [21] , using the General Time Reversible (GTR) nucleotide substitution model; one thousand bootstrapping replicates being used to estimate the robustness of the tree branches. The nucleotides sequences of all studied strains are available in GenBank database, under accession numbers summarized in Table  1. Phylogenic characterization of the studied Algerians strains was performed for all their AIV gene segments, in comparison with selected AIV H9N2 strains from Genbank. For amino acid analyses, selected sequences were subjected to ClustalW multiple sequence alignment  [22]. The analysis was performed for different molecular markers of host viral tropism, pathogenicity and virulence, as previously described for LPAIV H9N2 and other subtypes.
Phylogenetic analysis of internal genes As for surface genes, all internal genes (PB2, PB1, PA, NP, M and NS) of the studied H9N2 isolates were compared to their counterparts from GenBank to establish phylogenetic trees and genetic similarity matrixes at the nucleotide level. Here again, the internal genes of the Algerian strains clustered within the G1 sub-lineage, and were closely related to each other, as shown both by their high nucleotide identities (99. 30-99. 96%, 99. 43-100%, 99. 30-100%, 90. 22%-100, 98. 99 -99. 90%, and 99. 29 -100% for PB2, PB1, PA, NP, M, and NS gene segments, respectively) and the bootstrap values (Fig. 1 ). No reassortment event was observed. The strain A/Chicken/ 6BDD/Algeria/2017 remained the most distinct strain. In addition, the studied strains showed high nucleotide sequence identity with those isolated during the same year (2017), with more than 98, 98 to 99%, 98 to 99%, more than 99, 98, and 99% identity for PB2, PB1, PA, NP, M, and NS, respectively.
At the same positions, different substitutions were previously described, such as 190 T/V/A (H3 numbering)  [42]. Therefore, 198 T may allow binding of the Algerian strains to α [2-6] human-like receptors, but with lower affinity as compared to residues A and V at the same position, 198 V characteristic of HPAIV, conferring the most affinity binding towards human receptors  [41]. Considering the combination of multiple substitutions in the RBS, the Algerian strains may have higher binding affinity to human-like receptors. The Blast analysis (NCBI) of the HA protein showed an exclusive mutation T197A for our strains in comparison with H9N2 subtype viruses published in GenBank; the strains isolated the same year in the Center of Algeria, did not harbor this mutation. Interestingly, this mutation was conserved in all human H1N1 strains of 2014-2015, in India  [43]  and in Italian 2009 strains  [44].
Extensive Innate Response and Interferon Gamma Activation in COVID-19 Lung Biopsies Transcriptome data of human lung biopsy samples from the dataset GSE147507  (Blanco-Melo et al. , 2020)  were subjected to gene set enrichment analysis using the ''hallmarks'' gene set from the MSigDB database. As compared with healthy donor samples, the majority of samples from COVID-19 lungs were significantly enriched in genes associated with innate immunity, such as the interferon alpha response (with a normalized enrichment score [NES] of +7. 72, p value<0. 001; Figure  S1A ), TNFA signaling via NF-kB (NES of +4. 88, p value<0. 001; Figure  1 ), complement cascade (NES of +3. 51, p value<0. 001; Figure  S1A ), and reactive oxygen species pathway (NES of +2. 78, p value<0. 001; Figure  S1A ). Likewise, these tissues were also enriched in genes linked with IL-6/Jak signaling, which plays a role in both innate and adaptive immunity (NES = +2.
A large part of the network was organized around aspects of the innate response such as interferon alpha, TNFA, the complement cascade, and reactive oxygen species (ROS). The majority of the immune network (Figure  1A ) was composed of connections between enzymes, followed by signaling molecules, receptors, and transcription factors. We detected expression changes for a few ligands including chemokines and interleukins (Figure  1A ), which were mainly induced by the IFNa and IFNg response. To validate these results, we obtained single-cell transcriptome data from the bronchoalveolar lavage fluid of healthy donors (n = 6, green) and patients with mild COVID-19 (n = 3, blue) or severe COVID-19 (n = 3, red) from the dataset GSE145926. These data were merged together for the purpose of UMAP dimensionality reduction (Figure  1B )  (Liao et al.
These results suggest a major role for the innate immune response in the lungs of patients with COVID-19, which intensifies with disease severity. and S4B ), which could affect major functionalities of the cells in this organ. These results were consistent with those of a gene set enrichment analysis (Figure  S5 ). Specifically, the gene set enrichment analysis (performed using the ''hallmarks'' gene set of the MsigDB database) highlighted repression of the mitosis spindle and p53 pathway (cell cycle gatekeeper) in samples of COVID-19 lungs compared with those of healthy donors (NES = À3. 45 and À2. 77, respectively, with p value<0. 001, Figure  S5A ). We also detected the repression of signaling pathways that are implicated in stem cell functionality, such as the NOTCH pathway (NES = À1. 70, p value <0.
At the single-cell level, CD68 macrophage M1 marker was confirmed to be increased in bronchoalveolar lavage fluid of mild COVID-19 as compared with healthy donor samples but it was not case in severe COVID-19. In contrast, expression of CD163 macrophage M2 marker seems to be constant between different groups of samples (Figure  S6 ). In order to characterize the immune infiltration of COVID-19 lungs, transcriptome data from lung biopsies were cross-integrated with transcriptome data from human hematopoietic and non-hematopoietic tissues (GSE76340)  (Pont et al. , 2016) , which were taken to be representative of the micro-environment of the lung samples. The resulting cross-matrix comprised 170 whole transcriptome analyses and was submitted to the cross-batch normalization algorithm Combat to correct batch error (Figure  S7  and Table  S3 ).
Hematopoietic stem cells, B cells, T cells, and dendritic cells were found in intermediary positions between healthy donor and COVID-19 lung samples, which suggested that these cells played only a minor role in the immune infiltration (Figure  S4E ). We also confirmed that the microarray batches coming from different datasets used in this analysis did not influence the results, as they were found to be well distributed across the first principal axis (Figure  S8 ).
Deregulation of PPARg Interactome in COVID-19 Bronchoalveolar Lavage Fluid To further investigate the dysregulation of the PPARg-dependent macrophage program in the lungs of patients with COVID-19, we constructed an interactome of the PPARg network and examined the changes in expression among groups. To do this, PPARg interactions were collected from the innateDB database of immune network interactions  (Breuer et al. , 2013)  (Figure  7A ). Of the gene partners associated with PPARg, we found evidence in the single-cell transcriptomes (bronchoalveolar lavage fluid) for changes in expression associated with COVID-19 infection (Figure  7B ), specifically in SUMO1, NR3C1, KLF6, RXRA, CEBPD, and PLAGL1. At the single-cell level, a strong correlation was found between PPARg and SUMO1 (r = 0. 47, Figure  7C ), PPARg and NR3C1 (r = 0.
44, Figure  7C ), and PPARg and RXRA (r = 0. 35, Figure  7C ). A weaker correlation was detected between PPARg and KLF6 (r = 0. 13, Figure  7D ) in patients with COVID-19, and indeed, we did observe a strong activation of KLF6 associated with the severe cases of the disease (Figure  7E ) in all lympho-myeloid cell compartments. We constructed another PPARg interactome using the IntAct protein database (Figure  7F ) and, again, found evidence of relationships between PPARg and some of its partners at the single-cell level in the bronchoalveolar lavage fluid of patients with COVID-19 (Figure  7G ). For example, PPARg expression was strongly correlated with that of EP300 (r = 0. 29, Figure  7H ), CCDC88A (r = 0. 60, Figure  7H ), and RXRA (r = 0. 45, Figure  7H ) in COVID-19 cases. The expression of OPTN did not demonstrate a strong correlation to PPARg (r = 0.
Co-repression of PPARg-NR3C1-RXRA Cistrome during COVID-19 Disease in the Lungs We retrieved data from a PPARg ChIP-sequencing (ChIP-seq) experiment performed on the THP-1 monocyte cell line (Gene Expression Omnibus [GEO] sample GSM624141)  (Pott et al. , 2012)  from the Cistrome project database and processed the dataset using version HG38 of the human genome. Promoter mapping was performed with the BETA cistrome application +/À 100 kb around transcription starting sites  (Wang et al. , 2013). This analysis confirmed the presence of a well-conserved signal in the mammalian promoter database (Figure  8A ). When we integrated the PPARg ChIP-seq data with the PPARg-repressed signature found in the lungs of patients with COVID-19 (Table  S2 ), we were able to identify a genomic program with a landscape of both distal and proximal promoters (Figure  8B  and Table  S4 ).
To confirm the relationships between the PPARg-NR3C1-RXRA cistrome and the PPARg repression signature of the COVID-19 transcriptomes, CHIP-seq data obtained from the THP-1 monocyte cell line were retrieved for both NR3C1 (GEO sample GSM2661793)  (Rollins et al. , 2017)  and RXRA (GEO sample GSM624142)  (Pott et al. , 2012)  from the Cistrome project database. We also collected THP-1 CHIP-seq data for POLR2A (GEO sample GSM1905827)  (Yu et al. , 2015)  to highlight transcriptional activity in this program. Using the ''deeptools'' pipeline  (Ramı ´rez et al. , 2014) , all of these CHIP-seq experiments were integrated with the PPARgrepressed signature from COVID-19 lungs in order to identify common promoter signals +/À 5 kb around transcription starting sites. As shown in Figure  8C , the proximal PPARg-repressed epigenetic program in monocytes shared its normal transcriptionally active promoters with RXRA and NR3C1.
To characterize the active promoters in monocytes, we retrieved THP-1 CHIP-seq experiments for histone H3 lysine 27 acetylation (GEO sample GSM2544237)  (Phanstiel et al. , 2017)  from the Cistrome project database. From the PPARg-repressed program (Table  S4  and Figure  8B ), RGS12 was confirmed to present a PPARg signal in its second promoter; its activity was demonstrated with the H3K27Ac histone mark and binding of RNA polymerase II (Figure  8D ), suggesting that this target belongs to the PPARg-repressed program during COVID-19 in the lungs. At the single-cell level, RGS12 was confirmed to be repressed in the CD14-CD16 cell compartment in COVID-19 lungs, especially in the severe form of the disease (Figure  8E ).
SUMO1, from the COVID-19 PPARg-Repressed Complex, Interacts with Nucleoprotein in Human SARS Virus In order to investigate the interactions of the PPARg complex with the human SARS virus, we explored the VirusHostNet 2. 0 database  (Guirimand et al. , 2015)  using the deregulated partners of PPARg that we identified (Table  S5 ). By integrating these interactions with the PPARg network, we were able to create a virushost network (Figure  8F ). Using this, we determined that, within the PPARg-repressed complex, only SUMO1 is capable of interacting with a SARS virus, through the nucleoprotein NCAP_CVHSA of the human coronavirus. This result suggests that, during lung infection, repression of the PPARg complex may be mediated via the interaction of SUMO1 with the virus and that sumoylation function may also be affected.
ACKNOWLEDGMENTS We thank the association '' Vaincre le cancer NRB '' (https://www. vaincrelecancer-nrb. org/), which made it possible to finance the computer equipment used to carry out these analyses. This work was performed with grants from the  ANR ''Programme d'Investissements d'Avenir' ' of the  INGESTEM National Infrastructure  ( ANR-11-INBS-0009-INGESTEM ) and  INSERM, University Paris Sud.
Data and Code Availability We obtained transcriptomes from COVID-19 lung biopsies from dataset GSE147507 in the Gene Expression Omnibus database (https://www. biorxiv. org/content/10. 1101/2020. 03. 24. 004655v1) and now publish in Cell on 28th May 2020 (https://www. sciencedirect. com/science/article/pii/S009286742030489X?via% 3Dihub) (Blanco-Melo et al. , 2020). ''Volcanoplot'' software was developed to analyze the significance of immune scores; the corresponding R functions are available at the web address: https://github. com/ cdesterke/volacanoimmune. Chip2heat. sh software was developed in SHELL BASH (Bourne-again SHELL) and implemented with the deeptools pipeline, specifically computeMatrix and plotHeatmap, with the promoter plot saved in different formats: PNG, PDF, SVG. This software can be downloaded at https://github.
com/cdesterke/chip2heat. The R code for the single-cell analyses for marker validation is detailed in the Transparent Methods section of the Supplemental Information and also provided at the following address: https://github. com/cdesterke/covid19sc. Additional resources with interactive Web interface of the single cell analysis: to facilitate data exploration of disturb CD14+/CD16+ double population in bronchoalveolar lavage fluid of patients with severe COVID-19, we developed an interactive web interface available at https://cdesterke. shinyapps. io/COVID19LUNG/. This website was built with flexdasboard and shiny application inclusion and with graphical interactivity display by R plotly. This data dashboard allows exploring expression of biomarkers found on PPARg trajectory in lung monocyte-macrophage disrupted cell population in severe COVID-19.
The user needs to select gene ID on the left sidebar, and the application will display expression of this selected marker with interactivity on TSNE graph, number of positive cells for this marker will be displayed in the value box at the top right of the dashboard, also expression by group will be displayed on violinplot, finally statistical summary (mean and standard deviation, SD) will be displayed by group of samples. Mendeley Dataset associated to this manuscript: 10. 17632/3xnypzvcf7. 1.
SUPPLEMENTAL INFORMATION Supplemental Information can be found online at https://doi. org/10. 1016/j. isci. 2020. 101611. Signal-promoter plot of PPARg CHIP-seq data from THP-1 integrated with the repression signature found in COVID-19 lung transcriptomes (size of the circle is proportional to the negative logRatio found in the COVID-19 lung transcriptome, mapping +/À 100 kb around transcription starting sites  [TSS] ); (C) Promoter heatmap for PPARg-RXRA-NR3C1 cistrome and POLR2A from CHIP-seq data from THP-1 adapted to the repression signature found in the COVID-19 lung transcriptome (mapping +/À 5 kb around TSS); (D) PPARg CHIP-seq signal found in the promoter of RGS12, mapped with POLR2A and Histone H3 K27 acetylation in THP-1 cells; (E) UMAP representation of single-cell transcriptome (bronchoalveolar lavage fluid) expression of RGS12 in HDs or patients with mild or severe COVID-19; (F) Human-SARS protein-protein interactome network, with integration of the PPARg complex affected in COVID-19 lung infection (edges represent connections found in VirusHostNet database).
Transcriptomes from public datasets We obtained transcriptomes from COVID-19 lung biopsies from dataset GSE147507 in the Gene Expression Omnibus database (https://www. biorxiv. org/content/10. 1101/2020. 03. 24. 004655v1) and now published in Cell on 28th May 2020 (https://www. sciencedirect. com/science/article/pii/S009286742030489X?via%3Dihub)  (Blanco-Melo et al. , 2020, p. ). This dataset comprised human biological samples: lung biopsies from one male (age 72) and one female (age 60), used as biological replicates. Additionally, lung samples from a single male (COVID-19 deceased, age 74) patient were processed in technical replicates. These experiments had been performed with the approval of the institutional review board at the Icahn School of Medicine at Mount Sinai under protocol HS#12-00145.
Samples were sequenced on NextSeq 500 technology (Illumina , CA) after the selection of a polyA RNA library using the TruSeq RNA Library Prep Kit v2 (Illumina, CA); the library was prepared from total RNA extracted using the RNeasy Mini Kit (Qiagen). Raw sequencing reads were aligned to the human genome (hg19) using the RNA-Seq Alignment App (v2. 0. 1). We then used the matrix of raw count data to perform the bioinformatics analyses described below. The GSE150316 dataset had been prepared from distinct tissues from 5 COVID-19 patients and 5 healthy donors via RNAseq sequencing; samples from COVID-19 patients were processed in triplicate. Sequencing was performed on an Illumina NextSeq500 instrument after library preparation with the Smarter Stranded Total RNA-Seq kit v2 (634413, Illumina) starting from 10 ng of RNA (FFPE slides) extracted with the FormaPure Total nucleic acid extraction kit (C16675, Beckman Coulter).
Transcriptome of human tissues with immune cell sub-populations Normalized transcriptomes from different human tissues were retrieved from the dataset GSE76340 on the GEO website. Samples in this dataset had been processed with different versions of the HumanHT-12 beadchip (Illumina, CA): versions V3 & V4 were annotated with the transcriptome platforms gpl6947 & gpl10558. These experiments comprised 166 human samples which were representative of hematopoietic and non-hematopoietic tissues present in the human body and were compatible with immune deconvolution analysis  (Pont et al. , 2016).
Bioinformatics analyses Bioinformatics analyses were performed in R software environment version 3. 5. 3. Functional network analysis was carried out using an immune gene-set enrichment analysis with the standalone software Cytoscape version 3. 4. 0. Raw counts of next-generation sequencing data were normalized with the algorithm 'variance stabilization transformation' (VST) from EdgeR. The cross-matrix between datasets, which comprised 170 transcriptome samples (Supplemental Table  3 ), was constructed by merging experiment sets based on their unique gene symbol identifiers. Cross-batch normalization was applied to the resulting matrix with the algorithm 'Combat' of the SVA R-package. Unsupervised principal component analyses were performed with FactoMineR. Transcriptome expression heatmaps were created with the Made4 and pheatmap R-packages, with default Pearson distances for small heatmaps and Euclidean distances for large ones.
Single-cell analysis of lung samples from healthy donors and patients with mild and severe COVID-19 In order to validate, at the single-cell level, the disruption observed in immune molecules in the transcriptome analysis of lung cells from COVID-19 patients, single-cell transcriptome (10x Genomics) data from bronchoalveolar lavage fluid samples were downloaded in H5 format from the dataset GSE145926. From this dataset, we created a merged matrix by aggregating a total of 90696 transcriptomes. This included six healthy donor samples-GSM4475048, GSM4475049, GSM4475050, GSM4475051, GSM4475052, GSM4475053-comprising 39900 transcriptomes, three mild COVID-19 samples-GSM4339769, GSM4339770, GSM4339772comprising 9710 transcriptomes, and three severe COVID-19 samples-GSM4339771, GSM4339773, GSM4339774-comprising 41086 transcriptomes.
Polymorphism data were generated by mapping transcriptome reads of each individual to the corresponding reference genome. For O. nova , a multi-dimensional scaling plot (MDS; based on raw Hamming distances) revealed the presence of at least two clusters (hereafter referred to as divergent lineages), grouping individuals from different sampling locations ( Figure  2a  ). For O. subpectinata , individuals were separated into three distinct clusters, each corresponding to one location ( Figure  2b  ). Accordingly, between-location variation contributed much less to the species-wide genetic variation in O. nova (12. 0%) than in O. subpectinata (56. 4%). In O. nova most variation (90. 8%) was explained by variation within individuals. Figure  2 : Genetic divergence is more extensive within individuals than between populations for the asexual Oppiella nova (a) , in contrast to the sexual Oppiella subpectinata (b).
III) The deepest split in many haplotype phylogenies separates haplotypes. A classical signature of haplotype divergence under asexuality is the full separation of haplotypes A and B at the deepest split of a haplotype tree. By contrast, haplotypes are generally expected to diverge according to population divergence in sexual organisms ( Figure  1  ). To confirm that the phased regions represent allele-haplotypes and not merged paralogs, we verified that the genomic read coverage of phaseable regions was not exceeding the coverage of single-copy genes from the BUSCO arthropod database (merged paralogs should have an at least 2x higher coverage, see Methods ). This was indeed the case as single-copy BUSCO genes, phased regions and the scaffolds from which phased regions derived showed a median coverage of 126x, 101x, and 87x, respectively.
82  (Bushnell, 2014). These normalized overlap read libraries were assembled into contigs using SPAdes v3. 10. 1, a multi k-mer assembler  (Bankevich et al. , 2012)  , with options -m 400 --careful -k  21, 33, 55, 77, 99, 111, 127. The resulting contigs were ordered into scaffolds using the 350 bp, 500 bp and 3000 bp read libraries using SSPACE v3. 0  (Boetzer et al. , 2011)  with default parameters. To close gaps emerging during scaffolding, GapCloser v1. 12  (Luo et al. , 2012)  with option -l 125 was run. For details see https://github. com/AsexGenomeEvol/HD_Oppiella: assembly and mites. Scaffolds that were likely from contaminants (e. g. , bacteria, fungi) were removed by first annotating and visualizing contaminations using BlobTools v1. 0  (Laetsch & Blaxter, 2017)  , followed by custom filtering.