dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2020). Our data support this, but in addition, we observed that cDC2s also upregulated CCR2 expression with an even greater ES than monocytes. There is evidence for CCR2 being required for DC migration to the inflamed lung, respectively, although this is not the case in the steady-state condition  (Nakano et al. , 2015 (Nakano et al. , , 2017)). Thus, the declining number of cDC2s in the systemic circulation of COVID-19 patients may be a reflection of cDC2 extravasation into the affected lungs. Alongside signatures shared in severe RSs, we also extracted those specific to and unique in COVID-19s. These SARS-CoV-2induced adaptations were restricted to the T and NK cell compartment. Several studies described upregulation of PD-1 and CD38 alongside other activation and exhaustion markers, suggesting a hyperactivated and exhausted T cell compartment  (De Biasi et al.
, 2021). Supportive, elevated circulating GM-CSF + CD4 + T cell levels are predictive of poor outcomes in sepsis patients  (Huang et al. , 2019b). Collectively, this suggests that GM-CSF is an early driver of the underlying immunopathological cascade in COVID-19s, thereby being a promising therapeutic target (NRI, GEM TRIAL, ClinicalTrials. gov identifiers NCT04400929 and NCT04411680;  Bonaventura et al. , 2020; Bosteels et al. , 2020; Lang et al. , 2020; De Luca et al. , 2020). Using NGS-based HLA typing and further integration of this dataset into our single-cell immune profiling analysis, this multi-omics approach provides deep insights into COVID-19 immunopathology and a potential genetic influence: while COVID-19s-associated innate immune alterations were less pronounced in patients with predicted high HLA class I binding capacity to SARS-CoV-2 peptides, GM-CSF production in CD8 + CM T cells, a feature associated with severe COVID-19 disease, was increased.
HLA typing For DNA extraction the DNA easy blood and tissue kit from Quiagen was used. HLA typing was performed using next generation sequencing (NGS) with the NGSgo-AmpX v2 HLA kits (GenDx, Utrecht, Netherlands), and sequenced on an Illumina MiniSeq (Illumina, San Diego, CA). Sequence data were analyzed with NGSengine (GenDx, Utrecht, Netherlands). For samples with low DNA amount, HLA typing was also performed using sequence specific oligomers (SSO) with the LABType kits (One Lambda, Canoga Park, CA). The bead-based analysis was run on a LABScan 3D instrument (Luminex, Austin, TX) and analyzed using the Fusion Software (One Lambda, Canoga Park, CA). All assays were performed according to the manufacturer's recommendations.
ACKNOWLEDGMENTS We thank the patients and blood donors who contributed to this study and the hospitals at Nantes, Toulouse, and T€ ubingen for sample and data collection. Regarding specifically the  COVID-BioToul biobank (ClinicalTrials. govidentifier  NCT04385108 ), we thank the  CRB TBR , the Clinical Research Center  1436 , and the delegation for clinical research and innovation of  Toulouse University Hospital  for their highly valuable implication. We thank the biological resource center for biobanking ( CHU Nantes, Nantes Universite, Centre de Ressources Biologiques  [ BB-0033-00040 ],  Nantes, France). This work was supported by the  Swiss National Science Foundation  (  733310030_170320 ,  310030_188450 , and  CRSII5_183478  to B. B. ;  31CA30_195883  to  S. Kreutmair , M. C. , M. B.
, and B. B. ), The  LOOP Zurich , the  Vontobel Foundation  (to B. B. ), the  European Union 's  Horizon 2020 research and innovation program  under grant  847782  (to B. B. and A. R. ) and the  European Research Council  (ERC;  882424 ) to B. B. ,  Agence National de la Recherche (ANR)  and  Region Pays de la Loire -Flash  COVID-19: COVARDS  project (to A. R. ) and the  French Ministry of Health  with the participation of  Groupement Interregional de Recherche Clinique et d'Innovation Sud-Ouest Outre-Mer  ( PHRCI 2020 IMMU-NOMARK-COV ) (to R. L. and G. M. -B. ). S. Kreutmair is a recipient of a postdoctoral fellowship from  Deutsche Forschungsgemeinschaft (DFG). N. G. N. is a recipient of a  University Research Priority Program (URPP) postdoctoral fellowship. F. I. received a PhD fellowship from  Studienstiftung des Deutschen Volkes.
COVID-19 patient samples Clinical routine data and blood samples for peripheral blood mononuclear cell (PBMC) isolation and cryopreservation were collected at the University Hospital Tuebingen (Germany), the Toulouse University Hospital (France, in the frame of the COVID-BioToul biobank, ClinicalTrials. gov Identifier: NCT04385108) and the Nantes University Hospital (France) (Table  S1A ). All donors had given written informed consent and the study was approved by the regional ethical review board of Tuebingen (COVID-19), Toulouse (COVID-19) and Nantes (COVID-19, HAP, Healthy) respectively. COVID-19 diagnosis was established by a positive PCR test. PBMC samples were collected longitudinally at the indicated time points post-admission to the hospital (Table  S1C ). COVID-19 patients were graded according to the maximum severity of disease during the study based on the WHO ordinal scale (World Health Organization, 2020b).
The WHO grade 1 and 2 were combined to grade 1 in our scale, the WHO grade 7 and 8 were combined to grade 6 in our Continued REAGENT or RESOURCE SOURCE IDENTIFIER corrplot https://github. com/taiyun/corrplot dplyr https://cran. r-project. org/web/packages/ dplyr/index. html FlowJo V10. 6. 2. Tree Star https://www. flowjo. com/ FlowSOM (Van Gassen et al. , 2015) https://github. com/SofieVG/FlowSOM flowStats https://www. bioconductor. org/packages/ release/bioc/html/flowStats. html ggplot2 https://cran. r-project. org/web/packages/ ggplot2/index. html Harmony (Korsunsky et al. , 2019) https://github. com/immunogenomics/ harmony Hmisc https://cran. r-project. org/web/packages/ Hmisc/index. html pheatmap https://cran. r-project. org/web/packages/ pheatmap/index. html R studio (RStudio, 2015) https://www.
Description and functioning of mechanosensitive Ca 2+ channels 2. 2. 1. The Mid1-Complementing Activity (MCA) family The MCA family was first described in Arabidopsis thaliana  [97]. AtMCA1 and AtMCA2 are paralogs, with high sequence homology (the relationships between gene names and accession numbers are given in Table  S1 ). The MCA channel was thought to be organized as a homotetramer with a small transmembrane region forming the pore (with only one transmembrane domain per monomer) and a larger cytoplasmic region of motifs (an EF hand-like motif, a coiled-coil motif and a cysteine-rich region homologous to the PLAC8 or DUF614 motif near the carboxyl terminus) regulating the pore  [98] [99] [100]. While the PLAC8 domain is observed in eukaryotic proteins, its combination with the transmembrane domain and the EF hand-like motif is specific to streptophytes, and absent in chlorophytes (Figure  2A ).
AtOSCA4. 1 may have a distinct function. Cryo-EM studies revealed a molecular structure formed by a homodimer with two pores  [119] [120] [121]. Work on AtOSCA1. 1 and AtOSCA1. 2 suggests that the resulting pores are non-selectively permeable  The tree is drawn to scale, with branch lengths measured in number of substitutions per site. This analysis involved 22 amino acid sequences. All positions with less than 50% site coverage were eliminated, i. e. fewer than 50% alignment gaps, missing data, and ambiguous bases were allowed at any position (partial deletion option). There were a total of 770 positions in the final dataset. Evolutionary analyses were conducted in MEGA X. The four clades are colored: blue (Clade 1), yellow (Clade 2), green (Clade 3), and orange (Clade 4). to cations  [118].
These data are in agreement with the histochemical approaches using the GUS reporter gene highlighted by Mousavi et al. [111] , although this transcriptomic approach, which allows a stronger differentiation of expression levels, shows that AtPiezo expression in the columella is relatively low. Similarly, the tissue-specific expression of AtMCA1 in the columella and xylem was revealed by Yamanaka et al. [103]. Regarding the vascular tissues of the stem, apart from AtOSCA2. 1, the genes of the OSCA family show a higher expression in the phloem in contrast to AtMCA1 and AtPiezo. Concerning the OSCA gene family, it is interesting to observe that genes that are very close in sequence and probably originate from the same duplication event (see the phylogenetic tree Figure  3 ) can present very different expression profiles (e.
g. AtOSCA1. 1 and AtOSCA1. 2, or AtOSCA2. 1 and AtOSCA2. 2). These results suggest that these proteins may be involved in different physiological functions. The expression of many OSCA genes in the lateral cap or columella (AtOSCA1. 1, AtOSCA1. 3, AtOSCA2. 3, AtOSCA2. 5) could encourage studies on their role in root penetration or in the perception of water stress at the root level. In the context of mechanosensing, it would also be interesting to study AtOSCA2. 4 and AtOSCA1. 4, which show, respectively, strong and specific expression in rapidly-expanding epidermal cells. In contrast, the expression level of all these genes encoding MS channels is very low in the meristematic zone. S1  for the relationships between gene names and accession numbers.
To date, the role of MS channels in this type of response has not been studied. However, the growth and anatomical differences that have been established show that this sensitivity to bending modifies the cambial activity within the stem. Are all living cells in the stem mechanosensitive or is the sensing localized, and secondary signals are transmitted remotely to the cambium? As in the case of the root, we took advantage of recent RNA-seq data on single cells or isolated tissues to compare the expression of the MCA, Piezo and OSCA genes. As shown in Figure  5 , AtOSCA4. 1, AtOSCA2. 5 and AtMCA2 are very weakly expressed in the Arabidopsis stem. Within the OSCA family, no clade-specific expression pattern emerges from these data. Whatever the tissue analyzed, the expression of some AtOSCA genes is higher than Piezo or AtMCA1.
In particular, AtOSCA1. 3 and AtOSCA3. 1 are highly expressed. While AtPiezo shows a homogeneous expression profile in the different stem tissues, AtOSCA 3. 1, 1. 3 and 2. 2 have particular expression patterns with a very high level in the endodermis and the differentiating vessel elements. Interestingly, AtMCA1 is expressed more strongly in the differentiating vessel elements than in the endodermis, a tissue involved in graviperception, while recent studies showed a role of this channel in hypergravity  [104]. In order to investigate whether these tissue-specific expression patterns are found in other plant species, we also performed data mining and visualization on expression data obtained by RNAseq after cryodissection of stem sections at the level of phloem and differentiating xylem tissues in Populus tremula  [129].
Arabidopsis thaliana seedlings under hypergravity conditions show upregulation of AtMCA1 and AtMCA2 in the hypocotyl compared to seedlings under the 1 g condition  [104]. Since those mechanics-related gene expression patterns can suggest a gene function in the mecanosensing pathways, it could be interesting to reproduce our data-mining approach on previous transcriptome data obtained after a mechanical stress such as those performed after a transient stem/shoot bending  [132, 134] , after a load has been placed on a tree stem  [135]  or after touching rosette leaves  [136]. The expression dynamics of MS channel genes would probably give precious insight into their role in the physiological response, in short and long term. This kind of approach, combining mining and visualization of large molecular datasets with mechanical analyses at the tissue level, is a good illustration of the flourishing field of quantitative plant biology  [137].
data for AtOSCA1. 1, we entered its accession number AT4G04340 in the search bar (see Table  S1  for the relationships between gene names and accession numbers), then clicked on Tissue & Experiment eFP viewers, then on Single Cell eFP (RNA-Seq data). The resulting view can be accessed directly from the URL https://bar. utoronto. ca/eplant/?ActiveSpecies=Arabidopsis%20thaliana&Genes=AT4G043 40&ActiveGene=AT4G04340&ActiveView=SingleCellView. The raw data were then downloaded as JSON files. For the different tissue/cell types, to facilitate the graphical representation of expression levels in tissues, only expression values from sub-clusters containing differentiated (not differentiating) cells were used. In the stele, only the expression data from xylem-and phloem-cell types are shown. In the meristematic zone, we represented the expression mean of dividing cells and cells from the quiescent center.
Figure3. Maximum-likelihood phylogenetic tree of OSCA of Arabidopsis thaliana and Populus trichocarpa. The percentage of tree in which the associated taxa clustered together is shown next to the branches. Initial trees for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using the JTT model, and then selecting the topology with superior log-likelihood value. The tree is drawn to scale, with branch lengths measured in number of substitutions per site. This analysis involved 22 amino acid sequences. All positions with less than 50% site coverage were eliminated, i. e. fewer than 50% alignment gaps, missing data, and ambiguous bases were allowed at any position (partial deletion option). There were a total of 770 positions in the final dataset.
Acknowledgments: We thank  Mélanie Decourteix ,  Jean-Stéphane Venisse  and  Marylou Mantova  for advice on phylogenetic trees, and  Valérie Legué  for comments on the manuscript. (i/ii) occurs between phloem and xylem differentiation, in the middle of the dividing cambial cells; (ii/iii) marks the end of cell expansion and the onset of secondary cell wall formation; (iii/iv) marks the end of secondary cell wall deposition and the late maturation of xylem cells.
Funding: This work was funded by the  I-SITE CAP   20-25  ( ANR grant   16-IDEX-0001 ) Emergence 2019 from the  University of Clermont-Auvergne ,  France. Erwan Tinturier  benefits from a PhD grand funded by the  French Ministry of Higher Education, Research and Innovation. Funding bodies were not implied in the design and analysis of the study.
In STS, this analysis highlighted the enrichment in the pentose and glucuronate interconversion (PGI) pathway, also amplified in the Yang Huang syndrome described in the context of traditional Chinese pharmacology  [21]. The PGI pathway relies on UDP-glucuronosyltransferase (UGT) enzymes that catalyze the binding of d-glucuronic acid to toxic substances or endogenous compounds such as bilirubin via glycosidic bonds, contributing to the detoxification of lipophilic compounds or glucuronides. Exploration of the TCGA database allows one to identify more discrete signatures displayed by major STS subtypes versus other types of cancers. As shown in Fig. 1 , all cancer types display abnormalities in cell cycle regulation. Most carcinomas show an enrichment in oncogenic pathways, glycolytic signatures and alterations of energetic, nucleotide, amino acid or macromolecule pathways.
REDOX, VITAMINS Fig. 1  Analysis of the TCGA transcriptomic database. Dotplots showing functional enrichment for co-expression modules found in various cancer types and predominant sarcoma subtypes. Htseq raw counts were retrieved from TCGA using GDCquery  [22]  and VST-normalized  [23]. For each dataset, the unsigned co-expression network was produced using WGCNA with automatic pick for soft-thresholding powers. Genes in each module were queried for functional enrichment against Reactome Pathway Database [24] using clusterProfiler  [25]. p values were adjusted using Benjamini-Hochberg procedure. For each dataset-pathway pair, the p value corresponds to the lowest one from all the co-expression modules. A subset of the significant (q-value < 0. 05) pathways was manually annotated into functional groups for display in the figure.
Funding  Richard Miallot  is a recipient of an  INCA doctoral fellowship  ( PLBIO19-015 ). Philippe Naquet is supported by institutional funding from  CNRS ,  INSERM , and  AMU , as well as grants from  Fondation pour la Recherche Médicale  ( DEQ20140329532 ),  ARC  ( PJA 20181208002 ) and  INCA  ( PLBIO19-015 ). Jean Yves Blay is supported by funds from  NetSARC, LYRIC  ( INCA-DGOS 4664 ),  LYon Recherche Innovation contre le CANcer , European Clinical trials in  Rare Sarcomas  ( FP7-278742 ), and  European network for Rare Adult solid Cancer.
Gisttrials. https://gistt rials. org/iLRG/ showf rstl ine. php. Accessed16 June 2021 [B] NIH U. S. National Library of Medicine. Clinicalstrial. gov. https:// clini caltr ials. gov/ ct2/ home. Accessed 16 June 2021 [C] NIH. Cancer. gov. https:// www. cancer. gov/ about-cancer/ treat ment/ clini cal-trials/ search/ r? loc= 0&q= sarco ma& rl=1. Accessed 16 June 2021 [D] Rafael Pharmaceuticals, Inc. https:// rafae lphar ma. com/ resea rch-and-devel opment/ cpi-613-drug. Accessed 16 June 2021 [E] ICH GCP. Good Clinical Practice Network. https:// ichgcp. net/ clini cal-trials-regis try/ NCT04 593758. Accessed 16 June 2021.
Filtered reads were mapped to the mouse genome mm10 with Bow-tie2 version 2. 3. 5 (Langmead and Salzberg 2012) with default options. Only alignments with a mapping quality >30 were kept  (Danecek et al. 2021). PCR duplicates were removed with Picard version 2. 19. 0 (http://broadinstitute. github. io/picard). To decrease the importance of fragment length variation between libraries, only the first reads in pairs were kept, and BAM was converted to BED. Peak calling was run with a fixed fragment size of 200 bp with Macs2 Callpeak version 2. 1. 1. 20160309 (-call-summits -f BED -nomodel -extsize 200 -B -keep-dup all). In order to normalize all ChIP-M despite different signal to noise ratios, we ran MAnorm version 1. 1. 4 (Shao et al. 2012)  with -w 100 between each sample and the second replicate of the wild type.
The M-A model coefficient was extracted and used to normalize each Bedgraph from Macs2 (see https://github. com/lldelisle/ scriptsForAmandioEtAl2021 for all details). Replicates were then averaged at each base using Bedtools version 2. 27. 1  (Quinlan 2014). In Supplemental Figure  S9 , the coverages from Macs2 were normalized to the million tags. Then, wild-type replicates were averaged. CTCF site orientation was determined using CTCFBSDB 2. 0 (http://insulatordb. uthsc. edu; Ziebarth et al. 2012)  using the CTCFBS Prediction tool with sequences of 500 bp centered on each summit of each of the three wild-type replicates. For each sequence, the motif with the highest score among REN_20, LM2, LM7, and LM23 motifs was kept. Only motifs that were common between the three replicates are displayed.
Samples were genotyped by PCR as described above to select for homozygous mutant or control tissues. The SureSelectXT RNA probe design and capture Hi-C experiments were performed as described in  Bolt et al. (2021). The first part of the data analysis was performed on our local galaxy server  (Afgan et al. 2016). Raw reads were preprocessed with CutAdapt version 1. 16 (-a AGATCGGAAGAGCACACGTCTGAACTC-CAGTCAC -A AGATCGGAAGAGCGTCGTGTAGGGAAA-GAGTGTAGATCTCGGTGGTCGCCGTATCATT minimum-length = 15 -pair-filter = any -quality-cutoff = 30) (Martin 2011). Then, Hicup version 0. 6. 1  (Langmead and Salzberg 2012; Dryden et al. 2014)  and Samtools 1. 2  (Danecek et al. 2021)  were used with default parameters. The BAM file was converted to a tabular file with a Python script (see https://github. com/lldelisle/scriptsForAmandioEtAl2021 for more details).
The TAD separation scores were computed with HiCExplorer hicFind-TADs version 3. 6. (Ramírez et al. 2018; Wolff et al. 2020 ) with a fixed window size of 240 kb. Heat maps were plotted with pyGe-nomeTracks 3. 5  (Ramírez et al. 2018; Lopez-Delisle et al. 2021). Virtual C profiles were generated similarly to  Despang et al. (2019) , with a custom Python script available at https://github. com/lldelisle/scriptsForAmandioEtAl2021. The viewpoint coordinates (mm10) used were Hoxd4, chr2: 74721977; Hoxd8, chr2: 74704614; Hoxd9, chr2: 74697726; and TAD border: chr2: 75588758. The quantifications in Supplemental Figures  S6, S7 , and S13 were also carried out with a custom Python script.
Filtered reads were mapped on the mouse genome mm10 with STAR version 2. 7. 0. e  (Dobin et al. 2013)  with ENCODE parameters with a custom gtf file (https://doi. org/10. 5281/zenodo. 4596489) based on Ensembl version 102. This custom gtf file was obtained by removing readthrough transcripts and all noncoding transcripts from a protein-coding gene. In addition, all genes with the same gene name that overlaps were merged under the same gene ID to avoid ambiguous reads. FPKM values were evaluated by Cufflinks version 2. 2. 1  (Trapnell et al. 2010; Roberts et al. 2011 ) with optionsmax-bundle-length 10000000 -multiread-correct -library-type "fr-firststrand" -b mm10. fa -no-effective-length-correction -M MTmouse. gtf -G. Counts from autosomal chromosomes were used for differential expression analysis with DESeq2 version 1.
24. 0  (Love et al. 2014 ) with R version 3. 6. 0 (http://www. rproject. org) with default parameters except for θ, which was fixed to 0. 15 and 0. 99. Only genes with absolute log 2 fold change >0. 58 and adjusted P-value > 0. 05 were considered as significant. All the significant results are summarized in Supplemental File S1. PCA and clustering were performed on log 2 (1 + FPKM) values of the 500 most variant genes from autosomal chromosomes. Gene annotations on the figures were plotted using a custom gtf file (https ://doi. org/10. 5281/zenodo. 4596489) based on Ensembl version 102, and both Hoxa3-201 and Hoxd3-203 transcripts were removed to facilitate visualization.
Acknowledgments We thank  Dr. Aurélie Hintermann  for communicating results prior to publication,  Dr. Chase Bolt  for providing early access to control cHi-C data sets,  Dr. Hocine Rekaik  for help in ChIP-M experiments, and other members of the  Duboule laboratories  for comments and discussion. We are grateful to  Thi Hanh Nguyen Huynh  for her help with mouse breeding and genotyping. We thank the  Institute of Genetics and Genomics of Geneva (iGE3) Genomics Platform of the University of Geneva , and the  Gene Expression Core Facility  at the  School of Life Sciences of École Polytechnique Fédérale de Lausanne (EPFL)  for their assistance in NGS-related experiments. We are grateful to the small animal preclinical imaging platform of the  Medical Faculty of Geneva  for micro-CT scan imaging.
For instance, advanced sequencing technologies have resulted in near-complete, high-quality chromosome-scale genome assemblies at minimal cost  (Pham et al. , 2020). However, we are only just beginning to investigate the genomic information on underutilized crops (e. g. ,  Gioppato et al. , 2019). Against this backdrop, a genomic library was built covering around six times the genome length of the sour passionfruit (Passiflora edulis Sims)  (Santos et al. , 2014) , whose nuclear genome size was estimated at 1. 258 pg (expressed as 1C) or 1,232 Mbp  (Yotoko et al. , 2011). On the basis of gene richness, we selected and completely sequenced over 100 inserts. Sequence data were assembled from long sequence reads, and structural sequence annotation resulted in the prediction of some 1,900 genes  (Munhoz et al.