dataesr/scientific-paragraphs-categorization huggingface dataset:
Identification of Arctic-associated PFAMs PFAMs whose presence or absence were specifically associated with Arctic species were assessed by calculating the frequency with which the PFAM was recovered in Arctic, compared to non-Arctic species ("Antarctic" or "Other") in the dataset (Table  S2 , sheet 6). These frequencies were used to calculate a ratio and a chi-squared P-value of enrichment of the PFAM in Arctic species in the dataset with cutoff P-value 10 -05. Only PFAMs that were detected in both Arctic genomes and Arctic transcriptomes were considered as possible candidates for enrichment; and "Freshwater" and "Non-Photosynthetic" species were excluded from the dataset to avoid introducing artifacts by comparing species with highly divergent life strategies. To accurately identify expansions and contractions in PFAM content across each species, highfrequency PFAMs (defined as PFAMs for which the maximum frequency minus minimum frequency observed was greater than 100 across the entire dataset) were first fragmented into smaller orthogroups.
(Kumar et al. , 2018)  A single gamma rate for the lambda and error model was assessed from the low-frequency PFAM dataset across the entire phylogenetic tree. The CAFE outputs obtained were used to calculate enrichment ratios and Pvalues for "expansions", defined as signed positive CAFE scores, and "contractions", defined as signed negative CAFE scores for Arctic species within the dataset, using methodology as defined above. Summarised CAFE outputs and P-values are provided in Table  S3 , sheets 1-2; and all raw data, including the prior decomposition of PFAMs into orthogroups, are provided in the linked supporting database https://osf. io/3pmxb/  (Dorrell et al. , 2021b)  in the folder "CAFE and phylPCA".
Environmentally supported phylogenies of Arctic-associated PFAMs The environmental distributions of sequences containing three PFAMs (PF03831, PF03988, and PF11999) whose presence was significantly associated with Arctic species in the enrichment analysis above were investigated in meta-transcriptome and meta-genome sequence data from the Tara Oceans Expedition, inclusive of sequences from the Arctic Ocean  (Carradec et al. , 2018; Ibarbalz et al. , 2019). Sequences containing each PFAM were extracted using hmmer with gathering threshold option  (Potter et al. , 2018) ; and were classified into four categories based on the sum of all relative abundances (normalised against the total number of meta-T or meta-G unigenes sequenced from the sample) across all stations and size/depth fractions.
, 2017). Cultured sequence accessions were manually labelled with "Arctic" and "Antarctic" provenance considering the isolation site of the species, where recorded  (Guiry et al. , 2014). Alignments were manually trimmed after each step, and subsequently trimmed with TrimAl using the -gt 0. 5 setting  (Capella-Gutiérrez et al. , 2009) ; and finally used to infer best-scoring trees with RAxML using the PROTGAMMAGTR, PROTGAMMAJTT and PROTGAMMAWAG substitution matrices  (Stamatakis, 2014). Environmental sequence calculations, and individual and consensus topologies for each tree are provided in Table  S3 , sheets 3-7; and in the linked supporting database https://osf. io/3pmxb/  (Dorrell et al. , 2021b)  in the folder "Environmental PFAM calculations".
, 2021a) ; and presence of a corresponding transcript in decontaminated MMETSP transcriptome assemblies for the species, inferred by reciprocal BLASTp best-hit searches  (Keeling et al. , 2014). Next, a comparative pair of query genomes from a specific algal lineage, one Arctic and one non-Arctic (e. g. , Baffinella sp. CCMP2293, and Guillardia theta, from cryptomonads  (Curtis et al. , 2012) ) were searched against a composite dataset of Arctic and non-Arctic genomes and transcriptomes from another algal group (e. g. , chlorophytes) via LAST  (Kiełbasa et al. , 2011) , and the best-scoring reference sequences (defined by bitscore) to each query gene were recorded. As both Baffinella CCMP2293 and G. theta are of equivalent phylogenetic distance to all chlorophytes, the number of LAST best hits obtained by the Guillardia genome to each reference chlorophyte sequence should act as a predictor of the number of hits obtained by the Baffinella genome, with positive deviations from this, as inferred by linear regression, potentially relating to more recent HGTs between the Baffinella genome and the reference species.
A schematic diagram explaining the methodology; and exemplar scatterplot outputs, are provided in Fig. 6 -Figure Supplement 1. These analyses were repeated independently for four comparative pairs of Arctic and non-Arctic query genomes (Baffinella CCMP2293 v Guillardia theta; Pavlovales CCMP2436 v Chrysochromulina tobin; Ochromonas CCMP2298 v Nannochloropsis gaditana; and the novel pelagophyte CCMP2097 v Aureococcus anophageferrens), and combined reference libraries generated from the eight algal lineages considered in this study (chlorophytes, chrysophytes, cryptomonads, diatoms, dictyochophytes, dinoflagellates, haptophytes and pelagophytes), with the exception of within-taxon comparisons (e. g. , cryptomonad query genomes were not compared to the cryptomonad reference dataset). Details of the scripts, and tabulated LAST best hit frequencies, are provided in Table  S4 , sheets 2-3; and in the linked supporting database https://osf.
A full set of orthologues were assembled for each retained query sequence by retrieving the LAST best hit for 151 non-redundant taxonomic groups from the all tree of life library used above  (Dorrell et al. , 2019; Dorrell et al. , 2021a) ; the pan-algal dataset of genomes and transcriptomes used in this study; and a further set of 33 eukaryotic reference genomes and 59 prokaryotic reference genomes, sampled to include diverse representation from the tree of life, including multiple Arctic and Antarctic prokaryotes (Table  S4 , sheet 5); along with all sequences identified from the genomes and MMETSP transcriptomes of each of the four Arctic algae sequenced in this study that could be retrieved by LAST with threshold e-value 10 -05. Clusters for pairs of seed sequences that were found to show greater similarity to one another than any non-seed sequence within the cluster via internal BLAST searched were merged, yielding 215 merged gene clusters of non-redundant seed sequences and a total set of approx.
21 of the realised trees were found to contain monophyletic groups of multiple Arctic species with > 50% bootstrap support; and a further 13 contained groups consisting of a majority of Arctic species with some Antarctic or nonpolar species, with > 50% support for the ancestral node (Fig. 6 -Figure Supplement 2; Table  S4 , Sheet 9). Finalised alignments and consensus tree topologies are provided in Table  S4 , sheets 7-8; and all intermediate alignment processing steps, and individual bootstrap replicates for each tree, are provided in the linked supporting database https://osf. io/3pmxb/  (Dorrell et al. , 2021b)  in the folder "Candidate HGT trees". Inferred functions were inspected for all genes from each Arctic query genome identified at each stage of the pipeline (all decontaminated genes; all genes with a within-Arctic LAST best hit; all genes with an Arctic top ten BLAST best-hit; and all genes confirmed by single-gene trees to be participants of within-Arctic HGTs).
The assessed functions included KEGG and KOG annotations, assessed by BlastKOALA and GhostKOALA  (Kanehisa & Sato, 2020) ; PFAM domains, inferred by InterProScan  (Jones et al. , 2014) ; absolute and ranked rpkm values of transcripts corresponding to each gene in corresponding MMETSP transcriptomes derived under different physiological conditions  (Terrado et al. , 2015) ; and predicted subcellular localisations, inferred using ASAFind v 2. 0 used in conjunction with Signal P v 3. 0  (Gruber, Rocap, Kroth, Armbrust, & Mock, 2015) , HECTAR using a default scoring matrix  (Gschloessl, Guermeur, & Cock, 2008) , and WolfPSort, taking the consensus annotations obtained with animal, fungal and plant reference models  (Horton et al. , 2007). Tabulated functions associated with each sequence, and two-tailed chi-squared enrichment values for each function in within-Arctic HGTs, are provided in Table  S4 , sheets 9-12.
showing mean (horizontal axis) and median enrichments (vertical axis) in LAST top hits from the complete genomes of comparative pairs of Arctic algal species and phylogenetically equivalent non-Arctic species, searched across taxon-specific reference libraries trom the pan-algal dataset. Species are shaded by phylogenetic origin (inner colour) and biogeographical origin (outer colour). Five Arctic species that show strong enrichments in LAST top-hits to other Arctic queries, plus Pavlovales sp. CCMP2436 which uniquely amongst the four sequenced species does not, are labelled. Deviation values were calculated as (CCMP2097 obs -CCMP2097 exp|Aureococcus , CCMP2293 obs -CCMP2293 exp|Guillardia , CCMP2436 obs -CCMP2436 exp|Chrysochromulina , and CCMP2298 obs -CCMP2298 exp|Nannochloropsis ) where "obs" is the observed number of best hits for a given reference species and "exp" is the expected number based on linear regression from the number of best hits obtained with the non-Arctic query within the same comparative pair.
Searches between query and reference libraries from the same taxonomic group were excluded from all calculations. Fig. 1-Figure supplement 1. Scatterplot of sampled latitude, and growth temperatures, of all geolocalised algal genomes and transcriptomes within the pan-genomic dataset assembled for this study. Taxa are shaded by phylogeny and biogeography per Fig. 1. Data were manually verified for each culture by comparing the synthesis of the genome and transcriptome portal data  (Grigoriev et al. , 2021; Keeling et al. , 2014)  with recorded permissible growth temperatures for each corresponding culture collection accession, taking into account synonymous strain names housed in different collections. Tara Oceans data is taken from PANGAEA entries for each station  (Pesant et al. , 2015). Growth temperatures are provided as ranges, centred around the experimental temperature (if recorded) at which the genome or transcriptome library was generated.
Full coordinate values are provided in Table  S2 , sheet 6. Each coordinate point is coloured by phylogeny (fill) and biogeographical origin (line). The all other Arctic species in the datasets are labelled. The query library is convergent to Reference A; Reference B is also convergent, but fragmented; Reference C is also convergent, but with specific expansions in individual PFAMs; and Reference D is non-convergent. B: calculated Bray-Curtis, BUSCO-normalised shared PFAM, and Spearman correlation values between the query library and References A-D. C: exemplar heatmap of pairwise comparisons between 12 libraries, and D: the convergence calculations made between them, shaded per the sampled intersecting cells in the heatmap. Of note, comparisons within individual lineages, between genomes and transcriptomes, and involving either freshwater or non-photosynthetic species were excluded to minimise latitudeindependent biases on PFAM convergence estimates.
Mock, 2015) , HECTAR under default conditions  (Gschloessl, Guermeur, & Cock, 2008) , and WolfPSort, with consensus animal, plant and fungal reference datasets  (Horton et al. , 2007)  with proteins inferred to have a signal peptide and non-plastid prediction by either ASAFind or HECTAR, or an extracellular prediction by WolfPSort, inferred to be potentially secreted proteins. Pavlovales sp. CCMP2436 (  1 )                1 0 0 6 1 8                                  Geobacter-sp-FeAm09_A0A5C1M8E8 Ba nella sp. CCMP2293 (23 J Q 2 3 0 0 0 2. 1 _ C r y p t o m o n a s _ o b o v a t a _ s t r a i n _ F A C H B _ 1 3 0 1 K F 9 0 7 4 0 3. 1 _ C r y p t o m o n a s _ s p _ D u m o 2 _ 1 0 0 3 1 0 C C A M N T _ 0 0 1 6 2 0 1 4 0 3 _ H e m i s e l m i s _ t e p i d a _ S t K C 9 2 8 3 1 9. 1 _ C r y p t o m o n a s _ o z o l i n i _ v o u c h e r _ H B 2 0 1 1 _ 1 o z C A M N T _ 0 0 0 0 7 4 6 2 2 3 _ G u il la r d ia _ t h e t a _ C C M P 2 7 1 2 G Q 3 7 J Q 9 6 7 3 0 3.
Acknowledgments The work conducted by the  U. S. Department of Energy Joint Genome Institute , a  DOE Office of Science User Facility , is supported by the  Office of Science of the U. S. Department of Energy  under Contract No. DE-AC02-05CH11231. RGD and AMGNV acknowledge funding from a  CNRS Momentum Fellowship  ( 2019-2021 ) awarded to RGD. CL received support from the  Discovery program   of Natural Science and Engineering Council (NSERC, Canada)  and a pilot project grant from  Genome Québec. CB acknowledges funding from the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation programme  (grant agreement No. 835067 ; Diatomic), the.
Cells were analyzed using a BD LSRFortessa TM X-20 analyzer (BD Biosciences). Data were analyzed using Kaluza 2. 1 (Beckman Coulter) or FlowJo 10. 6. 1 (BD Biosciences). For t-SNE mapping, the R Console plugin (https://www. beckman. fr/flow-cytometry/software/ kaluza/r-console) was used to analyze flow-cytometry data in R within the Kaluza Analysis framework. Specific CAR-mediated activation in Jurkat RT3-T3. 5 cells. Jurkat J. RT3-T3. 5 cells (ATCC; TIB-153) transduced with either a CD28 CAR vector or a 4-1BB CAR vector were cocultured overnight with irradiated (35 Gy) HLA-A2/A28-positive or HLA-A2/A28-negative human splenocytes at a cell ratio of 1:1. After 20 h of incubation, the cells were stained with an HLA-A2 MHC pentamer and antihuman EGFR antibody to monitor CAR expression; 7-aminoactinomycin D (7AAD) and an anti-human CD69 antibody were all used.
Pierce ECL western blotting substrate was used for detection as previously described  64, 65. Quantification was performed using ImageJ software according to the standard gel signal measurement method. Uncropped and unprocessed scans of the blots are available in the Source Data file. Cytokine analysis. Plasma or culture supernatants were collected at the indicated times, and cytokine production (TNF-α, IFN-γ, IL-2, IL-4, IL-6, IL-10, and IL-17A) was determined using the Cytometric Bead Array Th1/Th2/Th17 Kit (BD Biosciences) according to the manufacturer's instructions. Liquid chromatography-mass spectrometry metabolite analyses. At day 16 of culture, 3 × 1. 10 6 cells of each culture condition were separated from stimulation beads, washed twice in PBS and stored at -80 °C. The extraction solution was composed of 50% methanol, 30% acetonitrile, and 20% water.
After the addition of extraction solution, the samples were vortexed for 5 min at 4 °C and then centrifuged at 16,000 × g for 15 min at 4 °C. The supernatants were collected and stored at -80 °C until analyses. Liquid chromatography-mass spectrometry analyses were conducted on a QExactive Plus Orbitrap mass spectrometer equipped with an Ion Max source and a HESI II probe and coupled to a Dionex UltiMate 3000 UPLC system (Thermo Fisher Scientific) as previously described  66. Data were acquired using Thermo Xcalibur software (Thermo Fisher Scientific). The peak areas of metabolites were determined using Thermo TraceFinder software (Thermo Fisher Scientific), identified by the exact mass of each singly charged ion, and by known retention time on the HPLC column. VCN assessment. Genomic DNA was isolated from transduced cells using a Genomic DNA Purification kit (Qiagen).
Using the manufacturer's QuantaSoft software (Bio-Rad), the concentration of the target amplicon per unit volume of input for each sample was estimated for both Psi and the Albumin reference gene. The VCN was determined for each sample as follows: VCN = 2 x Psi signal/Albumin signal. Quantitative real-time polymerase chain reaction (RT-qPCR). CAR transcript expression was assessed by RT-qPCR using primers designed in both scFv and EGFRt regions (sequences shown in Supplementary Table  1 ). Briefly, mRNA was extracted using the RNeasy Mini Kit® (Qiagen) according to the manufacturer's protocol and subjected to reverse transcription (High Capacity RNA-to-cDNA Master Mix (Applied Biosystems™)). mRNA expression levels were assessed using Fast SYBR™ Green Master Mix (Applied Biosystems™) with a Viia7 thermocycler (Thermo Fisher Scientific).
5. 4a (https://github. com/alexdobin/STAR/releases). The ribosomal RNA gene annotations were removed from the GTF file. The gene-level quantification was calculated by featureCounts (http://subread. sourceforge. net/). Raw read counts tables were normalized by median of ratios method with DESeq2 package from Bioconductor (https://bioconductor. org/packages/release/bioc/html/DESeq2. html) and then converted to GCT and CLS formats. RNAseq quality control and analysis Quality control. We screened for contamination by using known cell type-specific transcripts (per ImmGen ULI RNAseq and microarray data). Finally, The RNA integrity for all samples were measured by median Transcript Integrity Number (TIN) across human housekeeping genes with RSeQC software (http:// rseqc. sourceforge. net/#tin-py).
All samples passed the quality control, including the following criteria: (1) more than 1 million uniquely mapped reads; (2) more than 8,000 genes with over ten reads (3) TIN superior at 45. Differential gene expression. To limit the background noise and avoid unreliable values from low expression or/and high variability intrareplicate, only genes with a minimum average read count of 15 tpm in one of the groups and an inter-replicate coefficient of variation (CV) intrareplicate <0. 6 were retained for analysis. We used an uncorrected t-test to assess differential gene expression between the different groups from the normalized read counts dataset. Signature overlap. An activation human Treg signature was curated from a published human dataset (GSE76598)  69  downloaded through the Gene Expression Omnnibus (GEO) (https://www.
ncbi. nlm. nih. gov/geo/). To reduce noise, genes with an inter-replicate CV intrareplicate <0. 7 and an expression level >20 in either comparison groups were selected. Up and downregulated transcripts from the comparison "stimulated versus non stimulated ex vivo native Tregs" were selected at an arbitrary threshold of FC > 1. 5 or FC < 0. 75, and a t-test p-value < 0. 05. The overlap significance into our dataset was assessed by Chi square test. TSDR DNA methylation analysis. Regulatory T-cell-specific demethylation region (TSDR) DNA methylation analysis was performed as previously described  70  using genomic DNA isolated from freshly sorted or expanded Treg cells using the QIAamp DNA Mini Kit (Qiagen). A minimum of 60 ng bisulfite-treated (EpiTect; Qiagen) genomic DNA was used for RT-qPCR to quantify ∑ TSDR demethylation.
Liver and lung sections (4 µm) were stained with hematoxylin and eosin, scanned (Nanozoomer 2. 0, Hamamatsu) and blindly assessed by two independent researchers using NDPview software (Hamamatsu). Luciferase assay. To evaluate CAR-Treg homing in vivo, sorted Tregs (CD4+ CD45RO+ CD25 hi CD127lo) were activated as described above. Two days later, the cells were transduced with either a CD28 CAR lentivirus or a 4-1BB CAR lentivirus at a MOI of 30 together with luciferase-mCherry-lentivirus at an MOI of 5. The lentiviral plasmid encoding a firefly luciferase protein (pEFS-eFFLY-mCherry) was kindly provided by Dr. Matthias Titeux. After 7 days of culture, double-transduced mCherry+EGFR+ Tregs expressing the CAR (or MOCK, the negative control) and luciferase were sorted before restimulation as described in Fig.
1c. On day 18 of culture, 5 × 10 6 luciferase-Tregs and 5 × 10 6 human allogeneic HLA-A2+ PBMCs were injected intravenously into conditioned NSG mice. For bioluminescence imaging, D-luciferin potassium salt (150 mg/kg, Perkin Elmer) was intraperitoneally injected before anesthesia with isoflurane, and images were acquired within 20 min on an IVIS Spectrum CT (Perkin Elmer). Data were analyzed using Living Image software (IVIS Imaging Systems), and the BLI and X-ray superimposition signals were quantified after 3D bioluminescence imaging tomography (DLIT). Using coregistration with the Automatic Mouse Atlas, a region of interest (ROI) corresponding to the spleen was determined, and the absolute number of cells was obtained using in vitro calibration with corresponding cells. The cell indices (CIs) were normalized to the reference value (measured just prior to adding CAR-Tregs to the culture).
Cells were then split into prewarmed culture medium at 10 6 cells/mL with IL-2 (1000 UI/mL) and incubated at 37 °C and 5% CO 2 for 24 h before removing stimulation anti-CD3/CD28 beads. Statistical analysis. The results are presented as the mean +/-SEM or median for continuous variables. Frequencies of categorical variables are presented as numbers and percentages. Analyses were performed using GraphPad Prism software (version 8. 00; GraphPad Software). For statistical comparisons of in vitro data, we used the nonparametric two-tailed Mann-Whitney test for comparisons of two groups. For survival comparisons, the log-rank test was used. p-values < 0. 05 were considered significant. Autoscaled metabolic data were normalized by median through Metaboanalyst 5. 0 and compared with FDR correction.
xCELLigence cytotoxicity assay. To evaluate the cytotoxic potential of CAR-Tregs, the viability of HLA-A2+ and HLA-class I-deficient (through CRISPRinduced deletion) endothelial cell lines (human CiGEnc cell line (11RRID: CVCL_W185), provided by D. Anglicheau) was monitored every 15 min for 10 h by electrical impedance measurement with an xCELLigence RTCA MP instrument (ACEA Biosciences). In each E-plate (ACEA Biosciences) well, 1 × 10 4 HLA-A2+ or HLA-class I-deficient endothelial cells were seeded. After 15 h, 2 × 10 4 CAR-Tregs or CD8+ CAR-T cells were added to the culture. As a cytotoxicity control, CD8+ T cells were transduced 2 days after activation with the CAR construct at an MOI of 40 and incubated for 18 h for transduction. On day 5 posttransduction, CD8+ EGFRt+ cells were sorted using a FACS Aria II.
Acknowledgements We thank  S. Berissi  and colleagues at the Plateforme d'histologie et morphologie du petit animal;  O. Pellé  and colleagues at the  Plateforme de cytométrie ;  E. Panafieu  and colleagues at the  Laboratoire d'Expérimentation Animale et Transgénèse of the Structure Fédérative de Recherche Necker, Paris ;  G. Froment ,  D. Nègre  and  C. Costa  from the AniRA lentivectors production facility at the  CELPHEDIA Infrastructure  and  SFR BioSciences  ( UMS3444/CNRS/US8 / Inserm ,  ENS Lyon, UCBL ); the  Etablissement Français du Sang  for blood supply from healthy donors;  SIRION biotech GmbH  for providing LentiBOOST TM and  A. De Cian  ( U1154 ,  CNRS UMR7196 ,  Museum National d'Histoire Naturelle, Paris, France)  who kindly provided the Cas9 protein. B. L. was supported by the  IMAGINE Institute  and the  DIM-Thérapie Génique/Région Ile-de-France.
A. M. was supported by the  Assistance-Publique Hôpitaux de Paris. S. C. and T. B. were supported by the  Fondation Emmanuel Boussard. The study was also funded by the  Fondation Recherche Medicale  ( PME20160635834 ),  Fondation Centaure ,  Fondation Day-Solvay ,  Sandoz Pharmaceuticals ,  Agence de la Biomédecine  and  Agence Nationale de la Recherche  ( DAISY ,  ANR-20-CE18-0004 ). The IVIS spectrum used in this study was purchased with a grant from the  DIM-Thérapie Génique/Région Ile-de-France. B. L. created Fig. 6a from  Servier Medical Art  images, according to the terms of use (smart. servier. com).