dataesr/scientific-paragraphs-categorization huggingface dataset:
(ii) tSNE analysis T-distributed stochastic neighborhood embedding (tSNE) analysis was performed with FlowJo TM Software Version 10. 5. 3 for Mac OS X. cDC11 + and BDCA2-3 + cells were selected for the analysis and a downsampling of 3000 cells was performed to have the same number of cells per subject. Only surface markers were selected to perform tSNE analysis with 1000 iterations and a perplexity of 20.
Acknowledgements BR would like to acknowledge  Dr. Yamila Rocca  ( CIRI, T Walzer team ). Authors specifically thank  Sebastien Dessurgey  and  Thibault Andrieu  (SFR biosciences, cytometry platform) for their technical advices and  Dr. Chloé Journo  for her critical reading, helpful discussions and her help in the statistics. Authors are also grateful to  Dr. David Karlin  for his advices in writing and to  Dr Patrick Lecine  for critical reading.
RT-qPCR For quantitative reverse transcription polymerase chain reaction (RT-qPCR) analysis, total RNA was DNAseI-treated and reversetranscribed using the RevertAid RT Reverse Transcription Kit (Thermo Fisher Scientific, Waltham, MA, USA). qPCR was performed with 59 HOT FIREPol EvaGreen qPCR Mix Plus (Solis Biodyne, Tartu, Estonia) using three technical replicates from three biological replicates each. qPCR was performed on a CFX Connect Real-Time Detection System and the results analyzed on the CFX MANAGER software package (BioRad, Hercules, CA, USA). The relative expression values for all experiments were calculated based on the expression of the control housekeeping gene AT4G05320 (UBIQUITIN 10). Relative expression was calculated using the 'delta-delta method' formula 2 À [DCP sampleÀDCP control] , where 2 represents perfect PCR efficiency.
DMR identification The DMR analysis was carried on with the R package DMRCALLER  (Catoni et al. , 2018) ; biological replicates from control and infected samples were pooled and compared between treatments. In order to compare both pools, the genome was divided in equal bins of 50 bp size. The DMRs were then computed by performing Fisher's exact test between the number of methylated reads and the total number of reads in both conditions for each bin. The obtained P-values were then adjusted for multiple testing using Benjamini and Hochberg's  (Benjamini & Hochberg, 1995)  method to control the false discovery. Bins with fewer than three cytosines in the specified context or < 0. 25 difference in methylation proportion between the two conditions or an average number of reads lower than 8 were discarded.
Microarray analysis Microarray analysis was performed for the datasets indicated in Table  S2  and retrieved from the NCBI Gene Expression Omnibus (GEO) database (https://www. ncbi. nlm. nih. gov/geo/). The expression values were normalized by quantifying the ratio of the mean value for each treatment to the mean value of its respective control. A heat map for the analysis of microarray data was produced using HEATMAPPER  (Babicki et al. , 2016).
Analysis of TE activation under different stresses identifies M. persicae as a potential inducer of epigenetic changes To identify stresses that alter the epigenetic regulation in A. thaliana, we performed an analysis of TE expression from ATH1 microarray datasets, which have been widely used by the community. The ATH1 microarray contains 1155 TE probes used to track changes in transcript abundance influenced by epigenetic reprogramming  (Slotkin et al. , 2009). We investigated TE expression under different stresses, including abiotic (heavy metal presence, exposure to heat, cold, spaceflight or UV light among others) and biotic (viral, oomycete, bacterial and insect infection/infestation) (Fig. 1a ; Table  S2 ). We found that, in general, these stresses can induce a modest reactivation of TEs, although this response is dependent on the specific stress (Fig.
Aphid infestation induces transcriptional activation of TEs Our previous analysis of ATH1 public datasets indicated a potential reactivation of TEs during aphid infestation. However, the TE probes on the ATH1 array do not represent the genomic distribution of TEs, and favor Helitron elements that resemble genes. Accordingly, we explored TE transcriptional and posttranscriptional regulation by performing RNA and sRNA sequencing, which target (respectively) mRNAs and sRNAs derived from Pol II and Pol IV activity (Fig. 3 ). Principal component analysis of TE expression in mRNA libraries generated from control and infested tissue demonstrated that biological replicates clustered together (Fig. S3a ). Analysis of RNA sequencing indicated that 71 TEs suffered a transcriptional reactivation upon aphid infestation (Fig.
4e ), pointing to their association with gene body methylation (Fig. 4c ). On the other hand, DMRs in the CHG and CHH contexts are highly dynamic and experienced significant changes in different methylation contexts (especially in the CHG and CHH contexts) in the regions that experienced hypo-and hypermethylation (Fig. 4e ). Owing to the tight association between CHG and CHH methylation with H3K9me2  (Du et al. , 2015) , this might indicate that a strong reorganization of heterochromatin takes place in these regions upon aphid feeding. The relative low number of DMRs identified and the lack of overall changes in the global profiles of DNA methylation indicated that methylation changes could take place only in specific regions. To test if DMRs were associated with particular histone marks, we retrieved public datasets of different histone modification coverage in Arabidopsis somatic tissues  (Luo et al.
We used RNA-sequencing public datasets from Pol V and AGO4 mutants  (Zhu et al. , 2013; Rowley et al. , 2017). Pol V and AGO4 are components of the RdDM pathway that produces sRNAs to target genomic regions and introduces DNA methylation  (Matzke & Mosher, 2014). Pol V produces long noncoding transcripts that guide Pol IV-derived 24 nt sRNAs loaded into AGO4 to chromatin  (Wierzbicki et al. , 2009). Mutations in AGO4 or PolV impair RdDM-dependent methylation, especially in the CHH context, and 82% of loci regulated by Pol V or Pol IV are also regulated by AGO4/AGO6  (Duan et al. , 2015). Differentially expressed genes associated with DMRs and confirmed by RT-qPCR were significantly enriched in the portion of genes regulated by the RdDM pathway components AGO4 and/or Pol V (33. 3% overlap, two-tailed P < 0.
Acknowledgements Research in the Martinez group is supported by  SLU , the  Carl Tryggers Foundation  and the  Swedish Research Council  ( VR 2016-05410 ). Sequencing was performed by the  SNP&SEQ Technology Platform in Uppsala. The facility is part of the  National Genomics Infrastructure (NGI) Sweden and Science for Life Laboratory. The  SNP&SEQ Platform  is also supported by the  Swedish Research Council  and the  Knut and Alice Wallenberg Foundation.
Funding: This research was funded by  French LABEX  " SMS: Structuring social worlds " ( ANR-11-LABX-0066 ) and the  PSDR4 -Repro-Innov program  ( INRAE-Conseil Regional Occitanie ). This study was financed in part by the  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brasil (CAPES) -(Ph. D. fellowship  0900-13-3 ). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
metabolites such as trimethylamine (TMA)  16, 21, 22  and can induce specific host immune responses  7, 23, 24. Overall, the role of the human archaeome, particularly in health and disease  6, 9  , still needs to be explored, with the most puzzling question, whether archaeal pathogens do exist, as an intrinsically pathogenic capacity of archaea has never been identified. Based on the recent activities to generate and collect thousands of metagenome-assembled genomes (MAGs) from metagenomic datasets of human GIT  2, [25] [26] [27]  , a treasure of information was produced. In the present study, we present a public catalogue composed of 1,167 archaeal genomes and 28,581 protein clusters derived from the human gastrointestinal archaeal community. Leveraging this comprehensive sequence collection, we gain previously undescribed insights into the abundance, distribution, composition and function of the human archaeome.
NaTurE MIcrOBIOlOgy Methanocorpusculum (3; 0. 3%). Methanobacterium, Haloferax and Halorubrum spp. were represented by only one genome each. Of the 1,167 genomes, 10 (0. 85%) could not be assigned to any previously described genus and 98 genomes (8. 3%) did not match any known species. A large proportion of genomes not matching any known species (n = 83) and genera (n = 10) were affiliated with the order Methanomassiliicoccales. Read-based community profiling revealed a fraction of 1. 22% archaeal reads in representative original datasets (Supplementary Table  2a-f ). Based on growth rate index analyses, we received good evidence that the major archaeal species are indeed actively replicating within their habitat (see Supplementary Information and Extended Data Fig. 2 ). Pan-genome analyses (Supplementary Information) revealed that the gut archaeome still remains largely undersampled.
Archaeal protein profile correlates with geographic and demographic parameters. In total, 1. 8 million proteins were identified from the 1,167 genomes, 54% of which were annotated as hypothetical proteins. A protein catalogue of all 1,167 archaeal genomes was generated by clustering the genes predicted across all genomes and excluding singleton clusters, resulting in 28,581 cluster representatives (>50% amino acid identity and >80% coverage) (Extended Data Fig. 3  and Supplementary Material 1). 2,050 proteins (thereof 58% hypothetical proteins) were found to be shared among >50 genomes in our dataset, mirroring the taxonomic distance of the two most abundant orders, Methanomassiliicoccales and Methanobacteriales (Fig. 2a ). The protein catalogue had predictive potential for some metadata categories (Fig.
Please refer to Supplementary Information for results on combinatory effects of multiple metadata categories, and on the association of hypothetical proteins with various metadata categories (Supplementary Tables  5  and 6 ). The dataset reveals previously undescribed members of the human gastrointestinal archaeome. We obtained 20 genomes affiliated with Methanosphaera sp. , including three genomes from isolates. Taxonomically, human-associated Methanosphaera genomes were affiliated to three distinct species-level clades (Extended Data Fig. 6  and Supplementary Table  7a-c ). Among those, M. stadtmanae was the most commonly retrieved, with 17 genomes (14 MAGs). M. stadtmanae reads represented a fraction of 0. 028% among all microbial reads, with an average fraction of 13. 45% among all archaeal reads in reference datasets (for details, see Supplementary Table  2a , and also for other taxa mentioned below).
Two MAGs (average nucleotide identity (ANI) 98. 5%) clustered within M. cuniculi, and were retrieved from healthy Asian subjects living in an urban environment. The M. cuniculi type strain was originally isolated from the intestinal tract of a rabbit  28  and has not been reported thus far in human hosts. One additional MAG belonging to the genus Methanosphaera was binned from a gut metagenome of a diseased (colorectal cancer) European male (BMI 21, age 64 years, urban environment). This genome clustered together with RUG761, a genome recovered from cattle intestines  29  (ANI 99. 0%; Extended Data Fig. 6 ). The dataset of human-associated Methanomassiliicoccales consisted of 145 genomes corresponding to 12 species (Supplementary Table  1a ). The genomes were distributed into two families, most of them belonging to 'host-associated' Methanomethylophilaceae (116 genomes), the other to Methanomassiliicoccaceae ('free-living clade'; 29 genomes).
Several representatives of this species have the genetic potential to metabolize TMA, a bacterial metabolite involved in trimethylaminuria and suspected in cardiometabolic, cardiovascular and renal diseases. Th s species is part of a well-supported clade that is separated from other Methanomethylophilaceae genera (Methanomethylophilus, Methanogranum and Methanoplasma spp. ) and belongs to the candidate genus 'UBA71' following the Genome Taxonomy Database (GTDB) classification (Supplementary Table  1c ). We thus suggest that it represents a previously undescribed genus and species, and propose the name of 'Candidatus Methanoprimaticola hominis' gen. nov. , sp. no. (Me. tha. no. pri. ma. ti' co. la. N. L. pref. metha opertaining to methane; N. L. pl. n. Primates a zoological order; L. suff. -cola (from L.
Macfarlane; representative MAG: GUT_GENOME251929). This species represented 0. 076% of all microbial reads in 691 screened studies (Supplementary Table  2a ). A number of additional archaeal taxa not yet described to be constituents of the human GIT were recovered from the MAG dataset. For details on these and other taxa (Halorubrum, Haloferax, Methanocorpuscum and Methanobacterium spp. ), please refer to Supplementary Information. The M. smithii clade splits into two separate species. An overview on host association, geography, genome size and taxonomic association of known Methanobrevibacter spp. and genomes is given in Fig. 4  (for further details on Methanobrevibacter genomes besides the M. smithii clade, see Supplementary Information). Based on ANI similarity values, as well as information derived from the protein catalogue, the M.
smithii group was represented by two species-level clades (tentatively named 'smithii' and 'smithii_A' according to the GTDB classification 31 ) (Figs. 2c  and 4a , and Supplementary Table  8a ,b; see also ref. 25  ). M. smithii_A was represented in our entire dataset 185 times (16% of the entire dataset), whereas M. smithii was detected 797 times (68%), together.
NaTurE MIcrOBIOlOgy Fig. 2  | Genome distribution on different metadata categories covering geographic origin, demographics and health aspects. a,b, Categorical metadata were grouped in three alluvial diagrams referring to geographic origin (a, lifestyle and country) and demographics (b, age and BMI group). Obesity was defined as BMI > 30 kg m -2. Infant: 0-3 years; child: 4-12 years; teenager: 13-18 years; adult: 19-64 years; elderly person: >64 years. c, Health aspects (health status and disease type). NA, no data available. For improved visibility only genomes with a minimum of three representatives according to the GTDB classification are shown. Numbers indicate the amount of genomes in each group ( representing 84% of all genomes in our dataset (Supplementary Table  1a ). Based on read mapping, M.
We identified 94 viral populations in our genome datasets (Extended Data Fig. 8  and Supplementary Table  10a-c ). Of the identified proviruses, viral species representatives were found to be specific for Methanobrevibacter A, and one each for Methanomassiliicoccus and Methanosphaera spp. , and Methanomethylophilaceae UBA71. Although archaeal viruses in extreme environments were discovered in the early 1970s  34, 35  , little is known about nonextremophilic viruses in the highly abundant mesophilic environments, and only a few nonextremophilic archaeal viruses have been isolated so far  [36] [37] [38] [39]. To the best of our knowledge, no viruses/proviruses have been identified in the past infecting Methanomassiliicoccales and Methanobacteriales members of the human gut. 8 ). Genomes (strain level) from the human GIT are highlighted in green colours (taxon label).
M. smithii_A refers to the new species Ca. M. intestini. The bar on the left displays the origin: human (yellow bar), animal (shades of red) and plant (green). b-e, PCoA plots (Bray-Curtis distance) of protein profiles, according to: genome size (b), Methanobrevibacter clade according to the GTDB (c), assigned species (d) and geographical origin (e). NA, no data available. We explored the uniqueness of these 175 high-and mediumquality proviruses by comparing them with the latest comprehensive human Gut Virome Database (GVD)  3  , and the Viral Refseq Database, using the network-based viral classification tool vCon-TACT2 (ref. 40 ). However, none of the viruses clustered with any of the sequences in the databases. Due to the lack of similar archaeal viral genomes in the reference databases, the classification and further characterization of discovered archaeal viruses through metagenomic approaches remain challenging.
Discussion Our work adds original information on the biology of the GIT archaeome, by characterizing a collection of 1,167 nonredundant archaeal genomes. We were able to make initial associations between the diversity of gut-associated archaea with several demographic and geographic patterns. However, many geographic locations remain undersampled to date. As our genome collection is based on public datasets processed for the analysis of the bacterial component of the microbiome, a large number of archaeal species requiring specialized methods for cell lysis and DNA extraction 6 may be missing. Moreover, sequencing stool samples is not necessarily representative of the complete diversity of species in the intestines, because some archaea have been shown to form biofilms and stick to the epithelium  49.
Due to missing metadata and limited statistical power, it is challenging to establish significant associations between the archaeal genomic diversity and human lifestyles or diseases herein. Thus, experimentally driven, well-designed studies will ultimately elucidate the impact of archaea on human health  9. Moreover, incorporating both transcriptomics and proteomics data will further reinforce the genomic predictions and improve our understanding of the regulation of archaeal physiology and host adaptation. Future efforts should also seek to extend the dataset beyond the gastrointestinal environment, to other human body sites and hosts. Overall, our work contributes substantially to the understanding of the microbiome of the human GIT as a complex multi-domain bacterial, archaeal, fungal and viral network  [52] [53] [54] [55] [56].
Methods A resource summary is provided in Supplementary Table  14. Dataset description. To explore the diversity of archaea in human gastrointestinal samples, we compiled publicly available genomes from four recent collections of MAGs  2, [25] [26] [27] 57. Bri fly, the Unified Human Gastrointestinal Genome (UHGG) collection (data access June 2020, https://www. ebi. ac. uk/metagenomics/ genomes) holds published, nonredundant MAGs and isolates, collected from public repositories and associated metadata information (see ref. 2  for more details). No statistical methods were used to predetermine sample sizes. We additionally included published genomes from cultured archaea available in the National Center for Biotechnology Information (NCBI)  58  , Pathosystems Resource Integration Center (PATRIC)  59  and Integrated Microbial Genomes and Microbiomes (IMG/M)  60  repositories.
Genomes were compared using Mash v. 2. 1 (ref. 61 ) and, for genomes that were estimated to be identical and had a Mash distance of 0, only one was selected. In addition, we included genomes of 'Ca. Methanomethylophilus alvus'  15  and 'Ca. Methanomassiliicoccus intestinalis'  14  , as well as human gut-derived MAGs of Methanomassiliicoccales Mx02, Mx03 and Mx06, and additional 'Ca. M. intestinalis'  16  , and the human isolate Methanobrevibacter arboriphilus ANOR1 (ref. 42 ) to complete the dataset. Those genomes were assigned a genome accession no. (GUT_GENOME286998, GUT_GENOME287001, GUT_GENOME287002, GUT_GENOME287004), as given in Supplementary Table  1a. This brought the total number of genomes used for the analysis in the present study to 1,167. Data collection and analysis were not performed blind to the conditions of the experiments.
Read mapping was performed with Bowtie2 (ref. 64 ) for the genomes that had original raw reads available and were post-processed using samtools  65. Strain heterogeneity within each MAG was computed using the script 'polymut. py' from the CMseq tool (https://github. com/SegataLab/cmseq). Alignment files were used together with the parameters --minqual 30 and --cov 10, following the method description in refs. 2, 25. A threshold of ≤0. 5% indicates heterogeneity of assembly and the higher likelihood of one strain present per assembly. GUNC  63  was used to detect chimerism in all 1,167 genomes and resulted in a CSS of 0 for all genomes (Supplementary Table  1e ). A CSS closer to a value of 0 indicates that a genome is free of contamination and all genes are assigned to the same taxonomy, whereas a CSS score closer to 1 indicates chimerism.
The CSS, taken together with the contamination thresholds from CheckM, demonstrated that our 1,167 genomes were not chimeric in nature. DRep v. 2. 0. 0 (ref. 66 ) was used to dereplicate the complete dataset at 95% and 99% ANI values. The 95% ANI values were selected to separate between species boundaries (n = 27)  67. A cut-off of 99% was selected for strain delineation, provided that a stable number of clusters for MAGs >75% complete had <5% contamination (n = 98; Extended Data Fig. 3a ). Lower thresholds did not affect the number of strains recovered. The resulting strain and species representatives are given in Supplementary Table  1a-c. All genomes were taxonomically annotated following the procedure given in ref. 2. The taxonomic assignment was performed using the GTDB Toolkit v. 0. 3.
Genome annotation and protein catalogue. Protein-coding sequences (CDSs) were predicted and annotated with Prokka v. 1. 14. 5 (ref. 69 ) using the parameters '--kingdom Archaea' to include nonfragmented archaea-curated proteins from the UniProtKB database and '--rfam' to scan for noncoding RNAs. CDSs were further characterized using eggNOG-mapper v. 2. 0. 0 (ref. 70 ) and the eggNOG database v. 5. 0 (ref. 71 ), which includes the latest release of all archaeal clusters of orthologous groups and their proteins  72. The protein catalogue was generated by combining all predicted CDSs (total number 1,790,493) derived from the 1,167 nonredundant archaeal genomes. MMseqs2 linclust  73  was used to cluster the concatenated proteins dataset using the options '--cov-mode 1 -c 0. 8' (minimum coverage threshold of 80% the length of the shortest sequence) and '--kmer-per-seq 80'.
Relative abundance of archaea in human metagenomes. Raw read datasets (691) were obtained from studies of the human gut microbiome, out of which 691 (of 1,167) medium-or high-quality archaeal MAGs were assembled. The remainder was not made public by their original submitters (Supplementary Table  1a ). We mapped raw reads to the 27 reference archaeal species representatives using Bowtie2-align  64  and post-processed using samtools  65. The generated sorted mapping files were used to calculate the breadth of coverage. Breadth of coverage was calculated by dividing the total number of bases covered (using samtools mpileup) by the length of the reference genome. To get the percentage coverage breadth we multiplied the resulting number by 100. For measuring the relative abundance of the 27 archaeal species in the different metagenomics datasets we used CoverM (https://github.
com/wwood/CoverM) and the relative_abundance calculation method (Supplementary Table  2f ). Reads were additionally mapped using Kraken v. 2. 1. 2 (ref. 77 ) (with default settings) against (1) a custom database of the UHGG catalogue available from the MGnify FTP site (http://ftp. ebi. ac. uk/pub/databases/metagenomics/mgnify_ genomes/human-gut/v1. 0/uhgg_kraken2-db) and (2) a customized database of the 27 archaeal species representatives in our dataset because we supplemented the initial resource with additional isolates (Data description). Results were processed using Bracken v. 2. 5. 3 (ref. 78 ) using both read lengths 100 and 250 to estimate the relative abundance of domain-, family-and species-level taxa (Supplementary Table  2b-d ). We did not observe differences in the output values of the analysis between read lengths 100 and 250.