dataesr/scientific-paragraphs-categorization huggingface dataset:
Identification of Helarchaeota genomes from GB sediments. We recently obtained more than ~280 gigabases of sequencing data from 11 samples taken from various sites and depths at GB hydrothermal vent sediments  20. De novo assembly and binning of metagenomic contigs resulted in the reconstruction of over 550 genomes (>50% complete)  20. these genomes we detected a surprising diversity of archaea, including >20 phyla, which appear to represent up to 50% of the total microbial community in some of these samples  20. A preliminary phylogeny of the dataset using 37 concatenated ribosomal proteins revealed two draft genomic bins representing a previously unknown lineage of the Asgard archaea. These draft genomes, referred to as Hel_GB_A and Hel_GB_B, were re-assembled and re-binned resulting in final bins that were 82% and 87% complete and had a bin size of 3.
These results add support to the phylum level distinction observed for Hel_GB_A and Hel_GB_B in both the ribosomal protein and 16S rRNA phylogenetic trees. We propose the name Helarchaeota after Hel, the Norse goddess of the underworld and Loki's daughter for this lineage. Metabolic analysis of Helarchaeota. To reconstruct the metabolic potential of these archaea, the Helarchaeota proteomes were compared to several functional protein databases  20  (Fig. 3a ). Like many archaea in marine sediments  23  , Helarchaeota may be able to utilize organic carbon as they possess a variety of extracellular peptidases and carbohydrate degradation enzymes that include the β-glucosidase, α-L-arabinofuranosidase and putative rhamnosidase, among others (Supplementary Table  4  and Supplementary Data 1). Degraded organic substrates can then be metabolized via glycolysis and an incomplete TCA cycle from citrate to malate and a partial gamma-aminobutyric acid shunt (Fig.
3a , Supplementary Data 1). Both Helarchaeota bins are missing fructose-1,6-bisphosphatase and have few genes coding for the pentose phosphate pathway. Genes encoding for the bifunctional enzyme 3-hexulose-6-phosphate synthase/6-phospho-3-hexuloisomerase (hps-phi) were identified in Hel_GB_B suggesting they may be using the ribulose monophosphate pathway for formaldehyde anabolism. Genes coding for acetate-CoA ligase (both APM and ADP-forming) and an alcohol dehydrogenase (adhE) were identified in both genomes suggesting that the organisms may be capable of both fermentation and production of acetyl-CoA using acetate and alcohols (Supplementary Data 1). Like in Thorarchaeota and Lokiarchaeota, these genomes possess the large subunit of type IV ribulose bisphosphate carboxylase  19, 24. In addition, the Helarchaeota genomes encode for the catalytic subunit of the methanogenic type III ribulose bisphosphate carboxylase used for C-fixation  24    Numbers under each column correspond to the InterPro accession number (IPR) and Archaeal Clusters of Orthologous Genes (arcCOG) IDs that were searched for.
However, we could not identify any obvious syntrophic partner organisms based on cooccurrence analyses of abundance profiles in metagenomic datasets generated in this study  20. An evaluation of traditional energy transferring mechanisms showed that the Helarchaeota bins lack genes coding for NADH: ubiquinone oxidoreductase, F 420 -dependent oxidoreductase, F 420 H 2 :quinone oxidoreductase and NADH:quinone oxidoreductase that were identified in Ca. S. butanivorans (Fig. 4 )  7. These protein complexes are important for energy transfer across the cell membrane and are common among syntrophic organisms  2, 28, 29. Helarchaeota also lack genes coding for pili or cytochromes that are often involved in direct electron transfer to a bacterial partner, as demonstrated for different ANME archaea  26, 30.
a In Hel_GB_A the complex was found on the reverse strand but has been oriented in the forward direction for clarity (asterisk). Arrows indicate the length of the reading frame. Gene names were predicted by various databases (Supplementary Methods). Small numbers located above the arrows refer to the nucleotide position for the full contig. Bold numbers on Hel_GB_B refer to the amino acid number of the whole complex. b Figure depicts the membrane motifs identified on NODE_147_length_7209_cov_4. 62199_5, 6, and 7 using various programs (Supplementary Methods). Each circle represents a single amino acid. Bold circles represent amino acids at the start of the protein, the start and end of the transmembrane sites, and the end of the complex. Numbering corresponds to the amino acid numbers of Hel_GB_B in panel a.
In addition, reads matching Illumina artifacts or phiX were discarded. The remaining reads were mapped to a masked version of the human HG19 with BBMap and all hits over 93% sequence identity to the human genome were discarded. Trimmed, screened, paired-end Illumina reads were assembled using the megahit assembler using a range of kmers. Assemblies were preformed with default parameters in megahit with the following options: "-k-list 23,43,63,83,103,123". High-quality reads were mapped to the final assembly to calculate coverage information using bbmap by excluding all parameters except ambiguous=random as described by JGI. Genome reconstruction. The contigs from the JGI assembled data were binned using ESOM  33  , MetaBAT  34  , and CONCOCT  35  and resulting bins were combined using DAS Tool (version 1.
0)  36. For ESOM, binning was performed on contigs with a minimum length of 2000 bp using the K-batch algorithm for training after running the perl script esomWrapper. pl  33. Emerging self-organizing maps (ESOM) were manually sorted and curated. The bins were extracted using getClassFasta. pl (using -loyal 51). Reference genomes were included to add genetic signatures for the assembled contigs and improve binning. For CONCOCT, Anvi'o (v2. 2. 2) was used as the metagenomic workflow pipeline  37. Coverage information was obtained by mapping all high-quality reads of each sample against the assembly of another sample using the BWA-MEM algorithm in paired-end mode (bwa-0. 7. 12-r1034; using default settings)  38. The resulting sam file was sorted and converted to bam using samtools (version 0.
1. 19)  39. The bam file was prepared for Anvi'o using the script anvi-init-bam and a contigs database generated using anvi-gen-contigsdatabase. These files were the input for anvi-profile. Generated profiles for the assemblies were combined using anvi-merge and the resulting bins summarized using anvi-summarize (-C CONCOT)  37. If not mentioned otherwise, the scripts were used with default settings. Metabat was also used as a binning approach (v1)  34. As described for Anvi'o the input consisted of the scaffold files (≥2000 bp) and the mapping files. First, each of the mapping files were summarized using jgi_summarize_bam_contig_depths and then metabat was run using the following settings: -minProb 75 -minContig 2000 -minContigByCorr 2000. Results from the three different binning tools were combined using DAS Tool (version 1.
fasta -o G9 -pre_correction -mink 75 -maxk 105 -seed_kmer 55 -num_threads 30". Metaspades was run on Raw data and Metabat assembled bins using as follows: "metaspades. py -12 Guay16. 11400. 5. 204 846. CTCTCTA-CGTCTAA. filter-METAGENOME. fastq -o Metaspades -onlyassembler -meta". Binning procedures (using scaffolds longer than 2000 bp) as described above for the original bins were repeated with these redone assembles. All bins were compared to the original Helarchaeota bins using blastn  42  for identification. Mmgenome  43  and CheckM  40  were used to calculate genome statistics (i. e. , contig length, genome size, contamination, and completeness). The highest quality Helarchaeota bin from each sample was chosen for further analyses. For the 4572-4 dataset, the best bin was generated using the Metaspades reassembly on the trimmed data and for the 4569-2 dataset the best bin was recovered using the Metaspades reassembly on the original Hel bin contigs.
The final genomes were further cleaned by GC content, paired-end connections, sequence depth and coverage using Mmgenome  43. CheckM was rerun on cleaned bins to estimate the Hel_GB_A to be 82% and Hel_GB_B to be 87% complete and both bins were characterized by a low degree of contamination (between 1. 4 and 2. 8% with no redundancy) (Table  1 )  40. Genome size was estimated to be 4. 6 Mbp for Hel_GB_A and 4. 1 for Hel_GB_B and was calculated using percent completeness and bin size to extrapolate the likely size of the complete genome. CompareM was used to analysis differences between Helarchaeota bins and published Asgard bins using the command python comparem aai_wf -tmp_dir tmp/ -file_ext fa -c 8 aai_-compair_loki aai_compair_loki_output (https://github. com/dparks1134/Compare M). Read abundance summarized by jgi_summarize_bam_contig_depths were used to calculate relative read abundance and total percent of metagenomic reads.
fasta -db Hel_16s -out Hel_p-ossible_16s_blast. txt -evalue 1E-20) to recently acquired 16S rRNA gene sequences from MAGs recovered from preliminary data from additional GB sites. A 37 Phylosift 41 marker genes tree was used to assign taxonomy to these MAGs. We were able to identify five MAGs that possessed 16S and that formed a monophyletic group with our Hel_GB bins (Supplementary Table  2 ; Megxx in Fig. 2 ). Of the unbinned 16S rRNA gene sequences one was identified as likely Helarchaeota sequence. The contig was retrieved from the 4572_4 assembly (designated Ga0180301_10078946) and was 2090 bp long and encoded for an 16S rRNA gene sequence that was 1058 bp long. Given the small size of this contig relative to the length of the 16S rRNA gene none of the other genes on the contig could be annotated.
Blastn  42  comparison to published Asgard 16S rRNA gene sequences was performed using the following command: blastn -outfmt 6 -query Hel_p-ossible_16s. fasta -db Asgrad_16s -out Hel_possible_16s_blast. txt -evalue 1E-20 (Supplementary Table  2 ). The GC content of each 16S rRNA gene sequence was calculated using the Geo-omics script length+GC. pl (https://github. com/Geoomics/scripts/blob/master/AssemblyTools/length%2BGC. pl). For a further phylogenetic placement, the 16S rRNA gene sequences were aligned to the SILVA database (SINA v1. 2. 11) using the SILVA online server  44  and Geneious (v10. 1. 3)  45 as used to manually trim sequences. The alignment also contained 16S rRNA gene sequences from the preliminary Helarchaeota bins. The cleaned alignment was used to generated a maximum-likelihood tree with RAxML as follows: "/raxmlHPC-PTHREADS-AVX -T 20 -f a -m GTRGAMMA -N autoMRE -p 12345 -x 12345 -s Nucleotide_alignment.
phy -n output" (Fig. 1b ). Phylogenetic analysis of ribosomal proteins. For a more detailed phylogenetic placement, we used BLASTp  46  to identify orthologs of 56 ribosomal proteins in the two Helarchaeota bins, as well as from a selection of 130 representative taxa of archaeal diversity and 14 eukaryotes. The full list of marker genes selected for phylogenomic analyses is shown in Supplementary Table  6. Individual protein datasets were aligned using mafft-linsi  47  and ambiguously aligned positions were trimmed using BMGE (-m BLOSUM30)  48. Maximum likelihood (ML) individual phylogenies were reconstructed using IQtree v. 1. 5. 5  49  under the LG+C20+G substitution model with 1000 ultrafast bootstraps that were manually inspected. Trimmed alignments were concatenated into a supermatrix, and two additional datasets were generated by removing eukaryotic and/or DPANN homologs to test the impact of taxon sampling on phylogenetic reconstruction.
For each of these concatenated datasets, phylogenies were inferred using ML and Bayesian approaches. ML phylogenies were reconstructed using IQtree under the LG+C60+F+G +PMSF model  50. Statistical support for branches was calculated using 100 bootstraps replicated under the same model. To test robustness of the phylogenies, the dataset was subjected to several treatments. For the "full dataset" (i. e. , with all 146 taxa), we tested the impact of removing the 25% fastest-evolving sites, as within a deep phylogenetic analysis, these sites are often saturated with multiple substitutions and, as a result of model-misspecification can manifest in an artifactual signal  [51] [52] [53]. The corresponding ML tree was inferred as described above. Bayesian phylogenies were reconstructed with Phylobayes for the dataset "without DPANN" under the LG+GTR model.
For Bayesian analyses, four chains were run in parallel, sampling every 50 points until convergence was reached (maximum difference < 0. 07; mean difference < 0. 002). The first 25% or the respective generations were selected as burn-in. Phylobayes posterior predictive values were mapped onto the IQtree using sumlabels from the DendroPy package  59   Metabolic analyses. Gene prediction for the two Helarchaeota bins was performed using prodigal 60 (V2. 6. 2) with default settings and Prokka 61 (v1. 12) with the extension "-kingdom archaea". Results for both methods were comparable and yielded a total of 3574-3769 and 3164-3287 genes for Hel_GB_A and Hel_GB_B, respectively, with Prokka consistently identifying fewer genes. Genes were annotated by uploading the protein fasta files from both methods to KAAS (KEGG Automatic Annotation Server) for complete or draft genomes to assign orthologs  62.
Files were run using the following settings: prokaryotic option, GhostX, and bidirectional best hit (BBH)  62. Additionally, genes were annotated by JGI-IMG  63  to confirm hits using two independent databases. Hits of interest were confirmed using blastp on the NCBI webserver  46. The dbCAN  64  and MEROPS 65 webserver were run using default conditions for identification of carbohydrate degrading enzymes and peptidases respectively. Hits with e-values lower than e -20 were discarded. In addition to these methods an extended search was used to categorize genes involved in butane metabolism, syntrophy and energy transfer. Identified genes predicted to code for putative alkane oxidation proteins were similar to those described from Candidatus Syntrophoarchaeum spp. Therefore, a blastp  46  database consisting of proteins predicted to be involved in the alkane oxidation pathway of Ca.
Syntrophoarchaeum was created in order to identify additional proteins in Helarchaeota, which may function in alkane oxidation. Positive hits were confirmed with blastp  46  on the NCBI webserver and compared to the annotations from JGI-IMG  63  , Interpro 66 , Prokka  61  , and KAAS 62 annotation. Genes for mcrABG were further confirmed by a HMMER  67  search to a published database using the designated threshold values  68  and multiple MCR trees (see Methods). To confirm that the contigs with the mcrA gene cluster were not missbined, all other genes on these contigs were analyzed for their phylogenetic placement and gene content. The prodigal protein predictions for genes on the contigs with mcrA operons were used to determine directionality and length of the potential operon. To identify genes that are involved in electron and hydrogen transfer across the membrane, a database was created of known genes relevant in syntrophy that were download from NCBI.
The protein sequences of the two Helarchaeota genomes were blasted against the database to detect relevant hits (E-value ≥ e -10 ). All hits were confirmed using the NCBI webserver, Interpro, JGI-IMG, and KEGG. Hydrogenases were identified by a HMMER search to published database using the designated threshold values. Hits were confirmed with comparisons against JGI annotations and NCBI blasts, the HydDB database  69  and a manual database made from published sequences  70, 71. All detected hydrogenases were used to generate two phylogenetic trees, one for proteins identified as small subunits and one for large subunits in order to properly identify the different hydrogenase subgroups. Hydrogenases that are part of the proposed complex were then further analyzed to evaluate if this was a possible operon by looking for possible transcription factors and binding motifs (Supplementary Methods).
Acknowledgements This study was supported in part by an  Alfred P. Sloan Foundation  fellowship ( FG-2016-6301 ) and  National Science Foundation Directorate of Biological Sciences (Systematics and Biodiversity Sciences)  ( Award 1737298 ) to  B. J. B. Sampling  in  Guaymas Basin  and post-cruise work was supported by  NSF  Awards  OCE-0647633  and  OCE-1357238  to APT, respectively. 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  provided to N. D. A. S. was supported by a  Marie Curie IEF   European grant  ( 625521 ), a  VR starting grant  ( 2016-03559 ) and a  WISE  fellowship by the  NWO-I Foundation of the Netherlands Organisation for Scientific Research.
L. E. was funded by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No  704263. This work was supported by grants of the  European Research Council  ( ERC Starting  grant  310039-PUZZLE_CELL ), the  Swedish Foundation for Strategic Research  ( SSF-FFL5 ) and the  Swedish Research Council  (VR grant  2015-04959 ) to T. J. G. E.
Data availability The raw reads from the metagenomes described in this study are available at JGI under the IMG genome IDs 3300014911 and 3300013103 for samples 4569-2 and 4571-4, respectively. Genome sequences are available at NCBI under the Accession numbers SAMN09406154 and SAMN09406174 for Hel_GB_A and Hel _GB_B, respectively. Both are associated with BioProject PRJNA362212.
Helarchaeota are metabolically distinct from Lokiarchaeota as both Hel_GB draft genomes appear to lack a complete TCA cycle Informational DN A po lym er as e To po iso me ra se IB RN A po lym er as e su bu nit * RN A po lym er as e su bu nit RP B8 Ri bo so ma l pr ote in L2 2e Ri bo so ma l pr ote in L2 8e ES CR T-I: Vp s2 8 ES CR T-I: ste ad ine ss bo x do ma in ES CR T-II: Vp s2 2/3 6-lik e (E AP 30 ) ES CR T-II: Vp s2 5 ES CR T-III: Vp s2 /24 /46 ES CR T-III: Vp s2 0/3 2/6 0 Lo ng in-do ma in pr ote in Ro ad blo ck /R LC 7 TR AP P do ma in Se c2 3/S ec 24 Co ns er ve d lok iac tin s Ac tin -re lat ed pr ote in AR P 2/3 co mp lex su bu nit 4 Ge lso lin Pr ofi lin Tu bu lin RW D do ma in Ub -a cti va tin g en zy me E1 Ub -a cti va tin g en zy me E1 -ca tal yti c E2 -fin din g do ma in Pu tat ive E2 -lik e pr ote in Ub iqu itin -d om ain pr ote in Pu tat ive E3 -lik e pr ote in UF M1 -d om ain pr ote in Pu tat ive de ub iqu itin ati ng en zy me Ri bo ph or in I OS T3 /O ST 6 ST T3 Helarchaeote GB_A Helarchaeote GB_B Lokiarchaeote CR_4 Lokiarchaeum GC14_75 Odinarchaeote LCB_4 Thorarchaeote SMTZ-83 Thorarchaeote AB_25 Thorarchaeote SMTZ-45 Heimdallarchaeote AB_125 Heimdallarchaeote LC_2 Heimdallarchaeote LC_3 Heimdallarchaeote CS_D IPR029703 arCOG08649 arCOG04256-7 arCOG04271 IPR002671 IPR029004 IPR007143 IPR017916 IPR007286 IPR014041/008570 IPR005024 IPR005024 IPR011012 IPR004942 IPR007194 IPR007194 IPR004000/020902 IPR004000 IPR008384 IPR005455 IPR007122 IPR002453 IPR029071/000626 IPR006575 IPR000594 IPR019572 IPR014929 IPR000608 IPR013083 IPR018611 IPR000555 IPR007676 IPR021149 IPR003674.
Human adipose tissue analyses Human adipose samples (subcutaneous and omental) were obtained from the Boston Nutrition Obesity Research Center (BNORC) adipose tissue core. Biopsies of scWAT and omental adipose were taken from patients during elective surgery. All patients were either non-diabetic or pre-diabetic, with the exception of four diabetic individuals, 2 of whom were in the BMI cohort and 2 in the aged cohort (indicated by asterisks in data plots; see S1-S3 Tables for relevant patient data). Frozen and fixed tissue (from only a subset of patients) samples were obtained for analyses, including histology and western blotting.
Tissues were then incubated with fluorescent secondary antibodies overnight and washed again in 1XPBS on a rotating platform at 4˚C replacing PBS every 1hr for a total of 4-6hrs. Primary antibodies included: neurofilament-M (2H3, 1:500) and synaptic vesicles (SV2, 1:250) from Developmental Studies Hybridoma Bank, (University of Iowa, USA). Secondary antibodies included: Alexa Fluor 488 at 1:500 (A21121) and alpha-bungarotoxin (BTX)-conjugated to Alexa Fluor 594 at 1:1000 (B13423) from Molecular Probes (Eugene, OR, USA). Tissues were next mounted on microscope slides using Millipore mounting fluid (Burlington, MA USA; Cat. # 5013) and 1 1/5 coverslips then sealed and allowed to set overnight. All images were acquired with Nikon Eclipse E400 epiflourescent microscope using an integrated 'Real Time Manual EDF' acquisition tool in Nikon Elements Software (Nikon, Minato, Tokyo, Japan).
Statistical analysis For all animal experiments, mice were randomized to treatment groups to ensure no difference in starting body weight. All plots represent mean +/-SEM. Statistical calculations were carried out in Excel or GraphPad Prism software (La Jolla, CA, USA), utilizing ANOVA, Linear Regression, or Student's t-test as indications of significance (specified in Figure  legends ). Gene and protein expression data were normalized to a housekeeper and analyzed by either ANOVA or by Student's t-test, two-tailed, using Welch's correction when variance was unequal. Error bars are SEMs. For all figures, � p < 0. 05, �� p < 0. 01, ��� p < 0. 001, ���� p < 0. 0001.
iBAT gross morphology also revealed a marked increase in lipid accumulation, also called "whitening", in both male and female mutant BTBR mice, but most prominently in male mice that exhibited the stronger neuropathy phenotype at this age (Fig  2C ). For BTBR MUT mice, iBAT gene expression showed a significant decrease in Ucp1, whose activation by the SNS is necessary for thermogenesis to occur, along with reductions in the brown adipocyte markers Cidea and Dio2, despite no decrease in synaptic markers  (   and 2G ). Together, these data indicate depot-specific differences related to innervation with obesity/diabetes and suggest that different mechanisms may be involved in maintaining nerve health in different adipose depots. To determine whether peripheral neuropathy extended beyond adipose in BTBR MUT mice, we examined the neuromuscular junctions (NMJ).
Percent of total NMJs for WT and MUT animals in both MG and SOL muscles was calculated as an indicator of neuropathic state (i). Analysis shows multiple t-tests (per row) of replicate cohorts; N = 7 for WT MG, N = 6 for MUT MG, N = 7 for WT SOL, and N = 4 for MUT SOL. Micro CT analysis of BTBR WT and MUT femurs (j). Bone volume density shown as fraction of bone volume/total volume (BV/TV, top left panel); total bone mineral density (BMD) for trabecular bone (Tb) in top right panel; bone area/total area (BA/TA) bottom left panel; and cortical (Ct) thickness (Th) in bottom right panel (j). All animals were male, 12-13 weeks old, N = 5 for WT, N = 4 for MUT. Data analyzed by two-tailed Student's T-test. Error bars are SEMs. � p < 0. 05, �� p < 0. 01, ��� p < 0. 001, ���� p < 0. 0001. In human scWAT, protein levels of panneuronal marker PGP9.
https://doi. org/10. 1371/journal. pone. 0221766. g005 the subiliac lymph node (white arrow; red arrows point to branches of the thoracoepigastric vein). This staining pattern is similar to the staining pattern we observed with the other panneuronal marker, PGP9. 5. The posterior end (right of subiliac lymph node in image) of the tissue did not appear to have as much innervation as the anterior portion (left of subiliac lymph node in image) or as the area surrounding the subiliac lymph node. After cold exposure, a distinct change in the neural arborization pattern was observed (Fig 7B, middle panel), particularly an increase in intensity around the lymph node. Since these 2D representations of 3D data do not convey accurate changes in neurite density, tissue z-stacks were analyzed for mean innervation density (calculated as total axon length normalized to tissue weight) and this revealed a trend for increased innervation density in the 10 day cold exposed group compared to RT and rewarmed groups (Fig 7B, right panel).