dataesr/scientific-paragraphs-categorization huggingface dataset:
Termite phylogenetic tree reconstruction We built a phylogenetic tree of termites using mitochondrial genomes retrieved from metagenome assemblies. Mitochondrial contigs derived from termites were identified using BLAST search (sequence length > 5000 and percent identity > 90)  [4]  against previously published whole mitochondrial genomes of termites  [13] [14] [15] 146]. Mitochondrial genomes were complete or near-complete in most cases. Each contig derived from mitochondrial genomes was annotated using the MITOS webserver  [7]. The 13 protein-coding genes, two rRNA genes, and 22 transfer RNA (tRNA) genes were aligned with MAFFT v 7. 305  [69]  using default settings. The alignments were concatenated, and the third codon position of protein-coding genes was removed. The dataset was partitioned into four subsets: one for the first codon position of protein-coding genes, one for the second codon position of protein-coding genes, one for the two rRNA genes, and one for the 22 tRNA genes.
Reconstruction of metagenome-assembled genomes We reconstructed metagenome-assembled genomes (MAGs) from metagenomes contigs using CONCOCT v 0. 4. 0  [3]  implemented in the metawrap software v 0. 9 [142] with default parameters. MAG quality checking, based on 43 single-copy marker genes (Table  S9 ), was performed with CheckM v 1. 0. 11  [103]. High-quality MAGs, medium-quality MAGs, and low-quality MAGs with upward of 30% completeness and downward of 10% contamination were retained (Table  S9 )  [17]. We retained low-quality MAGs that were at least 30% complete because, in some cases, they were endowed with complete pathways. Despite having fewer single-copy marker genes, 65. 35% of these MAGs possessed more than ten tRNA, and 17. 54% had at least one of the three rRNA genes. All MAGs that did not meet these criteria were discarded.
In addition, we discarded MAGs with obvious mismatches among marker genes. To identify these MAGs, we built Maximum Likelihood phylogenetic trees for all 43 single-copy marker genes with Fast-Tree v 2. 1. 11 [110]. MAGs that fall in different phyla for different marker genes were considered having obvious mismatches and were discarded. The rRNA genes were extracted using METAXA2 software  [6] , tRNA genes were predicted via tRNAscan-SE tool  [31] , and MAG coverage was calculated with the "metawrap quant_bins" command of the metawrap software  [142].
Taxonomic annotation The annotation of genomic features of bacterial and archaeal contigs and MAGs was carried out with Prokka v 1. 14 [122]. This step allowed the identification of coding sequences (CDS), rRNAs, and tRNAs, which were used in downstream analyses. To identify the taxonomy of the metagenome contigs, we taxonomically annotated single-copy marker genes and other protein-coding genes in contigs longer than 1000 bps. Forty single-copy marker genes were extracted using mOTU software ver1  [131, 150]. Single-copy marker genes were taxonomically annotated using DIAMOND BLASTp  [28]  with e-value ≤ 1e-24 and output format 102, which uses the lowest common ancestor algorithm for annotation. Other protein-coding genes were annotated using the same settings as marker genes but with DIAMOND BLASTx algorithm.
Both annotations were performed using the GTDB database ver 95 as a reference  [104]. Taxonomic annotation of MAGs was based on bacterial and archaeal reference trees using GTDB-Tk v1. 3. 0 based on GTDB ver 95  [32]. We used the genomic DNA extracted from whole termite guts to produce PCR amplicon sequences of the V4 region of the 16S rRNA gene. PCR reactions were carried out using the primer pairs 515F (XXXXXGTG TGY CAGCMGCC GCG GTAA, [102]) and 806R (XXXXXXXXCCG GAC TACNVGGG TWT CTAAT,  [5] ). All pairs of primers were endowed with unique dual tag indexes (8X overhang on the forward primer and 5X overhang on the reverse primer) to minimize the effects of index hopping between libraries. We conducted PCR amplification using Takara Tks Gflex DNA Polymerase with the following conditions: initial denaturation (3 min at 94 °C), 30 cycles of amplification (45 s at 94 °C, 60 s at 50 °C, and 90 s at 72 °C), and a terminal extension (10 min at 72 °C).
All PCR reactions were scaled down using one-half of the reagents recommended in the manufacturer's protocol. Prepared libraries were mixed in equimolar concentration and paired-end-sequenced on the Illumina MiSeq platform. The analysis of the 16S rRNA gene amplicon sequences was performed with mothur v1. 44. 1 [118]  following the standard procedure for Illumina data analysis described by Kozich et al. [71]. After removing low-quality reads and chimera, sequences were clustered into operational taxonomic units (OTUs) at a sequence similarity level of 97% using VSEARCH  [115]. Sequences were classified using the naïve Bayesian classifier  [145]  implemented in mothur and the SILVA reference database release 138  [111]. The abundance of every family inferred from 16S rRNA gene amplicon data and the 40 marker genes annotated from metagenomic data was then compared.
Functional annotation We carried out functional annotation of the CDSs identified with Prokka v. 1. 14. 5 [122] using the "metagenome" option for the annotation of metagenomes and MAGs. The "metagenome" option takes into account the fragmented nature of metagenomic data. We only performed functional annotation on contigs taxonomically annotated as bacteria or archaea. We used the CAZy database  [83]  as a reference to identify CDSs with carbohydrate metabolizing properties. Protein sequences were searched against a set of profile Hidden Markov models (HMM) representing CAZy domains deposited in the dbCAN database release 7  [155]. We used an e-value lower than e-30 and coverage greater than 0. 35 as thresholds to extract the best domain matches. Hydrogenases were annotated using HMM searches against the Pfam database version 32.
0  [43]  using an e-value cut-off of e-30. Catalytic subunits of hydrogenases were classified into different classes using the k-nearest neighbor algorithm implemented in the HydDB webtool  [124]. For the  [FeFe]  hydrogenase Group A4, we carried out a manual inspection of the conserved motifs in the protein sequence  [121]. We reconstructed prokaryotic metabolic pathways from our metagenomes with KOFam scan v. 1. 1. 0  [49, 68]. We used the KEGG database as a reference and e-value cut-off of e-30. Each protein sequence was annotated to the gene family level with the KEGG-Decode python module  [49]. The MAG metabolic pathways were annotated with KOFam scan v. 1. 1. 0 using default settings. Some gene families appeared to be absent from some MAGs after annotation against the KEGG database. To confirm the absence of these gene families, we carried out BLAST searches (amino acid identity > 60% and alignment length > 100 amino acids) against the Annotree protein sequence database  [85].
Relative abundance of gene families The relative abundance of CDSs was calculated by mapping the raw reads on the sequences. Briefly, the reads were mapped to the assembled contigs annotated as bacteria or archaea. Relative abundance was calculated for each CDS using Salmon v. 1. 4. 0 with the "meta" option. Salmon corrects GC-content bias, gene-length differences, and sampling effort  [126]. We used Transcripts per Million (TPM) values to calculate the relative abundance of CDSs. TPM is a normalization method typically used in studies comparing gene expression levels among transcriptomes. In the case of gut metagenomes, TPM reflects the abundance of a gene in the bacterial community rather than its expression level (or the relative abundance of transcripts). TPM values were retained for downstream analyses if they were embedded into contigs longer than 1000 bps and had a TPM value higher than 1.
This approach removes potentially spurious genes with low coverage. The log(TPM+1) values were calculated for visualization of the relative abundance data. Individual TPM counts were normalized using centered log(2)-ratio (clr) transformation for marker genes and genes of functional interest to account for the compositional structure and unequal numbers of reads in our metagenome data. Clr transformation enhances sub-compositional comparisons (gene vs gene, bacteria vs bacteria) and reduces spurious correlations. Positive and negative TPM values indicate positive and negative departures from the overall compositional mean, which is zero  [47]. Clr transformation of TPM values for marker genes and genes of functional interest was performed using the R package propr using 0. 65 as a pseudo count to account for zero values  [112].
Statistical analysis We investigated whether the abundance of the genes and pathways of interest were phylogenetically autocorrelated to the time-calibrated tree of termites. To do so, we calculated the Moran's I phylogenetic autocorrelation index using the R package phylosignal  [70]  on CDSs embedded in contigs longer than 1000 bps and with TPM value higher than 1. This analysis was carried out for each bacterial and archaeal phylum present in at least 5% of the metagenomes, using the combined 40 single-copy marker genes (see Table  S3 ). A 5% false discovery rate (FDR) correction was calculated according to the p. adjust function implemented in the R package stats  [113]. Similarly, we calculated the Moran's I phylogenetic autocorrelation index for every 211 CAZymes present in more than 10% of gut metagenomes and carried out a 5% FDR correction.
Finally, the analysis was performed for each gene involved in the reductive acetogenesis, sulfate-reducing, nitrogen recycling, and nitrogen fixating pathways. For the mcrABG gene of the methanogenesis pathway, the analysis was performed on the combined genes because their abundance was too low to be analyzed individually as other genes. We applied a 5% FDR correction. To examine whether the abundance of the genes and pathways of interest differed with the termite diet and the presence of non-prokaryotic co-symbionts, we performed phylogenetic ANOVA using the procD. pgls function implemented in the R package geomorph  [2]. A 5% FDR correction was calculated using the p. adjust function implemented in the R package stats  [113]. The termite diet was determined based on literature data  [12, 37]  and was considered made of wood or soil.
All metagenome contigs longer than 5000 bps with dinitrogen-fixing genes were also examined. We visualized termite samples according to the abundance of CAZyme families present in their gut metagenomes using Principal Component Analysis (PCA). The PCA was performed using the prcomp function implemented in the R package stats [113] and visualized using the R package ggbiplot  [144]. Similar analyses were performed on the genes involved in reductive acetogenesis, sulfate reduction, dissimilatory nitrate reduction, urea degradation, glutamate biosynthesis, arginine biosynthesis, ammonia transport, nitrogen fixation, and mcrABG genes of the methanogenesis pathway.
Uricase genes encoded by termites We searched the 53 termite transcriptomes previously published by Bucek et al. [27]  for the presence of uricases. These transcriptomes were either derived from whole worker bodies or worker heads, and included species of all termite families. Protein sequences of predicted uricases from termites (XP_023702357, GFG34960), cockroaches (PSN45555, CDO39394), fireflies (KAF529609, XP_031344605), sawflies (XP_015591878, XP_015521616), ant (XP_011159093), fruit fly (NP_476779), and rat (NP_446220) were used as a query in TBLASTn searches. The longest open reading frames for all significant TBLASTn search hits (e-value < 10 -30 ) were identified and translated using hmmer2go obtained from https:// github. com/ sesta ton/ HMMER 2GO. The nonsense proteins that did not provide any significant BLASTx hit against the NCBI RefSeq database (e-value < 10 -10 ) were discarded.
The remaining predicted protein sequences, derived from 23 transcripts, were assigned KEGG annotations using eggNOG-Mapper version 4. 5  [57]. The protein sequences were aligned using CLUSTAL W  [77] , and the alignment was visually inspected. transcripts per million (TPM). Table  S6. Moran's I phylogenetic autocorrelation index calculated for 211 prokaryotic CAZymes present in more than 10% of gut metagenomes. Significance was assessed with 9999 random permutations. P-values <0. 05 are indicated by asterisks. Table  S7. Phylogenetic ANOVA calculated for 211 prokaryotic CAZymes present in more than 10% of gut metagenomes. Significance was assessed with 9999 random permutations. P-values of phylogenetic ANOVA and pairwise comparisons were adjusted at 5% false discovery rate (FDR). The relative abundance of each CAZyme for the four termite groups are indicated by mean TPM values.
Asterisks indicate genes that were annotated using BLASTx search against the AnnoTree database (perc. identity >60%, align. length >100 aa). Other genes were annotated using HMM search against the KEGG or Pfam databases. [FeFe] hydrogenase GroupA4 were annotated using the Hyddb webtool followed by manual inspection of the conserved motifs. The total number of HycB3 (PF13247) found in each MAG is shown. MAGs with almost complete reductive acetogenesis pathway (>5 genes) and HDCR complex are highlighted in grey. Table  S13. Relative abundance of methyl-coenzyme M reductase (mcrABG) gene complex present in metagenome contigs longer than 5000 bps. Contigs were annotated using BLASTx search against the GTDB database. Relative abundance of the gene family is shown as raw TPM. Table  S14. Distribution of genes involved in methanogenesis among MAGs.
Distribution is shown as presence (1) and absence (0). Asterisks indicate genes that were annotated using BLASTx search against the AnnoTree database (perc. identity >60%, align. length >100 aa). Other genes were annotated using HMM search against the KEGG or Pfam databases. Highlighted MAGs have a complete Methanogenesis pathway. Table  S15. Distribution of genes involved in sulfate reducing among MAGs. Distribution is shown as presence (1) and absence (0). Asterisks indicate genes that were annotated using BLASTx search against the AnnoTree database (perc. identity >60%, align. length >100 aa). MAGs with complete sulfate reducing pathway are highlighted. Table  S16. Genes involved in nitrogen metabolism and fixation found in our MAGs. Distribution is shown as presence (1) and absence (0).
Asterisks indicate genes that were annotated using BLASTx search against the AnnoTree database (perc. identity >60%, align. length >100 aa). MAGs with complete nitrogen fixation or dissimilatory nitrate reduction pathways are highlighted. Table  S17. Contigs endowed with a NifHDKENB (nifHDKENB, vnfHDKENB, or anfHDKENB) gene complex found in gut metagenomes. The relative abundance is given as raw TPM. Table  S18. Contigs endowed with a NifHDK (nifHDK, vnfHDK, or anfHDK) gene complex found in termite gut metagenomes. The relative abundance is given as raw TPM. Table  S19. Fossil calibrations used to calibrate the time-calibrated tree of termites.
Availability of data and materials Raw sequence data generated in this study have been deposited on MG-RAST (https:// www. mg-rast. org/ mgmain. html? mgpage= proje ct& proje ct= mgp10 1108) (see Table  S1  for individual IDs). MAGs generated in this study, their metadata, and the spreadsheets used to generate the main figures are available on Figshare (https:// figsh are. com/ proje cts/ EGU-The-funct ional-evolu tion-of-termi te-gut-micro biota/ 126695). The scripts used in this study are available on github (https:// github. com/ oist/ EGU-The-funct ional-evolu tion-oftermi te-gut-micro biota).
, Melville, NY, USA). Fluorescently labeled paclitaxel (Flutax-1) was visualized at an excitation wavelength of 488 nm and detection at 525/50 nm. To measure the depth of drug penetration, confocal images were analyzed using ImageJ software (NIH). Specifically, the length of penetration for each section was measured from the IEL to maximum penetration depth and normalized to the thickness of the medial layer or wall thickness.
Statistical analysis. All values were expressed as mean ± standard deviation (SD). Continuous variables were compared between groups using one-way analysis of variance (ANOVA) using GraphPad Prism 9 (Graph-Pad Software, La Jolla, CA, USA). A value of p ≤ 0. 05 was considered statistically significant. If statistical significance was shown, comparison of quantitative data of multiple groups was performed by Tukey's multiple comparisons post hoc test.
2. 2. Design. An observational, retrospective, single-center study was conducted in the adult ED of the Reims University Hospital (France) from January 1 st to December 31 st , 2018. All PCT measurements from the ED during this period were considered. Duplicates, dosages in patients under 18 years old, and patient files with relevant missing data were excluded. In a second time, patients with active malignancy or with the final noninfectious diagnosis were excluded as well. Biological data were collected from the dedicated software of the biochemistry department (Haemonetics software). ey included PCT, C-reactive protein (CRP), leukocytes, and neutrophil levels measured at the same time in the ED. PCT and CRP concentrations were performed with a Cobas 8000 biochemistry analyzer (Roche diagnostic).
e serum concentrations of PCT were measured by Elecsys BRAHMA PCT electrochemiluminescence immunoassay, and the serum concentrations of CRP were determined by latex-enhanced turbidimetric immunoassay according to the manufacturer's instructions. Leukocytes and neutrophils levels were measured with an ADVIA analyzer (Siemens-Healthineers). Blood cultures were carried out using a BACTALERT analyzer (Biomérieux). Clinical data were collected from digitalized patient files (Easily, SILPC) related to the same ED visit. ey included demographic data, ED nurse triage level using the FRench Emergency Nurses Classification in Hospital scale (FRENCH)  [18] , initial vital signs, patient comorbidities, and the infection site. e final infectious disease diagnosis, the need for hospitalization, and the occurrence of the outcomes were further collected for each patient.
e model was adjusted for age, gender, comorbidities, and infection site. e discriminative power of PCT was assessed for each outcome, using the area under the receiver operating characteristics curve (AUC) analysis for the whole cohort and for selected subgroups stratified by gender, age, FRENCH level, temperature, systolic blood pressure (SBP), Glasgow Coma Scale (GCS) level, and infection site. Forest plots were used to present the data. Analyses were performed using SAS, version 9. 4 (SAS Institute, Inc. , Cary, NC, USA).
Ribosome profiling data analysis Aligned reads were counted by either a Python package HTSeq (htseq-count) or an R package GenomicAlignments (summerizeOverlaps). Annotations and sequencing reads were handled using an R package GenomicFeatures. To avoid multiple counting on isoforms, transcript reference data were processed to have one unique annotation covering all isoforms (union of isoforms) per gene. Reads only in the CDS regions were counted. Transcripts with low read counts (,128 reads) were discarded. An R package DESeq (1. 20) was used for calculating normalized expression from either ribosome footprinting or RNA-seq data based on a negative binomial distribution and generalized linear model. For the mouse data, replicates were initially analyzed independently to confirm reproducibility, and then analyzed in combination for the final analysis.
For the human neuron data, biological triplicates were handled by DESeq. Translation efficiency (TE) was calculated based on the DESeq expression output. 59 Untranslated region (UTR) estimated folding energy table was extracted from the UCSC genome database (fold5UTR field: mm9). For the 59UTR estimated folding energy comparison, a control group with similar group size was randomly selected for each comparison to avoid potential bias from sample size differences. Transcript coordinates were calculated by a custom R script and re-aligned based on the rounded half point of the ribosome footprint [59 end 1 (footprint length/2)]. For icSHAPE data analysis, icSHAPE (in vivo) results from mouse ES cells (GEO: GSE64169) were downloaded, converted to mm9 (UCSC liftOver), and merged with our mouse ribosome profiling data.
A series of hyperpolarizing and depolarizing step currents were injected to elicit APs. For wholecell calcium currents, the external solution used contained 140 mM tetraethylammonium methanesulfonate (TEA-MeSO 3 ), 10 mM HEPES, and 10 mM BaCl 2 or CaCl 2 (pH 7. 4, 300-310 mOsmol/kg). The pipette solution contained 135 mM CsMeSO 3 , 5 mM CsCl, 1 mM MgCl 2 , 4 mM MgATP, 5 mM HEPES, and 5 mM EGTA (pH 7. 3, 290-300 mOsmol/kg). Currents were recorded by holding the cell at -90 mV, before stepping to various potentials from -60 to 150 mV for 250-ms in 10-mV increments. Tetrodotoxin (TTX; 1 mM) was used to block voltage-gated sodium currents. Data were acquired by PatchMaster software (HEKA Elektronik), sampled at 10 kHz, and filtered at 2. 9 kHz. APs and calcium currents were analyzed using Clampfit 10.
3E-J ; Extended Data Fig. 3-1 ). Since the CrPV IRES does not require any initiation factors to recruit ribosomes, the results indicate that the translational effects of G2019S LRRK2 are independent of translation initiation factors, and phosphorylation of S15 is sufficient to enhance the translation of mRNAs with structured 59UTRs. In scatterplots, centerline is a guideline with slope of 1, meaning that the dots on the line do not have TE value differences between the genotypes. SD of TE differences: 0. 226 (GS LRRK2 vs control), 0. 179 (GS/DA LRRK2 vs control), 0. 273 (LRRK2 KO vs WT). Standard z score was calculated, and 61. 5 cutoff was used to select TE up and TE down genes. Triplet periodicity is normal across the results (Extended Data Fig. 1-1 ). E, F, Histogram of TE differences (d TE, DTE) between (E) GS LRRK2 TG and non-TG control or (F) LRRK2 KO and WT.
Since Atf4 uORF footprint abnormality is observed in the LRRK2 KO brain and Atf4 induction is a central downstream pathway of ISR, we further sought to determine a potential relationship between G2019S LRRK2 and ISR. First, eIF2A phosphorylation levels in the G2019S LRRK2 transgenic brain were examined. We found no steadystate induction of eIF2A phosphorylation regardless of the transgene expression levels (Extended Data Fig. 4-1A ). Next, we tested the potential relationship by inducing ISR in G2019S LRRK2-expressing primary neurons cultured from the transgenic mouse model. Of note, G2019S LRRK2-expressing neurons have defective recovery from thapsigargin-induced ISR (Extended Data Fig. 4-1B ). Considering the enhanced translation of structured 59UTR-containing transcripts in the G2019S LRRK2 brain, this defected recovery could be because of translational defects caused by G2019S LRRK2 inhibiting 59UTR-mediated translational regulation required for ISR recovery.
In addition, since thapsigargin induces ISR by blocking SERCA, defective calcium handling in G2019S LRRK2 neurons could also exacerbate ISR. A previous study suggested that dysregulated translation leads to increased calcium influx in G2019S LRRK2 human dopamine neurons  (Kim et al. , 2020). In this regard, we performed calcium recordings with the G2019S LRRK2-expressing brain. Basic electrophysiological properties including spontaneous and evoked AP wave of SNpc dopamine neurons are indifferent to G2019S LRRK2 expression in the brain (Extended Data Fig. 5-1 ). Substantia nigra dopamine neurons also show similar pacemaking activities compared with the wild type (Fig. 5A ). However, calcium currents measurement showed significant increase of calcium currents in the G2019S LRRK2-expressing brain (Fig.