dataesr/scientific-paragraphs-categorization huggingface dataset:
Intergenic region The risk genotype refers to the minor allele used as the effect/non-reference allele. The likelihood ratio P-values shown were estimated by the method, which detected the variant as genome-wide significant or suggestive. When both GEMMA and FaST-LMM identified variants as statistically significant, P-values from GEMMA were used. MAF minor allele frequency. a Genome-wide significant unitigs. The SNPs were identified and annotated using the S. pneumoniae serotype 1 reference genome strain P1031 (GenBank accession: CP000920). The risk genotype refers to the minor allele used as the effect/risk/non-reference allele while the safe allele refers to the reference allele/genotype in the GWAS analysis. MAF minor allele frequency. a SNP identified by FaST-LMM. identified allelic variation in the proline-rich repeat region of pspC from our GWAS analysis results in differential propensity of the strains to translocate across the blood-brain barrier to the CNS to cause meningitis by modulating pneumococcal interaction with the laminin-integrin receptor, thus, highlighting pspC as a novel target for clinical interventions.
Isolates associated with known meningitis outbreaks, particularly in West Africa, and those with either unknown source of isolation or non-specific information on sampling locations, e. g. , those recorded as sampled from either blood and CSF or broadly as invasive, were excluded to avoid ambiguity between cases and controls in the GWAS analysis. We cultured the isolates and extracted genomic DNA using approaches described elsewhere  91, 92. DNA sequencing, assembly and isolate typing. The extracted DNA was shipped to the Wellcome Sanger Institute, where genomic DNA libraries were prepared for WGS using Genome Analyser II and HiSeq 4000 Sequencing Systems (Illumina, CA, USA). The following quality control (QC) criteria was used: samples were only included when the overall sequencing depth was >75x, percent coverage of reads of >80% across the pneumococcal genome (GenBank accession number: FM211187), an assembly length was between 1.
0. 1510612  95  , while the international pneumococcal lineage nomenclature defined by the global pneumococcal sequence clusters (GPSC)  52  were detected using PopPUNK v1. 1. 7  96. The consensus whole-genome sequence alignment was generated by mapping reads to the complete reference genome of a serotype 1 pneumococcal ST303 strain P1031 (GenBank accession: CP000920) using SMALT v0. 7. 4 [www. sourceforge. net/projects/smalt/] (minimum insert size: 50, maximum insert size: 1000, minimum quality: 30, minimum depth of coverage: 4, minimum matching reads per strand: 2 and minimum base quality: 50) (Supplementary Fig. 1 ). The insertions and deletions were realigned using GATK v4. 0. 3. 0  97. Phylogenetic analysis and SNP patterns. The generated whole-genome alignment was iteratively screened for recombination events for removal using Gubbins v1.
0. 0 106 using the 'baps' prior optimisation option. Before running fas-tBAPS, we filtered out non-informative and private mutations from the SNP alignment using extract_PI_SNPs. py script (https://gist. github. com/jasonsahl/ 9306cd014b63cae12154). Genetic diversity of the isolates in each clade were assessed using the Vegan v2. 5. 4 package 107 while the geographical distance was calculated using the 'distHaversine' function in 'geosphere' v1. 5. 7 package in R v3. 5. 3 (R Foundation Core Team, 2019). Where >2 alleles were detected at each genomic position, we generated bi-allelic variants at these positions by selecting only the two most common nucleotides. We used two of the most common nucleotides detected at each position variants at each chromosomal position for the analysis. Genomic location of genes were determined using nucleotide BLAST v2.
2. 30 + 108 and visually assessed using ACT v9. 0. 5  109  and DNAPlotter v1. 0 110 while multiple sequence alignments were generated using MUSCLE v3. 8. 31  111. Pan-genome gene presence/absence patterns. The draft genomic assemblies were annotated using Prokka v1. 11  112. These annotated assemblies were processed by Roary v3. 6. 1 pan-genome pipeline  113  to identify the clusters of orthologous genes (COGs). We selected the reference gene for each COG and compared this to each draft assembly using BLAST (percent identity: 85%, query coverage: 85%) in order to generate a COG presence/absence matrix. We then checked for unique COG presence-patterns and then created a reduced COG matrix by filtering out COGs with MAF and missingness <1% and >5% respectively and combined it with the phenotypic data (disease status) into the PLINK-formatted pedigree files for GWAS.
The mean number of unique k-mers per genome was 2,027,332 (range: 1,804,558 to 2,119,269). We converted the k-mers into a De Bruijn graph and then identified 123,401 unitigs  115. The unitigs in Fasta format were compared to the a De Bruijn graph of each genome to generate a unitig presence/absence matrix  115. The unitigs with MAF < 1% were excluded from the matrix and the resultant matrix was converted to the pedigree formatted files containing phenotype data similar to those required by PLINK. GWAS analysis of SNPs, accessory COGs and unitigs. We used the FaST-LMM v2. 07. 20140723  55  and GEMMA v0. 98  56  to fit a univariate linear mixed model for the association between SNPs, accessory COGs and unitigs with the disease phenotype of the isolates namely CNS and non-CNS. The input pedigree files were formatted as haploid human mitochondrial genotypes with chromosome code 'MT' similar to bacterial GWAS elsewhere  35, 44.
The genome-wide and suggestive unitigs were annotated using nucleotide-BLAST v2. 2. 30 + (match identity: 90%, query coverage: 90%)  108  and complete genome references of S. pneumoniae obtained from GenBank (Supplementary Table  1 ). The likelihood ratio test P-values from both FaST-LMM and GEMMA were used to generate the Manhattan plots and QQ-plots using 'ggplot2' v3. 1. 0  116. Correlation between variants was assessed using 'ggcorrplot' v0. 1. 3 in R. Protein structures were modelled using SWISS-MODEL  117  and Robetta 118 , and visualised using PyMOL v2. 4. 0  119.
Acknowledgements We thank the clinical and laboratory teams at the collaborating institutions, and the sequencing and informatics teams at the  Wellcome Sanger Institute. We are also grateful for the insightful feedback on the manuscript provided by  Dr. Bernard Beall  and  Dr. Allen S. Craig  at the  Centers for Disease Control and Prevention (CDC)  in the US. This study was funded by the  Bill and Melinda Gates Foundation  (grant number:  OPP1023440  and  OPP1034556 ). C. C. , G. T. and S. D. B. were supported by funding from the  Joint Programme Initiative for Antimicrobial Resistance (JPIAMR). The contents of this paper are solely the responsibility of the authors and does not necessarily represent the official views of their affiliated institutions and the funding agencies.
Phenotype measurements, genotyping, imputation and meta-analysis AFS is treated as a continuous measure with individuals considered as eligible if they had given a valid answer and ages lower than 12 excluded (see Supp Note 1. 2). Since AFS has a non-normal distribution, a within-sex inverse rank normal transformation is required. AFB is also treated as a continuous measure, assessed for those who have ever given birth to a child. Details about participating cohorts, sample inclusion criteria, genotyping and imputation, models used to test for association, X chromosome analysis, quality control filters and diagnostics, and meta-analysis are in the Supp Note. A sample-size weighted meta-analysis of quality-controlled cohort-level results was performed using the METAL software. 67 We performed conditional and joint multiple SNP analyses (COJO) to identify further independent SNPs and sex-specific analyses.
X chromosome analysis For AFS, the UK Biobank provided results for between 977,536 and 990,735 variants on the X chromosome after QC (see Table  S8 ). For AFB, 13 cohorts provided information on the X chromosome. Overall, we received 23 files, 13 for women, 8 for men and 2 for the pooled analysis in case there were individuals who were relatives in the data. On average, 275,023 variants survived QC with a minimum of 99,794 in women from WLS to 998,304 for the women in the UK Biobank sample (see TableS7 for full descriptives). Association analyses on the X chromosome were performed using software suggested in the analysis plan (XWAS, SNPtest or BOLT-LMM) using BOLT-LMM for AFS as this was only assessed in the UK Biobank data, for AFB, METAL was used as described above (see sup. note 3. 5).
Phenotypic and genotypic historical changes Descriptive analyses and correlations were undertaken using the UK Biobank data to illustrate phenotypic shifts in the age of AFS and AFB by birth cohort, in addition to changes in the spread of the distribution. Pearson's correlation coefficients were calculated and correlation graphs illustrate the changing relationship between the two phenotypes over time. Genotypic changes and SNPheritability by birth cohort were quantified in UK Biobank data using GREML  12  as described earlier. 13.
Genetic correlations Genetic correlation (rg) values were computed to estimate the genetic correlation between the two traits using all polygenic effects captured by the SNPs and LD-score regression. 76 We used summary statistics and the 1000 Genomes reference set, and restricted the analysis to European populations. We also followed the common convention of restricting our analyses to SNPs with MAF >0. 01, thus ensuring that all analyses were performed using a set of SNPs that were imputed with reasonable accuracy across all cohorts. The standard errors (SEs) were produced by the LDSC python software package that uses a block jackknife over the SNPs. We estimated the genetic correlation between 28 different traits, pooled by both sexes and then divided by sex. Traits were divided into the six categories of: reproductive, behavioural, psychiatric disorders, substance use disorders, personality and anthropometric.
Genomic SEM (structural equation modelling) and Exploratory Factor Analysis (EFA) In an attempt to understand the aetiology of the correlations, we used the R package GenomicSEM to fit genetic multivariable regression models. GenomicSEM 17 uses structural equation modelling to decompose the genetic covariance matrix, calculated using multivariate LD score regression, of a set of traits. Formally, structural equation models subsume many statistical methods and are quite flexible. We fit a series of genetic multivariable regression models, in which AFB was regressed on EA (educational attainment) and a trait X, in which we modelled various relevant traits such as openness, cognitive performance and AI (age initiation smoking). We also fit an analogous series of models in which AFS was regressed on EA.
Bi-directional Mendelian Randomization We then tested whether causal pathways linking these phenotypes are potentially bidirectional and whether our phenotypes might offer distinct contributions. We identified 1000 Genomes proxies for our SNPs and used these in multivariate Mendelian Randomisation (MR) models. All data was assumed to be on the forward strand, and as many of these data sets included UK biobank, allele identifier were matched to this study as a reference. First, we modelled the interplay between AFB, AFS and EA (educational attainment)  77  as well as risk taking (measured in adulthood)  4  and age at smoking initiation (AI). 20 In each case IVW  78  and MR-EGGER 79 methods were performed, with an additional round of IVW performed once a Steiger filter  80  had been applied to remove SNPs that appears to show a primary association with the outcome rather than the exposure.
It then queries precompiled databases for the disease names to find and score relevant seed genes. The seed genes are subsequently expanded to include related (predicted) genes based on several types of relationships, e. g. , protein-protein interactions, transcriptional regulation and biological pathways. Phenolyzer uses machine-learning techniques on seed genes and predicted gene rankings to produce an integrated score for each gene. We used search terms capturing three broad areas, i. e. , (in)fertility, congenital neurological disorders and psychological traits, based on results from pathway, tissue and cell type enrichment analyses. In silico sequencing. We used in silico sequencing to identify non-synonymous variants with an R 2 for LD>0. 7 with the lead SNPs in AFS and AFB-associated loci,  32  which yielded genes that may drive the GWAS associations through direct effects on protein function.
Acknowledgements A detailed list of acknowledgments for each cohort can be found in Section 12. 2 of the Supplementary Note. The research leading to these results has received funding from  PI M. C. Mills  from the  European Research Council (ERC)  Consolidator Grant SOCIOGENOME (615603, www. sociogenome. org), ERC Advanced  Grant  CHRONO (835079 ),  Economic & Social Research Council (ESRC) UK, National Centre for Research Methods (NCRM)  grant  SOCGEN (ES/N011856/1 ),  Wellcome Trust ISSF  and a large Centre grant from the  Leverhulme Trust, Leverhulme Centre for Demographic Science. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank  Evelina T. Akimova  and  Stine Møllegaard  for administrative work in the organization of the cohort information and author list.
Data availability Our policy is to make genome-wide summary statistics widely and publically available. Upon publication, summary statistics will be available on the GWAS Catalog website: https://www. ebi. ac. uk/gwas/downloads/summary-statistics The phenotype and genotype data for separate studies used in this GWAS are available upon application to each of the participating cohorts who can be contacted directly to follow their different data access policies. Access to the UK Biobank is available through application with information available at: http://www. ukbiobank. ac. uk).
After 30 min at 37 • C, the incubation buffer was removed and cell monolayers were rapidly washed with 300 µL of ice-cold buffer (Dulbecco's phosphate buffered saline + CaCl 2 + MgCl 2 ). Cells were then lysed by 10 min of incubation with 500 µL of NaOH (10 mM). A total of 200 µL was collected from each well (n = 4 wells per condition) and intracellular fluorescence was determined using a Mithras ® detector (Berthold, France, wavelength excitation 485 nm/detection 535 nm). For each inhibitor, uptake data were normalized to describe the extent of inhibition from 0 to maximal (100%) inhibition. Nonlinear regression was performed to estimate the 50% inhibitory concentration (IC 50 ) of each inhibitor (GraphPad Prism software, version 8. 4, San Diego, CA, USA).
Analysis of [ 99m Tc]mebrofenin Scintigraphy Data Images were analyzed with PMOD ® software (version 3. 9, PMOD Technologies LLC). Regions of interest (ROI) were manually drawn on the liver, intestine, heart (= image-derived blood curve) and urinary bladder. Corresponding TACs were generated by plotting the mean radioactivity amount in each ROI normalized to the injected dose in each animal (cps/MBq) versus time. For descriptive pharmacokinetic analysis, the maximum total amount (X max , cps/MBq), the time to reach X max (T max , min) and the area under the TAC from 0 to 30 min (AUC 0-30 min ) were determined for each ROI. The distribution of [ 99m Tc]mebrofenin from blood to the liver was assessed by determining the liver to blood AUC ratio (AUCR) from 0 to 3 min (AUCR liver/blood = AUC liver,0-3min AUC blood,0-3min ).
Cryo-EM analysis of the isolated SAM1-2 domains (Fig. 1D ), and docking of the crystal structure of the SAM1-2 domains' octamer ring (PDB code 6QWV) into the 3D maps demonstrates that indeed, the ARM and TIR domains are largely missing from this reconstruction, implying a disordered outer ring in ~80% of the particles. Exploring different buffers, pH and salt conditions, addition of various detergents, as well as variations in cryo-EM grid preparation (e. g. ice thickness) did not affect the visibility of the octamer outer ring considerably. These results are inconsistent with our previous analysis, where we used low resolution negative stain EM visualization and 2D classification of hSARM1E642Q that showed fully assembled inner and outer ring structures (Fig. 1C )  (25). We consider, that a gradient fixation (GraFix (33)) step that involves ultra-centrifugation of the protein sample through a glycerol + glutaraldehyde crosslinker gradients, which was applied before the negative stain -but not before the cryo-EM sample preparations -might be the cause for the difference between the two measurements.
2. 8-6. 5 Å resolution structure of a fully assembled compact hSARM1 GraFix-ed octamer We carried out 2D classification (Fig. 2A ) and 3D reconstruction and refinement (Fig. 2B-D ) of the GraFix-ed hSARM1E642Q to 2. 8-6. 5 Å resolution (applying 8-fold symmetry). Overall, the hSARM1E642Q octamer is 203 Å in diameter and 80 Å thick (Fig. S2A ). The SAM1-2 domains' inner ring is the best resolved part of the map, to which the high-resolution crystal structure (PDB code 6QWV) was fitted with minute adjustments. The TIR domains are the least defined part of the density map, mostly not revealing side chain positions (Fig. S3A ). However, the availability of high-resolution crystal structures of isolated hSARM1 TIR (PDB codes 6O0R, 6O0U) allowed their docking into the well resolved secondary structure elements in the map with very high confidence.
We have already established that glycerol meets these two criteria, as it imposes hSARM1 compact conformation (Fig. 2 ) and reduces NADase activity (Fig. 3F ). Glycerol was also found to occupy the active site of the hSARM1 TIR domain in a crystal structure (PDB code 6O0R), directly linking structural data with enzymatic inhibition. However, the glycerol concentrations in which these in vitro experiments were conducted were very high (15-18% for the NADase activity assay and cryo-EM, and 25-30% in the X-ray crystal structure), i. e. 2-4 M, which is considerably higher than the estimated sub 1mM concentrations of intracellular physiological glycerol  (36). We next considered that ATP, being the main energetic compound, with cellular concentrations of 1-10mM, which are depleted in cell death and axonal degeneration, may alternatively serve as a fit candidate to inhibit hSARM1.
Cryo-EM data acquisition and processing In this paper we present data that was collected with three separate cryo-electron microscopes: 1) F30 Polara in Ben-Gurion University, Israel was used for all sample preparation optimization. It was also used to collect data sets without and with potential inhibitors (shown in Fig. 1C  and the ATP and NMN supplemented classes in Fig. 4B ). Finally, it was used for data collection and the 3D reconstructions presented in Fig. 1D. Samples were imaged under low-dose conditions on a FEI Tecnai F30 Polara microscope (FEI, Eindhoven) operating at 300kV. Datasets were collected using SerialEM (44) on a K2 Summit direct electron detector fitted behind an energy filter (Gatan Quantum GIF) with a calibrated pixel size of 1. 1Å. The energy filter was set to remove electrons > ±10eV from the zero-loss peak energy.
5, S4) samples. Frozen grids were loaded into a 300kV Titan Krios (ThermoFisher) electron microscope (CM01 beamline at ESRF) equipped with a K2 Summit direct electron-counting camera and a GIF Quantum energy filter (Gatan). Cryo-EM data were acquired with EPU software (FEI) at a nominal magnification of ×165,000, with a pixel size of 0. 827 Å. The grid of the GraFix-ed sample was collected in two separate sessions. The movies were acquired for 7 s in counting mode at a flux of 7. 06 electrons per Å2 s-1 (data collection 1: 3748 movies) or 6. 83 electrons per Å2 s-1 (data collection 2: 4070 movies), giving a total exposure of ~50 electrons per Å2 and fractioned into 40 frames. 7302 movies of the of the NAD+ supplemented grid sample were acquired for 4 s in counting mode at a flux of 7. 415 electrons per Å2 s-1, giving a total exposure of ~40 electrons per Å2 and fractioned into 40 frames.
Initial maps of both GraFix-ed and NAD+ supplemented hSARM1 were calculated using Ab-initio reconstruction and high-resolution maps were obtained by imposing C8-symmetry in non-uniform 3D refinement. Working maps were locally filtered based on local resolution estimates. 3) Talos Glacios in EMBL, Grenoble, France was used for data collection and the comparison of NAD+ supplemented and not-supplemented samples (Fig. 4A, B ). Frozen grids were loaded into a 200kV Talos Glacios (ThermoFisher) electron microscope equipped with a Falcon3 direct electron-counting camera (ThermoFisher). Cryo-EM data were acquired with EPU software (FEI) at a nominal magnification of ×120,000, with a pixel size of.
78% ethanol, 4uM FMN (Riboflavin 5'-monophosphate sodium salt hydrate), 27. 2 U/ml Alcohol dehydrogenase (SIGMA), 1. 8 U/ml Diaphorase (SIGMA) and freshly dissolved (in DDW) 8 uM Resasurin (SIGMA), was added to each sample at 1:1 (v/v) ratio and then transferred to 384-well black plate (Corning). Fluorescent data was measured using a SynergyHI (BioTek) plate reader at 554-nm excitation and 593-nm emission wavelengths. Standard curve equation for calculation of NAD+ concentration was created for each assay from constant NAD+ concentrations. eNAD-based NADase assay: eNAD (Nicotinamide 1,N6-ethenoadenine dinucleotide, SIGMA -N2630) was solubilized in water and mixed with native NAD+ in ration of 1:10 (mol:mol) to a final stock concentration of 10mM. Serial dilutions were made with 25mM HEPES pH 7.
1:100 (v/v). BSA (NEB Inc. 20mg/ml) was included, and reactions were stopped by heating at 95°C for 2 minutes. Where specified, NMN (Sigma-Aldrich -N3501) was added in different concentrations. For control, NAD+ consumption was compared to a commercially available porcine brain NADase 0. 025 units/ml (Sigma-Aldrich -N9879). HPLC measurements were performed using a Merck Hitachi Elite LaChrom HPLC system equipped with an autosampler, UV detector and quaternary pump. HPLC traces were monitored at 260nm and integrated using EZChrom Elite software. 10 μL of each sample were injected onto a Waters Spherisorb ODS1 C18 RP HPLC Column (5 μm particle size, 4. 6 mm × 150 mm ID). HPLC solvents are; A: 100% methanol; B: 120mM sodium phosphate pH 6. 0; C double-distilled water (DDW). The column was pre-equilibrated with B:C mixture ratio of 80:20.
Calculation of SARM1 kinetic parameters For Vmax and Km determination, the NADase activity assay was performed with several different NAD+ substrate concentrations and sampled in constant time points. For each NAD+ concentration, linear increase zone was taken for slope (V0) calculation. All data were than fitted to the Michaelis-Menten equation using non-linear curve fit in GraphPad Prism software. Kcat was calculated by dividing the Vmax with protein molar concentration.
GraFix-ed map The monomers of known octameric X-ray structures of the hSARM1 SAM1-2 domains (PDBs 6qwv and 6o0s) were superimposed by the CCP4  (53)  program GESAMT  (54)   monomer) with scores in the range of 0. 78-0. 81. These helical fragments were used for building of the ARM domain in Coot  (57). The quality of the high resolution GraFix-ed density map was sufficient for assignment of side chains for all ARM domain residues. The TIR domain BB loop region (a. a 595-607) was built to fit a relatively poor density map in a conformation different to those observed in X-ray structures. The model was refined using both REFMAC5  (58). Side chains of some Lys residues had blobs of undescribed density attached to them. These were modelled as glutaraldehyde ligands. Geometrical restraints for the di-glutaraldehyde molecule and its links to side chains of Lys residues were prepared using JLIGAND  (59).