dataesr/scientific-paragraphs-categorization huggingface dataset:
parapsilosis, C. krusei, and C. tropicalis. The mechanisms of action of Mo-CBP3-PepI against the Candida genus were revealed using C. albicans as a model  [15] [16] [17]. Regarding the antibacterial activity, Mo-CBP3-PepI, the only relevant activity, was evaluated against K. pneumoniae. Therefore, this study employed fluorescence and scanning electron microscopes and proteomic analysis to provide new insights into the mechanism of antibacterial action of Mo-CBP 3 -PepI against K. pneumoniae. Additionally, toxicity tests were performed against human cells to produce new data on the safety of Mo-CBP 3 -PepI.
Protein Identification Tandem mass spectra were extracted into PKL files for both samples, and the proteins were searched using MASCOT MS/MS ions search from MATRIX SCIENCE (https: //www. matrixscience. com/cgi/search_form. pl?FORMVER=2&SEARCH=MIS (accessed on 15 September 2022)) against UP625_E_coli_K12 (AA), UP808_K_pneumoniae and Swis-sProt databases (the taxonomy was set in bacteria). The terms for the search were: fixed modifications to Carbamidomethyl (C); variable modifications to Oxidation (O); the Peptide tolerance was set to 1. 2 DA (with 1% FDR); the MS/MS tolerance was set to 0. 6 DA; the peptide charge was set to 2+, 3+, and 4+; and finally, the instrument was set to ESI-QUAD-TOF. The proteins identified in both samples were searched for in UNIPROT and separated into 3 sets (unique from control, unique from the cells treated with Mo-CBP 3 -PepI, and Mo-CBP 3 -PepI x control shared proteins).
The proteins with a fold-change value ≥ 1. 5 (p < 0. 05, Tukey's test) that were upaccumulated (increased the abundance), and proteins with a fold-change value ≤ 0. 5 (p < 0. 05, Tukey's test) that were down-accumulated (decreased the abundance) were taken into consideration for comparisons. For each protein, its corresponding FASTA file was downloaded. Then, the blast2go program (https://www. blast2go. com/ (accessed on 30 September 2022)) was used to categorize the proteins detected by Gene Ontology (GO) annotation according to Molecular function, Biological Activity, and subcellular location.
Morphological Analysis of Apoptotic Cells For this assay, the concentration of peptides was 1 mg mL -1. DMSO-NaCl was the negative control for damage, and MMS at 4 × 10 -5 M was used as the positive control for DNA damage. Peptides and control solutions were incubated as described above. Cells with the morphological characteristics of apoptosis, such as apoptotic bodies, peripheral condensation of chromatin, fragmented nucleus, and small cell volume, were evaluated after 24 h of treatment, either with controls or peptides. The evaluation was executed using acridine orange (AO)/ethidium bromide (EB) staining assay. A total of 50 µL of the cell suspension was mixed with 1 µL of the staining solution (100 µg/mL AO + 100 µg/mL EB in PBS) and spread on a slide, where 300 cells were counted per data point.
07 times higher than Mo-CBP 3 -PepI. These results revealed that Mo-CBP 3 -PepI is much more effective against K. pneumoniae than other synthetic peptides. We further evaluated the cell viability of K. pneumoniae cells and biofilm formation after treatment with Mo-CBP 3 -PepI. The first was to evaluate the number of viable cells after treatment with Mo-CBP 3 -PepI (Table  1 ). The MTT assay revealed that only 47. 54% ± 0. 008 of K. pneumoniae cells were viable after incubation with Mo-CBP 3 -PepI, which agrees with the data of MIC 50. These results prove that Mo-CBP 3 -PepI kills half of the cells at the tested concentration. Otherwise, 100% of K. pneumoniae cells were viable in control treated with 5% DMSO in 0. 15 M NaCl (Table  1 ). Moving forward, the antibiofilm potential of Mo-CBP 3 -PepI toward K.
By inducing the reduction in accumulation of Lon protease in K. pneumoniae cells, Mo-CBP3-PepI dramatically reduced the chances of the cell to respond to stress and inhibit several essential processes to cell normal function leading to death. The proteins unique from control cells were identified, such as multidrug resistance protein MdtN, UPF0194 membrane protein YbhG, and multidrug resistance protein EmrK (Table  S2 ). MdtN is a protein involved in the resistance against puromycin and acriflavine  [55] , EmrK is a part of the efflux pump involved in multidrug resistance  [57] , and YbhG is involved in resistance to chloramphenicol  [58]. This is an exciting result because the absence of these in the Mo-CBP3-PepI-treated cells indicates that they became susceptible to these antibiotics again, which is an important outcome.
Author summary The ribosomal RNA gene repeats (rDNA) form large tandem repeat arrays in most eukaryote genomes. Their tandem arrangement makes the rDNA prone to copy number variation, and there is increasing evidence that this copy number variation has phenotypic consequences. However, difficulties in measuring rDNA copy number hamper investigation into rDNA copy number dynamics and their significance. Here we developed a novel bioinformatics method for measuring rDNA copy number from whole genome sequence data that is based on the modal sequence read coverage. We established parameters for optimal performance of the method and validated it using yeast strains of known rDNA copy numbers. We then applied the method to a dataset of almost 800 global yeast isolates and demonstrate that yeast populations have different rDNA copy numbers that partially correlate with phylogeny.
Calculation of rDNA copy number using mean and median coverage Per-base read coverage across W303-rDNA from Bedtools was input into custom R-scripts to obtain mean and median coverage values for the whole genome and rDNA after removing the rDNA, 2-micron plasmid, and mitochondrial DNA coverage values from the whole genome calculation. rDNA copy number was then calculated for the mean and median data as follows: 𝒓𝑫𝑵𝑨 𝒄𝒐𝒑𝒚 𝒏𝒖𝒎𝒃𝒆𝒓 = 𝒄𝒐𝒗𝒆𝒓𝒂𝒈𝒆 𝒂𝒄𝒓𝒐𝒔𝒔 𝒓𝑫𝑵𝑨 𝒄𝒐𝒗𝒆𝒓𝒂𝒈𝒆 𝒂𝒄𝒓𝒐𝒔𝒔 𝒘𝒉𝒐𝒍𝒆 𝒈𝒆𝒏𝒐𝒎𝒆.
Subsampling To generate different coverage levels for copy number estimation, sequence reads were randomly downsampled using the seqtk tool (https://github. com/lh3/seqtk): command: ~/seqtk/seqtk sample -s$RANDOM <name of fastqfile> <number of reads required> <outputfile> rDNA copy number measurement by ddPCR At least three independent cultures (biological replicates) were generated for each isolate using one independent colony per culture. To evaluate rDNA copy number variation over generations, cultures were propagated over four days (~60 generations) as follows: individual colonies were initially grown in 3 mL YPD for 24 hr. 30 µL of this was used to inoculate 3 mL YPD and this was grown for another 24 hr. This process was repeated for four days. Cells were harvested after 24 hr (~15 generations) and four days, and cell pellets frozen at -80˚C.
Yeast Genome project rDNA copy number estimation Illumina reads from the 1002 Yeast Genomes project were obtained from the European Nucleotide Archive (www. ebi. ac. uk/) under accession number ERP014555. We omitted clades with few members, mosaic clades, and unclustered isolates, giving a total of 788 isolates. Reads were downsampled to 10-fold-coverage using seqtk() and rDNA copy number for each isolate was calculated using W303-rDNA as the reference. Bin sizes of 1/200 th of the mean coverage for rDNA and 1/50 th for the whole genome, and a window size of 600 bp for both estimates, were used. Violin plots were plotted using the ggplot() package in R.
For the white noise distribution, data were simulated from a normal distribution with mean and standard deviation matching those of the observed copy number data (mean=92. 5, sd=30. 8). For the Brownian motion model, we first estimated the ancestral mean (z0=83. 2) and the rate parameter (σ2=72557. 2) from the observed copy number data using the fitContinuous function from geiger  [62]  (https://cran. r-project. org/package=geiger). Then, we simulated from these parameters on the SNP tree using fastBM from phytools 0. 7 (https://cran. rproject. org/package=phytools). Phylocorrelograms were generated for the observed and two simulated datasets, estimating correlations at a series of 100 phylogenetic distances using 100 bootstrap replicates.
2). To help smooth across positions that stochastically vary in coverage, an issue that is particularly prevalent with very low coverage datasets, we used a sliding window approach to calculate coverage. Our straightforward pipeline uses a sorted BAM file of reads aligned to a reference genome for which the position of the rDNA is known (either embedded in the genome or as a separate contig) to calculate copy number. reference genome containing a single rDNA copy. Mean read depth for each postion is calculated across the rDNA and the WG using a sliding window, then allocated into coverage bins (shown as histograms). To calculate modal rDNA copy number, the highest frequency coverage bins for both the rDNA and WG are used to compute ratios that represent the rDNA copy number range. The histograms shown were plotted using a 20-copy yeast strain at 5fold WG coverage with bin sizes of 1/200 th of mean coverage for rDNA and 1/50 th for WG, and a sliding window of 600 bp for both.
The coverage ranges for the three most frequent bins for each are indicated in boxes. To implement our modal coverage approach, we generated test datasets by performing WG Illumina sequencing of a haploid wild-type laboratory S. cerevisiae strain reported to have 150-200 rDNA copies, and three isogenic derivatives in which the rDNA has been artificially reduced to 20, 40 and 80 copies, and "frozen" in place through disruption of a gene (FOB1) that promotes rDNA copy number change  [17]  (Table  1 ). Initially, we investigated which parameters provide the most accurate results by applying our pipeline to the WG sequence data obtained from a strain with 20 rDNA copies (20-copy strain). We obtained a genome-wide read coverage of 13. 1-fold (Supplementary Table  1 ) and mapped these reads to the W303-rDNA yeast reference genome that has a single rDNA copy.
We chose ddPCR to experimentally determine rDNA copy number because it is less sensitive than qPCR to biases in secondary structure regions that are common in the rDNA coding region  [22]. The ddPCR data showed that the rDNA copy numbers of our strains are similar to those calculated by our modal coverage method, with both methods suggesting that the "80-copy" strain actually has substantially fewer copies than reported (Supplementary Table  2 ; Supplementary Figure  5A ), perhaps due to a stochastic change in copy number that has occurred in our version of this strain. We also compared our modal coverage approach with the mean coverage calculated from the same datasets. We used a simple mean calculation to match the implementation of our modal approach, using the same down-sampled 10-fold WG coverage datasets.
It was previously reported that coverage below ~65X results in precision issues when estimating rDNA copy number  [5]. However, we did not find this, either for our method or when using mean coverage, suggesting the issues might be specific to the approach and/or dataset used in that study. The simple implementation of our modal approach coupled with its good performance make it an attractive method for estimating rDNA copy number from sequence read data. Furthermore, a modal approach is expected to be more robust to features that can perturb mean coverage approaches by skewing coverage distributions, such as repeat elements, large duplications and deletions, regions exhibiting sequencing biases, modest sequence divergence from the reference sequence, and aneuploidies  [46]. Although we have developed our pipeline for measuring rDNA copy number, in principle it can be used to calculate copy number for any repeated sequence where all reads map to a single repeat copy and the sequence is known, such as mitochondrial and chloroplast genome copy numbers.
Within-species evolutionary dynamics of rDNA copy number Studies in model organisms have provided evidence that each species has a homeostatic copy number which is returned to following copy number perturbations  [7] [8] [9] [10]. This homeostatic copy number appears to have a genetic basis  [5, 26] , which suggests it might vary between populations, as well as between species. However, few studies have addressed this question. Given that variation in rDNA copy number has been associated with altered phenotypes  [8, 12, 17, 22, [27] [28] [29] [30] [31] [32] [33] [34] [35] , we decided to undertake a comprehensive assessment of S. cerevisiae rDNA copy number at the population level using the global wild yeast dataset from the 1002 Yeast Genomes project  [64]. We obtained WG sequence data for 788 isolates from the 1002 Yeast Genomes project.
Reads for each isolate were downsampled to 10X genome coverage, mapped to our W303-rDNA reference genome, and rDNA copy numbers estimated using our modal coverage pipeline. The rDNA copy numbers ranged between 22-227 (x ̅ = 92) across the 788 isolates (Supplementary Table  3 ). The copy numbers of 11 wild S. cerevisiae isolates included in our dataset had previously been estimated  [14, 25] , and our results are largely consistent with these (Supplementary Table  4 ). However, the copy numbers we estimate are, in general, much lower than those (~150-200) measured for most laboratory strains (e. g. [17, 38, 41] ). We looked to see whether ploidy affects rDNA copy number, given that laboratory strains are predominantly haploid while the wild S. cerevisiae isolates we analyzed are mostly diploid.
cerevisiae and Drosophila have reported the persistence of stochastic copy number changes without recovery  [65, 67]. It will be important to reconcile these conflicting results and to determine to what extent the population-level differences we observe are the result of copy number homeostasis (as we interpret them) versus copy number inertia. Our results showing population-level differences in rDNA copy number suggest that such differences can arise relatively quickly in evolutionary time, although the very high level of copy number variation between individuals acts to obscure this pattern. Therefore, it is important to take the large variances and rapid copy number dynamics of the rDNA into account when interpreting the impact of copy number variation on phenotype. Bioinformatics pipelines, such as the one we have developed here, in conjunction with the increasing availability of appropriate NGS datasets provide a way to establish baseline data on rDNA copy number variation between cells, individuals, populations, and species, as well as to investigate the phenotypic consequences of this variation.
Acknowledgements We thank  Gianni Liti  ( IRCAN, CNRS, INSERM, Université Côte d'Azur, University of Nice ) for kindly providing strains. We acknowledge the  University of Auckland Centre for eResearch (CER)  and the  New Zealand eScience Infrastructure (NeSI)  for providing high performance computing facilities, consulting support, and training services. We thank  Auckland Genomics  for advice and whole genome sequencing, and  Kevin Chang  for help with statistical analyses. We thank  Alastair Harris  for helpful suggestions, and the  Ganley Lab  for helpful comments and feedback on the manuscript. This work was supported by a grant from the  New Zealand Marsden.
Feature peak lists were generated from MS spectra within a retention time window of 1-15 minutes and a filtering of 5000 counts using MetaboScape 4. 0 software (Bruker). The peak lists consisted of the areaunder-the-peaks of extracted analytes from the three tissues (medaka's gut, liver and muscles) sampled on days 0, 1, 2, 4 and the M. aeruginosa lyophilized culture. A log transformation was applied to metabolomics datasets. Principal Component Analysis (PCA) were performed using the mixOmics  18  (v6. 14. 1) R package.
DNA extraction, sequencing and analysis of the V4-V5 region of bacterial 16S rRNA DNA was extracted from the pellets of the guts and M. aeruginosa culture, and from 0. 22 µm filters for aquarium water. Extractions were performed using the ZymoBIOMICS DNA Mini-prep kit with a FastPrep 5G beat beater disruption (DNA Matrix; 4x30s; 6m. s -1 ) following manufacturer's instructions. An extraction-blank control sample was also performed. The V4-V5 region of the 16S rRNA encoding gene was amplified using primers 515R and 926F  19  and sequenced on an Illumina MiSeq 250x2 bp platform (Biomnigene, Besançon, France). Reads were deposited into the Sequence Read Archive (SRA) database (accession number PRJNA836730 (samples SRR19170691-SRR19170767; Table  S2 ). Sequence analysis including primer removal and quality control was performed using the QIIME2-2021.
2 pipeline  20. Forward and reverse reads were trimmed at 250 and 200 bp, respectively. Amplicon Sequence Variants (ASVs) were obtained with DADA2 (default parameters) and affiliated with the SILVA 138-99 database. Diversity metrics were computed with the phyloseq  21  (v1. 34. 0) R package. Statistical analyses were performed using R packages vegan  22  (v2. 5-7) and RVAideMemoire 23 (v0. 9-79). All values are displayed as median ±standard deviation.
MEBA analysis A Multivariate Empirical BayesianAnalysis  24, 25  (MEBA) was performed to discriminate differentially abundant taxa through time and between treatments. Relative abundance tables at the Phylum and Genus taxonomic levels were obtained from phyloseq and analyzed using the MEBA plugin from Metaboanalyst 5. 0 (https://www. metaboanalyst. ca), with no data transformation and all by-default parameters. Taxa with a MEBA T 2 score superior to 5 were consider discriminant and further statistically analyzed.
ASV-metabolites correlative networks A correlative network analysis was performed with DIABLO  26  , a multi-omics framework created by the MixOmics team  18  , to analyze putative correlated dynamics between gut microbiota ASVs and gut metabolites. Briefly, the block. plsda() function performs a Pattern Latent Structure Discriminant Analysis which provides a reduced-dimension space with covariance-maximizing axes for each dataset (named "block"). A correlation score for the given blocks was computed with the plotDiablo() function.
Comparative dynamics of microbiota and metabolome The composition change dynamics in microbiota versus metabolome were investigated by comparing trajectories of centroids using a newly developed method: MOTA (Multivariate Omics Trajectory Analysis). Microbiota and metabolome datasets were log transformed and analyzed separately but in a similar way. Distances between PCAs' centroid coordinates were computed to create a trajectory between each sampling day for the two treatments and displayed as the fraction of the total length achieved at each day (from 0% to 100% between day 0 to 4). Trajectories were plotted for the 16S rRNA versus metabolomic data, allowing comparison of their respective dynamics in the two treatments (details in the supplementary information).
A B exposed fish. Indeed, the gut metabolome achieved 14% of the total distance at day 1 versus 47% for the microbiota. At day 2, cumulative values are 56% for the metabolome and 98% for the microbiota, suggesting that most of the change ultimately observed in the microbiota composition is achieved after only 2 days. This holds true also when accounting for a higher percentage of the total explained variance (Fig  S3 ). The trajectory thus shifts towards the gut microbiota axis for exposed samples, suggesting a faster response of the gut microbiota compared to the gut metabolome. The gut microbiota bacterial ASVs and gut metabolites datasets were well correlated at both day 2 and 4 (correlation score: 0. 91). These results suggests that changes in microbiota might be preceding, and possibly driving observed metabolome changes, a hypothesis congruent with the localization of the gut microbes at the interface between host and its environment (digestive lumen), and thus their role as a primary barrier to contaminants  7, 10.
Methods Each study (Supplementary Table  2 ) obtained informed consent from participants and approval from the appropriate institutional review boards or committees. Samples and study design. We conducted a large meta-analysis for physical activity traits, including results from up to 703,901 individuals (including nearly half-a-million from the UK Biobank) to identify genetic loci associated with physical activity and sedentary behavior across different ancestries. We first examined genome-wide, ancestry-and sex-stratified associations in 51 studies with questionnaire-based data on: (1) MVPA; (2) LST; (3) sedentary commuting behavior; and/or (4) sedentary behavior at work, using study-specific, tailored analysis plans (Supplementary Table  2 , see Supplementary Note for rationale). Next, we performed ancestry-specific, inverse variance-weighted fixed-effects meta-analyses of summary statistics for each of the four self-reported traits (Fig.
SNP-based heritability estimation. To estimate the heritability explained by genotyped SNPs for each physical activity and sedentary trait, we used BOLT-REML variance components analysis  74  , a Monte Carlo average information restricted maximum likelihood algorithm implemented in the BOLT-LMM v. 2. 3. 3 software. As in most GWAS for complex traits, the SNP heritability (up to 16%) was lower than the heritability estimates from twin studies (31%-71%)  8, 9  , likely at least in part due to the absence of rare variants in GWAS  75. Although we performed a multi-ancestry meta-analysis, data from relatively few individuals of non-European ancestries were available to us, and our functional follow-up analyses were conducted based on the European ancestry results. Studies with data from more individuals of non-European ancestry will no doubt further increase the understanding of physical activity etiology.
49). PheWAS with physical activity PGSs. To assess the out-of-sample predictive power of the variants associated with self-reported sedentary behavior and physical activity, we constructed two PGSs-for LST and for MVPA-in up to 23,723 Mount Sinai BioMe BioBank participants, using summary statistics of the primary European ancestry meta-analyses and PRSice software  78. We subsequently assessed the association of MVPA and BMI with the PGSs in individuals of European and African ancestry, as well as in Hispanic participants, within the BioMe BioBank. Among the 2,765 European ancestry individuals with physical activity measurements and genotypes, the PGSs were calculated on common variants (minor allele frequency >1%) using P value thresholds from 5 × 10 -8 to 1 (all variants) in the LST and MVPA GWAS, and clumping parameters of r 2 < 0.
Briefly, International Classification of Diseases 9 and 10 codes from electronic health records were mapped to phecodes using the PheWAS package  79. Among 8,959 BioMe European ancestry participants, the 1,039 disease outcomes with at least ten cases were analyzed. We used logistic regression to separately model each phecode as a function of the two PGSs, adjusting for age, age-squared, sex and the top ten principal components. Interpretation of results was restricted to outcomes with more than ten cases. Multiple testing thresholds for statistical significance were set to P < 4. 8 × 10 -5 (0. 05/1,039).
These can be divided into six categories: lifestyle traits, anthropometric traits, psychiatric diseases, other diseases (cardiometabolic diseases and cancer), biomarkers and others (Fig. 4 ). Using objectively assessed physical activity traits (accelerometry) instead of self-reported traits yielded similar results (Supplementary Fig. 2 ). Two-sample MR. We performed MR analyses to disentangle the causality between LST and MVPA, on the one hand, and BMI, on the other hand. We further investigated the causal effects of LST and MVPA on common diseases and risk factors, while considering BMI through multivariable MR. For multivariable MR, we used BMI (exposure 2) summary statistics based on UK Biobank data, and summary statistics for disease outcomes and other relevant traits based on data from the largest publicly available GWAS without data from UK Biobank participants on the MR-Base platform and OpenGWAS database  80, 81.