dataesr/scientific-paragraphs-categorization huggingface dataset:
The NxS motif was most dominantly observed in HCs (9 out of 11) and the NxT motif (7 out of 11) was most often seen in LCs. The non-canonical N-glycosylation motif NxC was observed in one LC sequence (Figure  1B ). The location of the glycosylation sites was predominantly confined to the region between Framework 1 (FR1) and FR2 in the LC sequences (Figure  1C , red). N-glycosylation sites of the HC were observed over the entire Fab region, with a hot spot at complementary determining region (CDR) 2 and FR3 (Figure  1C , blue). Next, we determined the occurrence of Fab regions containing a de novo N-glycosylation site in the polyclonal immunoglobulin population of healthy donors available in the cAb-Rep database  24. This database provided access to sequences of polyclonal HC of 19 healthy individuals (average sequences per donor = 4645) and sequences of polyclonal kappa + lambda LCs of 31 healthy individuals (average sequences per donor = 4809).
Studying M-protein de novo glycosylation using glycoproteogenomics Protein N-glycosylation is a non-template driven process capable of generating thousands of unique N-glycan structures for several hundred unique sugar compositions. As such, the collection of Nglycan structures and their stoichiometry that decorate a single N-glycosylation site cannot be derived in silico from sequence information. Moreover, the mere presence of an N-glycosylation sequon does not guarantee any glycan occupancy. Therefore, we established a glycoproteogenomics workflow to characterize the glycosylation status of de novo N-glycosylations sites of patient-specific genomic alterations. Firstly, based on RNA-seq datasets from malignant plasma cells in the bone marrow of patients with MM, the sequence of the Fab region of the M-proteins was predicted (Figure  2  Genomics).
Acknowledgements This work was financially supported by two grants from the  Dutch Cancer Society for JFMJ  (# 10817  and # 14465 ) and by  Sebia (Lisses, France). This research was supported by  ZonMw Medium Investment Grant  ( 40-00506-98-9001 ) and was part of the  Netherlands X-omics Initiative  supported by collaboration project " EnFORCE " ( LSHM21032 ) which is co-funded by the PPP Allowance made available by  Health~Holland, Top Sector Life Sciences & Health , to stimulate public-private partnerships. The authors further acknowledge the  Multiple Myeloma Research Foundation  for providing access to the CoMMpass study data through NIHdbGap.
Dataset selection Using Google Scholar and Web of Science search engines (a list of keywords is available as Supplementary Materials), we collated bacterial studies from systems where an experimental disturbance was imposed, and 16S rRNA gene amplicon sequencing datasets were available. Speci cally, we chose studies that: 1) were sequenced in Illumina or IonTorrent platforms; 2) sequenced the V3-V4 regions of the 16S rRNA gene; 3) were published after 2014; 4) repeatedly sampled microbial communities following a discrete disturbance or environmental change; 5) included samples from before the disturbance (i. e. , controls), at least one (replicated) sample within a week after disturbance, and at least one (replicated) sample within a month after disturbance; and, 6) included experimental triplicates (i.
e. , three samples per time point). Criteria 1-3 ensured that the sequencing techniques were comparable between studies, and reduced the biases associated with sampling different regions of the 16S rRNA gene  [26]. Importantly, downstream analyses adopted a synthetic framework (i. e. , we reprocessed sequences using a single approach described below), and samples from different studies were not combined. We applied criteria 4-6 to examine variation in rates of compositional change across environments. Criterion 6 ensured that the variability of the microbiomes at each time point could be measured. We de ned a disturbance as a "discrete, rapid environmental change". We excluded datasets for which raw sequencing data were not publicly available, and stopped data collection in October 2020. In all, datasets from 29 studies matched our criteria  [13, 20, , see Table  S1  for all datasets).
Sequence reprocessing and functional inference Raw 16S rRNA gene amplicon data and metadata was obtained from the NCBI Sequence Read Archives with the exception of two datasets, one of which came from another database, and the other was obtained directly from the authors (see Table  S1  for accession numbers). We reprocessed sequences in R 3. 4. 3  [51]  using the dada2 package  [52] , and a conservative approach. To account for the different sequence qualities across datasets and to improve comparability in the reprocessed data, each dataset was inspected and reprocessed separately, and downstream statistical analyses accounted for betweenstudy differences. Prior to processing, we visually inspected two samples per study with the plotQualityPro le to determine whether the reads had been merged prior to archiving, and to con rm that primers were not present.
We ltered, dereplicated, and chimera-checked each read using standard work ow parameters  [54]. While we did not use taxonomic assignments in our analyses or compare amplicon sequence variants (ASVs, 100% sequence identity) across datasets, we assigned reads to ASVs with the SILVA v. 132 training set  [55]  to remove non-bacterial ASVs. Unassigned, bacterial ASVs (i. e. , those classi ed as Bacteria) were preserved. Details about the percentage of reads lost at each step of sequence processing, per study, are included in Figure  S1. As the samples included in these studies had a wide range of sequencing depths across samples (independent of study environment), we randomly subsampled each sample to 1500 reads per sample to obtain a similar degree of biodiversity detection across studies. To ensure that our ndings were not affected by observation depth, we additionally ran all analyses in parallel using the deepest possible observation depth (with a lower bound of 1500 reads per sample) for each study (Table  S1 ).
RESULTS Our nal dataset included 2588 samples in 86 time series from 29 studies (Table  S1 ) belonging to soil micro-and mesocosms (n = 49), seawater mesocosms (n = 16), and mammalian microbiomes (n = 21) that were sampled multiple times within 50 days after disturbance (Fig. 2a ). Across all samples, we detected 56,480 ASVs. Sample completeness was highest in mammalian microbiomes (0. 98 ± 0. 02; mean ± sd), lowest and most variable in soil microbiomes (0. 93 ± 0. 06), and was signi cantly different between environments (ANOVA, F = 475. 1, p < 0. 001, Fig. 2b ).
g. , those that impact certain populations versus those that indiscriminately impact all populations) on microbiome responses. Overall, this work provides a new empirical perspective on the dynamics and generalities of microbiome disturbance responses that is supported by directly comparable metrics, equivalent temporal scales among datasets, and a consistent modeling approach. It suggests that with comparisons of standardized diversity measures, responses that were previously believed to be applicable to all microbiomes (i. e. , the AKP) are not present, and that the environment is a key determinant of microbiome of both the response to, and recovery from, disturbance.
Also, this patient is now waiting for a liver transplant. Genetic analysis. After obtaining written consent of the family, peripheral blood was collected from the patient and his parents. Genomic DNA was extracted using QIAsymphony SP instrument (Qiagen). Targeted sequencing of a panel of 392 genes involved in movement disorders was performed on the trio (adapted from  Montaut et al. , 2018). PCCA and PCCB were among the tested genes. Libraries prepared with SureSelect® enrichment probes (Agilent) were sequenced with Mid Output Kit v2. 5 (300 cycles) on NextSeq550 instrument (Illumina). Data were analyzed using the homemade bioinformatics pipeline Stark. Variants were annotated and ranked according to VaRank software  (Geoffroy et al. , 2015). PCCB variants identified in the family were annotated according to the MANE transcript NM_000532.
5. New PCCB variants described in this work have been submitted to ClinVar database. Cell culture. PCCB deficient fibroblast cell line stably transformed with the T22 plasmid which contains SV40 DNA sequences was used for in vitro expression analysis  (Perez-Cerda et al. , 2003). Cells were cultivated according to standard procedures. Briefly, cells were maintained in Minimum Essential Medium supplemented with 1 % glutamine, 10 % foetal bovine serum (FBS) and antibiotics. Expression analysis. PCCB variants were introduced by PCR mutagenesis (Quikchange Lightning Site-Directed Mutagenesis kit, Agilent Technologies) in the pRcCMVB52 vector coding for wild-type PCCB cDNA. Sequence analysis confirmed the identity of the mutant clones. The day before transfection, cells were seeded into 6-well cell culture plates (400,000 cells per well).
, 2010)  and SIFT  (Ng and Henikoff, 2001). PCC holoenzyme is a trimer of hetero-tetramers composed by two central PCCB-subunits and two peripheral PCCA-subunits. Structural prediction of the novel PCCB variants was performed in a previously reported  (Gallego-Villar et al. , 2013)  homology model, using as template the homologous crystallized PCC holoenzyme from Ruegeria pomeroyi (PDB ID: 3n6r)  (Huang et al. , 2010) , composed of PCCA and PCCB-subunits sharing 54 % and 65 % sequence identity to the human subunits, respectively. Structural modelling was performed as previously described (Rivera-Barahona et al. , 2018), using the SWISS-MODEL server (https://swissmodel. expasy. org/interactive).
Acknowledgement The technical support of  E. Montalvo  and  M. Antin  is gratefully acknowledged. This work was funded by  Spanish Ministry of Science and Innovation and European Regional Development Fund  (grants  PID2019-105344RB-I00/AEI/10. 13039/501100011033  and  PID2022-137238OB-I00 ) and by  Fundación Ramón Areces  (grant from  XX National Call 2020). Centro de Biología Molecular Severo Ochoa  receives an institutional grant from  Fundación Ramón Areces.
Raw data files were analyzed with the Proteome Discoverer software (version 1. 4, Thermo Fisher Scientific, Waltham, MA, USA) connected to a Mascot Search engine (version 2. 2. 4, Matrix Science, London, UK) against the UniProt database (Sunflower section, version October 202184477 sequences) concatenated with a database of common contaminants found in proteomics experiments. For the unrestricted search (no enzyme), peptide and fragment tolerance were set at 10 ppm and 0. 6 Da, respectively. All identified peptides were reported in Supplementary Data section.
Molecular docking analysis The identified peptides were screened using an in silico molecular docking approach to identify those that may have antioxidant effects. The Kelch domain of Keap1, which binds to a conserved DxETGE motif located in the Neh2 domain of Nrf2 (PDB 2FLU), was chosen as target for the docking analysis  (Lo et al. , 2006). The Nrf2 peptide present in the crystal structure was deleted, and the resulting model was equilibrated and protonated in preparation for molecular docking. The peptideprotein interaction was assessed using two different docking methods, CABS-Dock and GalaxyPepDock, simultaneously  (Kurcinski et al. , 2015; Lee et al. , 2015). According to the docking protocols used, the procedure is unbiased regarding the pre-selection of the possible binding site. However, in agreement with the internal binding score of each software, the peptides that bound with the Neh2 binding site of Nrf2 were preferred.
Each plate contained different experimental groups. The test was conducted using a Dan-ioVision system (Noldus, Wageningen, the Netherlands). The protocol included an initial phase of light lasting 10 min, followed by alternating periods of light and darkness, each lasting 10 min. Python was used to preprocess the DanioVision datasets. Kernel Density estimation for the fish position probability was estimated using Scipy software (version 1. 9. 3,  Virtanen et al. , 2020).
4 M NaCl, 0. 1 mM PMSF, 1 mM NaF, and protease inhibitors. The mixture was mixed every 2 min for 10-15 sec. Samples were centrifuged at 20,000 g for 10 min at 4 • C. Nuclear proteins (30 µg) were loaded onto a 10 % SDS-PAGE and subjected to Western blot analysis to determine the levels of Nrf2, NF-κB and IκB with PCNA and GAPDH being used as the loading control  (Tonolo et al. , 2022; Tonolo, Fiorese, et al. , 2020). Densitometry evaluation was performed using the Nine Alliance software (Mini 9 17. 01 version, Uvitec Alliance, Cambridge, UK).
Data were expressed as a percentage of the values observed in nontreated group. (B) Lipid peroxidation evaluated by the production of thiobarbituric acid-reactive substances (TBARS). Caco-2 cells were pre-treated with peptides (0. 05 mg/mL) for 24 h and oxidative stress was induced with 250 µM TbOOH. TBARS production was estimated fluorometrically and the data were expressed as a percentage of the values observed in nontreated group. ### p < 0. 001 vs. nontreated group (Cnt). Means of at least 3 experiments were compared. ** p < 0. 01, * p < 0. 05 vs. treated group (TbOOH). genes, including those encoding cytokines and chemokines, we evaluated the translocation of the mentioned transcription factor into the nucleus in Caco-2 cells treated with P-12-V, before the exposure to TNFα. As depicted in Fig.
SARS-CoV-2 RNA sequencing NGS sequencing libraries were prepared using COVIDSeq Test kit (Illumina, Inc San Diego, CA, USA) with SARS-CoV-2 specific primers Artic v4. 1 as described in  Gerber et al. (2022). The SARS-CoV-2 enriched libraries were sequenced using NovaSeq 6000 SP Reagent Kit v1. 5 (300 cycles) according to the manufacturer's instructions (Illumina, Inc San Diego, CA, USA). Sequencing data was analyzed using our in-house bioinformatic pipeline as described in  Gerber et al. (2022)  using Pangolin database v4. 2  (O'Toole et al. , 2021).
Acknowledgments This study was supported by the  University of Strasbourg , the  French scientific group of interest GIS-OBEPINE , The  Agence de l'Eau Rhin Meuse , by the  Grand Est region , the  French national agencies for research ANR  ( COVIDEU project ) and  ANRS-MIE  ( EmerEaUde  project). The authors would like to thank operators and local authorities from  Syndicat des Eaux et de l'Assainissement Alsace-Moselle (SDEA)  for the sampling efforts made in Alsace WWTPs. Special thanks to  Florence Burvingt  and  Nathalie Caille  for their support to our WWBE projects. Work in C. V. L. lab was supported by the  Belgian National Fund for Scientific Research (F. R. S. FNRS, Belgium) , the  ULB-COVID-Fund  and the '' Les Amis des Instituts Pasteur à Bruxelles , asbl''. The laboratory of C.
Variable transformation rates across Acinetobacter and Legionella strains We analysed 496 draft genomes of Ab and 786 of Lp. The species pangenomes included 31,103 (Ab) and 11,932 (Lp) gene families. Ab genomes had on average 3598 genes with a coefficient of variation of 5. 0%, whereas genomes of Lp had on average 3091 genes with a coefficient of variation of 3. 3%. We used the gene families present in more than 95% of the genomes (persistent gene families: 2326 in Lp and 2629 in Ab) to build a recombination-aware phylogenetic tree for each species by maximum likelihood (Table  S1 ). The two trees have approximately similar average root-to-tip distances (Ab: 0. 048; Lp: 0. 056 subst -1 ), but the Lp tree has many more small terminal branches than Ab. Altogether, the Ab dataset is more variable in terms of gene repertoires, but the species are of comparable age (distance to the species' last common ancestor).
Pilins evolve quickly by point mutation, HGT and duplication processes, complicating their detection in draft genomes. More importantly, the gene comM, encoding a helicase involved in the homologous recombination of transforming DNA into the chromosome, was often pseudogenized. This was significantly more frequent in non-transformable than in transformable strains (𝜒 $ , p=1. 46x10 -04 ) (Figure  3 ) and in clinical strains relative to environmental ones (𝜒 $ , p<2. 2x10 -16 ). The inactivation of this gene was most often caused by its interruption in the MgCh domain by MGEs. This has been described in clinical strains, where AbaR and AbGRI resistance islands integrate in this region. Contrary to previous analyses our dataset has only a small number of clinical strains. Our analysis of this locus revealed AbaR islands in a fourth of comM inactivations.
/ , 11 matched proteins with very high viral quotient (higher than 0. 9, see Methods). Among them, the most significantly transformation-inhibiting gene seems to be part of a prophage antidefence system. We predicted its protein structure with AlphaFold and aligned it against large dataset of protein structures using Foldseek. This analysis revealed structural homology to DarA, a protein that is involved in capsid morphogenesis and is a component of an anti-restriction system (found in A. baumannii ACICU, Figure  S5 )  [30]. However, the homology is restricted to the N-terminal domain and this gene stands alone in the GWAS (the Dar system being composed of many proteins). Further work will be necessary to disentangle the function of this protein. In brief, the abundance of prophage genes in the GWAS suggests these elements have an important role in the inhibition of transformation in Ab.
Bacterial strains, origin and typing We analyzed draft assemblies of 830 Lp clinical strains from the Centre National de Référence pour Legionella (CNRL) collection and of 510 Ab environmental and clinical strains, all listed in Table  S6. We serotyped Ab collection with mlst v. 2. 19. 0 (https://github. com/tseemann/mlst). The Lp collection was assembled from clinical isolates collected in France from 2018 to 2020 and was serotyped by the CNRL. This publication made use of the PubMLST website (https://pubmlst. org/) developed by Keith Jolley  [53]  at the University of Oxford. We also typed Ab capsules with Kaptive v. 2. 0. 3  [54] ,  [55]. The search for antimicrobial resistance genes was done with ABRicate (https://github. com/tseemann/abricate) which used the following databases ARG-ANNOT  [56] , CARD  [57] , MEGARes  [58]  and VFDB  [59].
9), Mgch (PF01078. 24) and MgchC (PF13335. 9). We relied on the coordinates and order of the domains in our alignments to identify the correct region of interruption in which we would search for MGEs and AMR genes. In Acinetobacter baumannii, we had to devise a specific method to search for pilA because of the high variability of its sequence across the species  [62]. We searched the most conserved region of pilA, the pilin domain (PF00114) in the representative sequence of each gene family with hmmsearch (hmmer/3. 3. 2). We considered that the pilin was present when the sequence score of the alignment was above the gathering threshold score cutoff (--cut_ga). Every gene family  (34)  presenting this domain was considered encoding PilA and every strain having this gene family was deemed having pilA.
Variant calling and estimation of genetic linkage We identified single nucleotide polymorphisms (SNPs) and small insertions and deletion (indels) in our strains using as a reference genome the Paris strain for Lp and the AB5075 strain for Ab. The identification was done using snippy v. 4. 6. 0 (https://github. com/tseemann/snippy). We annotated them and predicted their functional effects with snpEff v. 4. 3  [63]. From the previous variant calling we only kept biallelic SNPs that were polymorphic in transformable strains and in non-transformable ones. We defined non-overlapping windows of 500 bp that scanned the whole genome. We considered pairs of SNPs (A,B). In each pair, we calculated pr the frequency of the reference allele of SNP A (Aref) and qr the frequency of the reference allele of SNP B (Bref).
Identification of MGEs and defense systems We characterized the different types of mobile genetic elements. Conjugative elements were searched using Macsyfinder 20221213. dev  [74] ,  [75]  and the CONJScan/Chromosome model (https://github. com/macsy-models). We detected conjugative systems (CONJ) which are complete conjugative systems formed by a T4SS, a type IV coupling protein (t4cp) and a relaxase, mobilizable systems (MOB) which are systems that have a relaxase that is either alone or co-localizing with a CONJ component but not enough of the latter to make a functional conjugative system. We checked if two contigs of a sample presented complementary incomplete CONJ on their extremities. If so, we included them with the other conjugative systems. Available methods cannot precisely delimit the Integrated Conjugative Elements (ICEs).
Each putative prophage comprised between two persistent genes in a putative prophage containing at least 5 genes among which one had a PHROG annotation and whose average viral quotient is more than 0. 5 was deemed to be a prophage. Integrons were searched for using IntegronFinder v. 2. 0. 2  [81] -  [84]. We also characterized defense systems. CRISPR-Cas systems were searched using CRISPRCasFinder v. 4. 2. 20  [74] ,  [75] ,  [85] ,  [86]. We retained the ones verifying the following criteria: the presence of a Cas gene in the corresponding genome, the presence of more than 2 spacers in the array, and imposing a mean size for spacers of at least 15 bp. We identified the targets of these spacers by running blastn and its task option of blastn-short (blast+ v. 2. 12. 0) on our collection of genomes, available RefSeq plasmids and GenBank phages setting the same parameters as CRISPRTarget (CRISPRSuite)  [87].
4. 6. 0 (https://github. com/tseemann/snippy). We reduced the regions of interest to the regions that overlapped between unitigs of opposite association when this was possible. We summarized the positive or negative association to the phenotype, the effect size, the significance, the minimum allele frequency and the number of unitigs mapping to each variant. This procedure identified 52% (out of the 12%) of the genes in Ab and 66% (out of the 43%) in Lp. These genes with overlapping unitigs of opposite sense will be candidates for future analyses. Given their abundance, we further enquired on the regions with such unitigs in Lp. In Lp, half of them were in gene families of ICEs.
Structural prediction of hypothetical proteins of interest To get insights into the functional role of proteins of unknown function, we characterized their protein domains. We first predicted their protein structure with ColabFold v. 1. 5. [90]. The output protein structure was then given to Foldseek server  [91]  which compared it to a large collection of protein structures (6 databases: AlphaFold/Proteome, AlphaFold/Swiss-Prot, AlphaFold/UniProt50, GMGCL, MGnify-ESM30, PDB100) in 3Di/AA mode.
Acknowledgements We thank  Eloïse Guignard  for technical assistance in isolation of L. pneumophila isolates. We thank  Evelyn Skiebe  for preparing and shipping A. baumannii isolates, and members of the  RKI central sequencing lab  for excellent technical assistance. We thank  Eugen Pfeifer  for his help in defining and providing all the data necessary for the viral quotient calculation. We are grateful to  Suzana Salcedo  for providing additional A. baumannii isolates [92]. The strains CIP and CRBIP have been obtained from the  Collection Institut Pasteur (CRBIP, Paris, France). This work was supported by the  French Agence Nationale de la Recherche (ANR) , under grant  ANR-20-CE12-0004  ( TransfoConflict ). EPCR  lab if funded by a grant  Equipe FRM  ( Fondation pour la Recherche Médicale )  EQU201903007835  and by the  Laboratoire d'Excellence IBEID Integrative Biology of Emerging Infectious Diseases  [ ANR-10-LABX-62-.