dataesr/scientific-paragraphs-categorization huggingface dataset:
Validation in external datasets demonstrates EYA1 and CSMD3 mutations are associated with poor survival To evaluate the generalizability of our findings, we analyzed the mutations that we identified that appeared to be more frequent in LNM than in primary tumor foci or were frequently mutated in our cohort. We examined data from twelve published studies  [2, 3, 6, [41] [42] [43] [44] [45] [46] [47] [48] [49]  that are publicly available in cBioPortal  [38, 39]  comprising 8902 samples. Of those 8902 samples, 6650 samples (Supplementary Table  S13 ) were definitively annotated as either primary tumors (n = 3929) or metastases (n =2721). We found that mutations were enriched in metastases over primary tumors for TP53 (q = 2E-64), EYA1 (q = 2E-9), CSMD3 (q = 6. 1E-6), MAP3K15 (q = 3. 3-5), PIK3CD (q = 2.
2E-4), SLC16A14 (q = 1. 4E-3), FLT4 (q = 1. 6E-3), PCDH15 (q = 2. 1E-3), CACNA1E (q = 1. 5E-2), and NCOR2 (q = 1. 9E-2) by Fisher exact test adjusted for false discovery (Figure  5A ). We found SPOP mutations were enriched in primary tumors (q = 3. 1E-3), and surprisingly, LRRC4C were also enriched in primary tumors (q = 3. 1E-5). The complete list of mutations from this meta-analysis enriched in metastases or primary tumors includes well established oncogenes and tumor suppressors such as AR, TP53, PTEN, RB1, APC, MYC, and CTNNB1 and is provided in Supplementary Table  S14. Mutational comparisons for EYA1 and CSMD3 are shown in Figure  5B ,C. To examine the clinical significance of these mutations, we compared the disease-specific survival (492 patients) in the TCGA PanCancer dataset  [46] , disease-free survival (334 patients), and progression-free survival (494 patients) for samples with and without EYA1 (Figure  5D-F ) or CSMD3 (Figure  5G-I ) mutations (including SNV, CNV, and structural variations) and found that mutations in both these genes were significantly associated with poor outcome.
Additionally, analysis of multiple primary foci greatly increases the richness of our dataset and enables an integrated functional and clinical genomics approach to reveal genes driving aggressive metastatic prostate cancer. Our data suggest that LNM contain multiple subclones that are already present in multiple related primary tumor foci. However, some subclones are more abundant in the LNM than in primary tumor foci, suggesting natural selection for these subclones and enrichment for specific mutations. We also show here that in addition to well-established oncogenes such as AR and TP53, mutations in lesser studied genes such as EYA1, CSMD3, FLT4, NCOR2, and PCDH15 are enriched in prostate cancer metastases relative to primary tumors. Additionally, mutations in FLT4 and PCDH15 are associated with the CE7 and CE8 gene expression ecotypes associated with Androgen, PI3K, MTORC, and OXPHOS pathways.
Finally, using publicly available datasets, we have shown that mutations in EYA1 and CSMD3 are not only more frequent in metastases, but also are associated with worse disease-specific survival, progression-free survival, and disease-free survival. The most frequent somatic mutation that we observed occurred in the Speckle-type POZ Protein (SPOP) gene, which is essential for ubiquitination and subsequent proteasomal degradation  [53, 54]. Functional SPOP is necessary for the degradation of AR, and mutated SPOP fails to ubiquitinate AR, allowing for an increase of AR protein  [53, 54]. Consistent with previous findings, we observed recurrent mutations in the MATH binding domain of SPOP, which mediates protein-protein interactions with AR. Recent studies have also identified important interactions between SPOP and c-JUN  [55] , in which mutated SPOP can bind to and stabilize c-JUN, potentially leading to accelerated cell proliferation.
There are a number of limitations to this study. First, while this study includes tissues from only 43 patients, of which only 16 had metastases, it does provide RNAseq analysis of 165 samples and WES analysis of 137 samples, with at least three and up to seven samples for each patient. Second, the samples used were macrodissected from FFPE blocks and thus were analyzed by bulk sequencing. Finally, while many of our observations were either consistent with previous literature or validated in large external datasets, functional validation of the observed mutations and gene expression changes are needed to better understand the mechanisms of metastasis and poor outcome for prostate cancer patients.
Acknowledgements This work was supported by grant  CTQ2017-85658-R  funded by  MCIN/AEI/10. 13039/501100011033/FEDER  " Una manera de hacer Europa ", grant  PID2020-114256RB-I00  funded by  MCIN/AEI/10. 13039/501100011033 , and the  diaRNAgnosis  project funded by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No  101007934. P. M. R. P. acknowledges funding from  Fundação para a Ciência e a Tecnologia , I. P. , through projects  UIDB/00100/2020  and  2022. 04076. PTDC. Funding for open access charge:  Universidad de Granada / CBUA. The authors would like to acknowledge the  Centro de Instrumentación Científica (CIC) of Universidad de Granada (Spain)  and the  Laboratorio de Microscopias Avanzadas, ICTS ELECMI (Spain)  for the use of their AFM and TEM facilities.
Susceptibility to antimicrobials Forty-ve genes coding for proteins potentially related to antibiotic resistance were found in the CEC15 genome by aligning against the CARD database (Additional le 9). These genes are classi ed into three resistance mechanisms: antibiotic e ux (n = 37), antibiotic target alteration/protection (n = 5), and antibiotic inactivation (n = 3). The antibiotic classes comprised by these genes are mostly uoroquinolones, β-lactams, macrolides, glycopeptides, and aminoglycosides. The EcN genome, similarly, presented 44 genes potentially-related to antimicrobial resistance (Additional le 10), the vast majority of them coding for antibiotic e ux mechanisms (n = 38). Four EcN genes were related to antibiotic target alteration/protection, and 2 for antibiotic inactivation.
Adhesion to human intestinal epithelial cells According to the SPAAN software, 84 genes of CEC15 and 89 of EcN, six from each are duplicated genes (Additional le 12) were predicted with a high probability pro le (score > 0. 8) to code for adhesins. A total of 33 genes were found exclusively on CEC15 genome, against 32 on EcN. From these exclusive gene products, CEC15 presents 13 mbriae proteins, 2 agella proteins, 9 transport proteins, and 4 phage related proteins. EcN, on the other hand, posses 7 mbriae proteins, 5 transport proteins, and 3 phage related proteins. Among the predicted CEC15 adhesin genes, 29 are related to mbriae/pili proteins (34%), 18 to porins/transporters (21%), and 8 to agella proteins (10%). A similar categorical distribution of adhesins was observed for EcN: 30% of mbriae/pili (n = 27), 13% of porins/transporters (n = 15), and 7% of agella (n = 6).
DNA was quanti ed using the nanodrop 2000 spectrophotometer (ThermoFisher, Massachusetts, EUA) and proceeded to sequencing. Genome sequencing, assembly, annotation, and phylogenomic analysis DNA sequencing was performed using the Illumina HiSeq platform, with a pair-end library of 2x151 bp and an insert size of 450 bp (Göttingen, Germany), and by the PacBio platform. The analysis of the quality of the reads was performed using the software FastQC (FastQC: a quality control tool for high throughput sequence data. After 21 days of differentiation, the adhesion assay proceeded. For this, an overnight culture of E. coli (CEC15 and EcN) was diluted to 1% in fresh LB broth and incubated until reaching an optical density (OD 600nm ) of 0. 5 (≈ 2 x10 8 CFU/mL). One mL sample of bacterial cultures was centrifuged at 6000 x g for 10 min, and the pellet resuspended in 1 mL of DMEM-HG medium without FBS, penicillin and streptomycin.
Proteomic analysis. Three independent replicates of shearing-derived proteins (10 µg each) were separated on 12% homemade SDS-PAGE minigels (Miniprotean II, Bio-Rad) and stained with Coomassie-blue (BIO-RAD, France). In-gel trypsin digestion was performed as described before  [149]. Peptides were identi ed by mass spectrometry as described elsewhere [150], followed by protein identi cation (maximum e-value of 0. 05) from the MS/MS spectra with the X!TandemPipeline software  [151]. The peptides were searched against the genome sequences of the two strains described above with parameters as described before  [152]. A minimum of 3 peptides per protein was necessary for the validation of the identi cation and a protein was only considered present when it was identi ed in at least two of the three replicates.
Histopathological analysis A section of approximately 4 cm of ileum was opened, washed with PBS to remove fecal matter, rolled up, and xated with a 10% formalin solution. Later, tissue was embedded in para n, and sections of 4 µm were placed in microscope slides and stained with hematoxylin and Eosin (HE). From each animal, 10 pictures from different tissue sections were collected using a BX41 optical microscope (Olympus, Tokyo, Japan) (20x). The pictures were blindly scored according to the system previously described by  Howarth et al. [154] , and the villus height and crypt depth (20 per animal) were measured with the assistance of the Image-J software (v. 1. 51j. 8 -NIH, Bethesda, MD, USA).
Acknowledgements: This work was conducted in the frame of  BactIn am International Associated Laboratory be-tween INRAE (France)  and  UFMG (Brazil). We thank  Jessica Musset  and  Sébastien Dévault  for their excellent technical support. The author would like also to give thanks to  Agnès Burel  from  MRic Microscopy Rennes Imaging Center at Université de Rennes  for sessions with TEM and  Loïc Joanny  from the  Centre de Microscopie Électronique à Balayage et microAnalyse (CMEBA ) at  Université de Rennes  for sessions with SEM.
,772,817 bp (four gaps) forming a chromosome, one contig of 201,163 bp representing the plasmid, and 26 contigs of 7588 bp also belonging to the chromosome; however, these 26 contigs were excluded from the analysis by an assembly quality lter for being recognized as an artifact of low sequencing coverage and without signi cative similarity by BLAST analysis to any E. coli genome deposited on NCBI database (April 6th, 2022). MOB-suite software[126] was used to classify contigs from the chromosome and the plasmid. The chromosome scaffold had its origin xed at the dnaA gene, with a total of 5 gaps, and the plasmid had its origin xed at the repB gene. The remaining gaps were closed using the software GFinisher (v. 1. 4)[127] based on contig assembled by the software EDENA (v. 3. 131028)[128]. In the end, we have a chromosome with 4,780,804 bp, with sequence coverage of 383.
babraham. ac. uk/projects/fastqc). Data from Illumina sequencing generated a Phred value of 39 for 6280224*2; thus, trimming was unnecessary. Sequenced from PacBio presented 122,634 reads and, after the Phred value was adjusted to 24, a new le was generated with 12238 reads with a range size of 500-24781 bp. The assembly was performed ab initio with the SPAdes software (v. 3. 15. 3 [Python version: 3. 5. 2])[125] with a hybrid assembly approach from the two sequencing platforms' results. A scaffold assembly was The genome of CEC15 and EcN strains (NCBI access: NZCP058217 [chromosome], NZCP058218 [pMUT1], and NZCP058219 [pMUT2]) used in this study were automatically annotated by the Prokaryotic Genome Annotation Pipeline (PGAP-NCBI) [129-131]. Functional annotation was performed with EggNOG-mapper [132, 133].
The orthology between the two genomes was analyzed by the OrthoFinder tool [134]. The sequences of protein were aligned by MUSCLE software[136], and the corresponding codon sequences were concatenated. The phylogenomic inference was realized via the RAxML program [137] with support values estimated by 100 fast bootstrapping runs [138]. The tree was visualized and edited with the tool iTOL (v. 6. 53) (https://itol. embl. de/). Genomic islands prediction, transposases, and insertion elements Prediction of Metabolic (MI), Resistance (RI), and Pathogenicity (PAI) islands in CEC15 and EcN strains was performed with the software GIPSy (Genomic Island Prediction Software, v. 1. 1. 3) [139], using Escherichia coli O157:H7 str. Sakai genome (NC_002695) as a reference. Phage islands were predicted utilizing PHASTER tool (PHAge Search Tool Enhanced Release) [140, 141].
Visualization of the genomic island's map was performed with BRIG software (BLAST Ring Image Generator, v. 0. 95) [142]. The annotation of insertion elements was done using the tool ISSaga (Insertion Sequence Semi-Automatic Genome Annotation) (http://issaga. biotoul. fr/) [143]. The serotyping of CEC15 was identi ed based on reached with 4the access number JAHWXU000000000. genes for speci c O-antigen (O typing) and agellin genes (H typing) with the SerotypeFinder 2. 0 web tool hosted by the Center for Genomic Epidemiology (CGE) (www. genomicepidemiology. org). Data was curated manually and tabulated.
Bioinformatic analysis Illumina Miseq amplicons were processed via an automated bioinformatics work ow microSysMics (https://bio. tools/microSysMics). Quality ltration of reads, trimming, taxonomic assignation, as well as ASV processing and decontaminations were conducted as in Huet et al. [12]  and Martinez et al. [14]. ASVs were assigned to taxa according to UNITE database (classi er v. 8-99 2020. 02. 04) for fungi  [54]  and SILVA database (classi er 138-99-nb) for bacteria  [55, 56]. ASVs of interests that were not assigned were further computed in the NCBI database with a BLAST of the nucleotide sequence. ASVs were numbered by alphabetical order according to the SILVA or UNITE code, respectively for bacteria and fungi. Their associated codes and corresponding DNA are available in supplementary data (Tables  S2  and S3 ).
Microbial community analyses All analyses were performed via R (R Core v4. 0. 3), on RStudio 2022. 07. 1 + 576. Secondary analyses were computed via the Phyloseq package v. 1. 34. 0  [57]. To simplify analyses, lowabundance taxa (< 0. 1%) were grouped into a common class "<0. 1% abond. ". For α-and β-diversity analyses, abundance tables were rare ed to the minimum library size (across samples and weeks) to account for sampling intensity variations  [58]. Analyses of α-diversity were performed using richness (i. e. number of ASVs detected) and Shannon (i. e. number of ASVs detected and their evenness) indices, and compared with two-sided Wilcoxon Rank Sum tests. Analyses of β-diversity were performed via permutational analysis of variance (PERMANOVA) via the Adonis function in the Vegan package v.
2. 6. 2 (Oksanen et al. 2022), using a Bray-Curtis dissimilarity matrix. Dissimilarities were interpreted via multivariate analysis on principal coordinate (PCoA). Differential abundant analysis was performed using the DEseq2 R package v. 1. 30. 1  [59] , to assess the differences between the abundance of ASVs from the conducive and suppressive soil at the initial time.
Screening of microbial ASVs functionally correlated to parasitic traits To describe correlation patterns between fungal dynamics and parasitism suppression, we employed a weighted gene co-expression network analysis (WGCNA v. 1. 70. 3 [60] ). This allowed to detect clusters of highly correlated taxa in relation with external sample traits (total attachments, relative proportion of developmental stages, germination and prehaustorium formation ratios). Composition of clusters was described by measuring the relative abundance of their ASVs, and plotting the results onto a bubble plot (https://github. com/alex-bagnoud/OTU-table-to-bubble-plot). Finally, a cross-sectional analysis of DEseq2 and WGCNA results was conducted to detect ASVs most likely associated with parasitism soil suppression. First, using a Pearson's correlation test, abundances of ASVs over abundant in SN soil (DEseq2) were tested against previously highlighted suppressive parasitic traits (total parasitic attachment and necrosis ratio).
Statistical methods Analyses were performed on RStudio 2022. 07. 1 + 576 with different statistical approaches according to data distribution and experimental design. Differences of parasitic attachments between the soils were modeled with a Poisson distribution, adapted to t discrete count data with low value, e. g. comprised between 0 and 113 attachments, and compared with analysis of variance using ANOVAs on a generalized linear mixed effects models (glmer) with the Car package v3. 0-10, (Fox and Weisberg 2019) and the lme4 package v1. 1-29  (Bates et al. 2015). Parasitic stages were analyzed by calculating their relative abundance among the total attachment observed. Each stage was used as explanatory variable with a glmmPQL, a glmer based on a quasi distribution for logistic regression with a probit link function.
GlmmPQL were employed to model proportion data following a binomial law as our data were continuous and comprised between 1 and 0. For both total number of attachments and relative stages, the xed effects tested were: sampling week, soil effect (conducive or suppressive) and nature (native or sterile). Their effects on the explanatory variables were compared with multi comparisons of estimated means with the emmeans package 1. 7. 5 Germination and prehaustorium formation kinetics were rst analyzed using a glmmPQL model glmer based on a quasi law for logistic regression. The soil (CN, SN, CS or SS), week (from 1 to 7) and myrosinase (with or without) factors were used to explain rzhisophere exudate activity, and co-cultivation replicate was used a random factor to account for biological variability.
(Pseudonocardiaceae family), the Vicinamibacteraceae family (ASV_12501), uncultured species (ASV_25744; order Vicinamibacterales) and Streptomyces sp. (Streptomycetaceae family). Blastn alignment of ASV_12501 and ASV_25744 DNA sequences on NCBI database was not conclusive to determine further taxonomic assignation. Nevertheless, these ASVs were quite rare in SN soil (33 to 177 reads), which con rms a lack of structural differences between CN and SN soils. Measures of α-diversity showed that bacterial richness was slightly higher but variable in the SN soil with 447±116. 49 ASVs (median±sd) compared to 352±35. 40 ASVs in the CN soil (p value = 0. 01; Table  1 ). However, this tendency was not signi cant (i. e. p value > 0. 05) with either Shannon (5. 26±0. 08 for CN soil and 5. 39±0. 28 for SN soil; p value = 0.
83±4. 21%), Aspergillus (10. 51±2. 37%), NA (not attributed; 6. 62±1. 89%) and Botryotrichum (5. 72±1. 46%). In SN soil, the genus Aspergillus (69. 53±6. 75%) was the most abundant, followed by genera less than 0. 1% abundant (5. 25±0. 74%), Penicillium (4. 76±1. 76%), Fusarium (4. 33±1. 53%) and NA (3. 03±1. 16%). Among the Aspergillus genus, 37 distinct ASVs were detected: 9 ASVs in CN soil and 28 ASVs in SN soil. The top three species of total Aspergillus ASVs in SN soil were ASV_853 (88. 48±84. 89%), A. protuberus (2. 83±1. 43%) and A. penicillioides (2. 59±1. 16%). In CN soil, the three species were A. alliaceus (66. 84±8. 14%), ASV_853 (25. 77±6. 23), and A. pseudode ectus (3. 36±4. 36%). ASV_853 was not attributed to a unique Aspergillus species after blastn of DNA sequence into NCBI database.
Among the Fusarium genus, 9 distinct ASVs were detected: 8 ASVs in CN soil and 7 ASVs in SN soil. The top three species of Fusarium in SN soil were ASV_2553, (63. 05±9. 51%), F. domesticum (27. 87±8. 82%) and ASV_1542 (5. 16±1. 57%). No species assignation was found neither in UNITE or NCBI database. In CN soil, the top three species of Fusarium were ASV_2553 (90. 68±2. 23%) and two F. solani (ASV_2338: 3. 50±2. 14%; ASV_1744: 3. 45±1. 33%). Statistically, the DEseq2 analysis revealed that69 ASVs were differentially abundant in SN soil compared to 63 in CN soil (Fig. 10, B ). Of these 69 ASVs abundant in SN soil, the top ve were identi ed as three Aspergillus spp. (one A. caesiellus and two A. penicillioides), Fusarium domesticum, and Wallemia tropicalis. Other notable species such as Alternaria sp.
ASVs were considered of interest when they were i) initially overabundant in SN soil (DESeq2), ii) individually correlated to suppressive parasitic traits (i. e. total attachment and necrotic symptom ratio) and iii) present in one of the three clusters (WGCNA; Table  2 ). Following this strategy, eight fungal candidates were detected. The six ASVs Holtermanniella takashimae (ASV_756), ASV from Nectriaceae family (ASV_951), Monocillium mucidum (ASV_2460), Fusarium domesticum (ASV_2920), Tetracladium sp. (ASV_2949) and ASV from Bartaliniaceae family (ASV_3836), were differentially abundant in SN soil, found in the Blue cluster and negatively correlated to broomrape attachments. Additionally, one ASV_3513 (Pyrenochaeta terrestris or P. lycopersici according to NCBI database; percent identity = 100.
basicola enters a necrotrophic phase during which it lyses and consumes plant root cells  [121]. We thus suspect that this species could also be pathogenic to broomrapes. Given the candidate we propose, pathogenic assays on will be necessary to conduct on rapeseed and on potential successive crops. In all cases, metabarcoding analyses are increasingly valuable to monitor biodiversity and asses potential shifts in diverse environmental samples, without requiring steps of cultivation and taxonomic identi cation in the lab  [122]. Nevertheless, they are strictly descriptive and still carry limits (e. g. low resolution for species assignation, lack of representative DNA sequence in reference databases, rDNA gene copy numbers, DNA extraction or PCR ampli cation  [123, 124] ). Furthermore, pathogenicity is not always correlated with the abundance of the corresponding pathogen, thus searching for active microbial genes in suppressive soils should be more informative  [125, 126].
Acknowledgements work was permitted by the kind cooperation of  Terres Inovia 's technicians under the supervision of  Christophe Jestin  and  Elodie Tourton , whom the authors are particularly thankful. The authors are grateful to the two farmers who gave access to their elds and provided all the information about their agricultural practices. The authors are grateful to  Coralie Marais  and  Muriel Bahut  from the  ANAN  platform for their support on metagenomic analysis to the bioinformatics core facility of Nantes ( BiRD -Biogenouest)  for its technical support, and  Erwan Delage  for his help with the microSysMics pipeline. From the  US2B lab in Nantes , the authors are most thankful for technical help of  Johannes Schmitt  and  Sabine Delgrange  on the preparation of seed lots and growth medium, as well as internship student  Marie-Emilie Deschez.
52. Data have been deposited in https://doi. org/10. 5281/zenodo. 7686404. ACKNOWLEDGMENTS. We would like to thank B. Rauer, and T. Schweigler for helpful discussions in the early stage of the project. We also thank S. Weinfurtner for fruitful discussion. S. S. would also like to thank P. Moosavi for discussions on theoretical aspects. This work has been supported by the DFG/FWF CRC 1225 'Isoquant', the DFG/FWF Research Unit FOR 2724 'Thermal machines in the quantum world", and the FQXi program on 'Information as fuel' ESQ Discovery Grant 'Emergence of physical laws: from mathematical foundations to applications in many-body physics' of the Austrian Academy of Sciences (OÄW). J. E. has also been supported by the DFG CRC 183 and the BMBF (MUNIQC-ATOMS), as well as by the EU's Horizon 2020 research and innovation program under grant agreement No.