dataesr/scientific-paragraphs-categorization huggingface dataset:
Results Composition of the fire coral microbiome. To discriminate the relative contribution of host genetic background and surrounding environment on coral-microbiome composition, we determined bacterial communities of six clonal genotypes of M. cf. platyphylla from three adjacent, but environmentally distinct reef habitats at Moorea, French Polynesia (Fig. 1  and Supplementary Data 1) using 16S rRNA gene amplicon sequencing. After quality trimming and removal of chimeric, undesired (e. g. , chloroplasts and mitochondria), and rare sequences, 16S rRNA gene sequencing from 135 colonies of the fire coral M. cf. platyphylla, yielded 1 236 195 sequences that were further clustered into 20 144 amplicon sequencing variants (ASVs) (Supplementary Data 2). M. cf. platyphylla associated with 45 unique bacterial phyla, 107 classes, and 265 orders.
51. These bacterial taxa were from 3 phyla, 3 classes, 4 orders, and 3 families (Table  1 ) and comprised on average 41% of the relative abundance of the bacterial community across all samples. Gammaproteobacteria and unclassified Firmicutes were the most dominant groups, representing 38% (6 ASVs out of 16, accounting for 17% of the relative abundance of the total bacterial community) and 19% (3 ASVs, 14%) of the core microbiome, respectively. Bacterial community composition differs between host genotypes. To test for a possible effect of host genotype on microbial association, we assessed the assemblage of bacterial ASVs across different clonal host genotypes for each habitat. Our data revealed that bacterial communities differed significantly between fire coral genotypes present on the mid slope (PERMANOVA, F = 1.
4D  and Supplementary Data 4). Fifty bacterial indicator taxa were identified for the mid slope, of which approximately three quarters belonged to the Alphaproteobacteria (13 unclassified Thalassobaculales, 3 Rhizobiaceae, and 6 Kiloniellaceae, including 4 of the genus Pelagibius) and 1 to the Polyangia (ASV00030 Sandaracinaceae). Only three indicator ASVs were identified for the upper slope habitat, two of which belonged to the classes Cytophagales (Cyclobacteriaceae of the genus Fulvivirga) and Flavobacteriales (Cryomorphaceae) of the phylum Bacteroidetes, and one Alphaproteobacteria of the family Rhizobiaceae. To assess whether time since fragmentation contributed to microbiome structuring, we used colony size as a proxy. We did not find any patterns of bacterial community composition associated with distinct colony size classes among clonal replicates that were found in distinct reef habitats (PERMA-NOVA based on the ASV composition of four genotypes; G1, G4, G5, G6, P > 0.
e. , from August 23 to September 26, 2019) to assess the environmental differences between the three surveyed reef habitats. Temperature was recorded in 60-sec intervals using in situ deployed HOBO Pendant Temperature Data Loggers (Onset, USA), while the light conditions were recorded in 90-sec intervals using two 2π PAR Loggers (Odyssey, New Zealand). Differences in daily temperature and light intensity between reef habitats were assessed using Kruskal-Wallis tests (because assumptions of normality and homoscedasticity were not satisfied) with the R package 'stats', and the complementary post hoc pairwise comparisons were also conducted. DNA extraction and clonal genotypes. From our previous surveys  46, 106, 110  , 3 160 colonies of M. cf. platyphylla were sampled and genotyped using microsatellite loci (as described in Dubé et al.
4. 0 (Applied Biosystems, Foster City CA, USA). Further details on the microsatellites loci and genotyping procedure are described in Dubé et al. 46. Multilocus genotypes (MLGs) were identified in GENCLONE v. 2. 0  113. Colonies with the same alleles at all loci were assigned to the same MLG (genet) and were considered as clone mates due to fragmentation when the genotype probability (GP) was < 0. 001. GP was computed in GENALEX v. 6. 5  114. We selected six genotypes with at least four clonal replicates in at least two of the surveyed habitats (n = 135 samples) to examine variation in bacterial communities among fire coral clones across distinct reef habitats (Fig. 1B  and Supplementary Data 1 for MLGs of selected samples). A map of the locations of each clonal genotype was produced using the package 'ggplot2'  115  as implemented in R software v.
Indexing adaptors were added via PCR (8 cycles) according to the Nextera XT DNA library preparation protocol using the Multiplex PCR Kit. Indexed PCR products were purified and normalized using the SequalPrep Normalization Plate Kit (Invitrogen, Carlsbad CA, USA), subsequently quantified using a Qubit dsDNA HS Kit (Invitrogen, Carlsbad CA, USA), and run on the Bioanalyzer 2100 (Agilent Technologies, Santa Clara CA, USA) to confirm amplicon length and purity. The 16 S rRNA gene amplicon library was sequenced at the KAUST BioScience Core Laboratory on the Illumina HiSeq 2500 platform using the rapid-run mode with 2 × 250 bp overlapping paired-end reads with a 10% phiX control. Determined sequencing data for this project are available under NCBI BioProject PRJNA610240. Sequence data processing.
Demultiplexed paired-end sequencing reads were processed with QIIME 2 pipeline (version 2020. 6) for quality control, error correction, and taxonomical classification  118. Briefly, a total of 25 829 809 reads were obtained from the 135 samples after demultiplexing. DADA2 119 was used for denoising, filtering, merging, and chimera removal from these sequences and to generate amplicon sequence variants (ASVs). Sequencing results were subsequently rarefied based on the sample having the smallest number of sequences, i. e. to 9 357 sequences (as described in the MiSeq SOP protocol  120  ). Taxonomic identification of ASVs was performed using the classify-sklearn method  121  via the q2feature-classifier plugin 122 against (99% of clustering) the 16 rRNA (full length) Silva SSU 138 database  123.
16S rRNA gene-based microbial community analysis. Taxonomically annotated 16S sequences were used to create bacterial community composition stacked column plots at the family level using the means of relative abundances from samples grouped by genotype and habitat (Fig. 2 ). Plots were drawn using the package 'ggplot2' in R  115. To assess the contribution of host genotype and environment to microbiome community structuring, differences in the assemblage of bacterial ASVs were tested using a two-way permutational multivariate analysis of variance (PERMANOVA) with the adonis function in the 'vegan' R package  124. The effect of host genotype and environment were also investigated separately using one-way PERMANOVA tests and the results were visualized in non-metric multidimensional scaling (nMDS) ordination plots with ellipses drawn around each group's centroid using the package 'ggplot2' in R  115  for groups with n ≥ 3 samples.
All statistical analyses were performed on Bray Curtis distances of log (x + 1) transformed ASV counts using R  116  including only groups with sufficient replication (i. e. , n ≥ 3), which resulted in the exclusion of G2 and G3 in the back reef habitat and of G1 in the mid slope. Similarity percentage (SIMPER) analyses, combined with Kruskall-Wallis tests, were conducted using the R package 'vegan'  124  to determine the degree of dissimilarity in bacterial communities between host genotypes as well as between habitats, and to determine which bacterial families were responsible for the largest portion of those dissimilarities. To confirm that the data fulfilled the requirements for SIMPER testing, multivariate tests calculating the dispersion of samples between genotypes and habitats were performed using the 'betadisper' function in 'vegan'  124.
Homogeneity of multivariate dispersion between groups (i. e. , genotypes and habitats) was tested with ANOVAs (P > 0. 05). ASVs that were consistently present in at least 80% of samples were considered members of the core microbiome  51. These ASVs were therefore designated as putative core microbiome members and their sequences were BLASTed against the GenBank database [nr/nt and 16S rRNA sequences (Bacteria and Archaea)] to identify closely related matches. We also analyzed our data to characterize changes in microbial communities related to colony size differences at the genotype level. In particular, we assessed colonies of the same genotype that were produced naturally in different habitats through wave-induced breakage, i. e. microbiomes of small recently fragmented clones versus larger clones that have been most likely fragmented earlier.
Because coral morphology aligned with environmental differences, it was not possible to determine whether microbiomes were different between growth forms. Bacterial species representative of host genotype and reef habitat. We employed the R statistical package IndicSpecies  126  to identify ASVs that were significantly associated with distinct fire coral host genotypes and/or reef habitats (Supplementary Data 4). The analysis was conducted on ASV count data. All samples were assigned to one of the six host genotypes using the command 'groups'. IndicSpecies was run using the command 'multipatt' with the function Indval. g for corrections of unequal sample sizes and 9 999 permutations to assess statistical significance. Significant ASVs were summarized (command 'summary') for each genotype separately and for all genotype combinations.
This analysis was also performed for the three habitats. Only ASVs that were highly significantly (P <0. 01) associated with one or several groups were considered. Heatmaps showing bacterial ASVs associated with a specific host genotype and/or a reef habitat were compiled using 'ggplot2'  115. Taxonomically inferred functional profiling of host genotype and reef habitat bacterial species. To better understand the potential functional profiles of specific bacterial taxa in host genotype and/or reef habitats, we applied a computational approach using the program PICRUSt2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States)  50. PICRUSt2 predicts metagenomic functional content from the 16S rRNA marker gene by estimating the genomic copy numbers of each gene family, based on the strain's phylogenetic relationship with regard to all bacteria and archaea for which sequenced genomes are available  50.
KEGG orthology (KO) metagenomes, enzyme commission (EC) metagenomes, and MetaCyc pathway abundances were predicted through the QIIME 2 implementation of PICRUSt2 in the module called q2-picrust2. Briefly, QIIME 2-compatible ASV tables for both host genotype-and reef habitat-specific bacterial ASVs (i. e. , bacterial indicator taxa) were imported in QIIME 2 format. The 16S ASVs were aligned (NSTI cutoff value of 2) to a reference phylogenetic tree of 16S sequence variants from sequenced prokaryotes. Then, the software predicted functional gene families and copy numbers for each specific ASV. During the process, the ASVs were normalized for their 16S copy number in the corresponding bacteria. Individual KEGG Ortholog groups (KOs) were summarized at KEGG-Pathway levels 1, 2, and 3 and with the MetaCyc pathway.
Acknowledgements The authors thank  G. Martin  for expert technical expertise and  Drs. M. Pende ,  H. Stenmark ,  C. Raiborg , and  S. J. Weiss  and members of P. C. lab for helpful comments during the preparation of this manuscript. C. C. was supported by a grant from the  Cancéropôle Région Ile-de-France  ( 2016-2-APD-04-ICR-1 ) to P. C. This work was supported by a donation from  Mr. T. Paulsen  ( InvaCell Project ), by  Fondation ARC pour la Recherche contre le Cancer  ( PGA1 RF20170205408 ) and  Fondation Ruban Rose (Prix Avenir 2018 ) and by institutional support from  Institut Curie and Centre National pour la Recherche Scientifique  to P. C. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. The experimental protocol and animal housing were in accordance with institutional guidelines and with the recommendations of the  French Ethics Committee  (Agreement  B75-05-18 , France).
The email indicated the objectives and the funding of the trial, its procedure and the legal rights of participants. Participants were invited to give their electronic consent and were complete an inclusion questionnaire to verify eligibility criteria. Data were therefore collected on sex, age, occupational activity and monthly income. Participants were also invited to provide information on grocery shopping frequency, including online grocery shopping frequency, back-of-pack nutritional information reading frequency and to self-estimate their nutritional knowledge. No incentive was provided to the participants. The monthly income was calculated per household unit, and participants with an income above €1200/month were excluded-corresponding approximately to individuals from the first two deciles of the income distribution in France.
Acknowledgements The authors would like to thank  Younes Essedik  (IT manager),  Nathalie Pecollo  (operational coordinator) for the design of the experimental supermarket,  Paul Flanzy  (computer technician) for computer management,  Julien Allègre  (data manager) for data extraction and data management,  Flora Demory ,  Apolline Caroux ,  Wassila Ait Haddad ,  Amaia Cherbero ,  Aurélie Gayon  for their contribution in the implementation of the experimental supermarket and University deans for their contribution and involvement in the recruitment of participants. Contributors ME, CJ and IB wrote the statistical analysis plan, analysed the data and drafted and revised the paper. SP, PD, MT, PG, CB, RP, PR, SH and EK-G analysed the data and critically revised the paper for important intellectual content.
SH and CJ designed data collection tools, implemented the study, monitored data collection for the whole study and critically revised the draft paper for important intellectual content. All authors, external and internal, had full access to all of the data (including statistical reports and tables) in the study and can take responsibility for the integrity of the data and the accuracy of the data analysis. All authors have read and approved the final manuscript. on April 15, 2022 by guest. Protected by copyright. http://bmjopen. bmj. com /BMJ Open : first published as 10. 1136/bmjopen-2020-041196 on 8 February 2021. Downloaded from.
We evaluated the performance of matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) associated with the Bruker BioTyper TM V7. 0. 0 database for the identification of 713 bacterial strains isolated from seafood products and sea water samples (ANSES B3PA collection) under culture conditions that may have been significantly different from those used to create the reference spectrum vs. the 16S rDNA sequencing. We identified 78. 8% of seafood isolates with 46. 7% at the species level (Bruker score above 2) and 21. 2% (Bruker score between 1. 7 and 2) at the genus level by the two identification methods, except for 3. 8% of isolates with a difference of identification between the two methods (Bruker score between 1. 7 and 2). There were 41. 9% isolates (Bruker score below 1.
7) with the identification at the genus level. We identified 94. 4% of seafood isolates with 16S rDNA sequencing. The MALDI-TOF allowed a better strain identification to the species level contrary to the 16s rDNA sequencing, which allowed an identification mainly to the genus level. MALDI-TOF MS in association with the Bruker database and 16S rDNA sequencing are powerful tools to identify a wide variety of bacteria from seafood but require further identification by biochemical, molecular technique or other conventional tests.
The unknown bacteria is identified by comparing its protein spectrum with databases containing reference spectra of each known bacteria, mainly reference clinical strains and collection strains (ATCC, CIP,. ) that do not reflect the strains under stressful environmental conditions. However, many databases are incomplete or contain bacterial reference spectra obtained under specific and stringent culture conditions. Indeed, variability in culture conditions, sample preparation, or strain origin can be problematic because the mass spectrum is influenced by all these factors. Several studies have investigated the use of this MALDI-TOF MS method to identify the microflora present in different food products such as in seafood  (Carrera et al. , 2013). There are few studies to identify and classify the seafood spoilage and pathogenic bacteria  (Böhme et al.
, 2011 (Böhme et al. , , 2013;; Popović et al. , 2017) , and in these studies, the strains investigated were mainly reference strains and less than 50 strains were isolated from seafood. Databases of MALDI-TOF are rich in patterns of clinical isolates, reference strains but less in patterns of seafood and environmental isolates. In this study, we evaluated the performance of MALDI-TOF MS associated with the Bruker BioTyper database for the identification of 713 bacterial strains isolated from seafood products and sea water samples vs. the 16S rDNA sequencing. Identification results generated according to each methodology used had been compared, taking into account genera and species.
75 µl of nuclease-free water (Qiagen). PCR was performed in an iCycler thermocycler (Bio-Rad, Marnes La Coquette, France) under the following conditions: 15 min at 95 • C followed by 35 cycles of 45 s at 94 • C and 45 s at 48 • C and 60 s at 72 • C. PCR products were sequenced by the Genoscreen private company (Lille, France) using the amplification primers. Alignments of nucleic acid sequences were performed by using Bioedit software. Then, a homology search against the NCBI 16S ribosomal RNA sequences database was performed with the obtained DNA sequences using BLAST algorithm. The identification was considered reliable if the identification rate was greater than or equal to 97% for the genus and 99% for the species  (Drancourt et al. , 2000). If several species belonging to the same genus match, then the identification was restricted to the genus.
Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry Analysis All isolates were grown on Mueller Hinton agar according to the specific incubation procedures (30 or 37 • C depending on the strain) and identified using a Microflex LT mass spectrometer (Bruker Daltonics, Germany), with MALDI BioTyper and FlexControl V3. 0 software. Two different sample preparation procedures were used: (i) direct transfer (spotting) of the colony onto a target plate and (ii) formic acid overlay method that consists of depositing 1 µl of 70% formic acid on direct colony spotting. Three colonies per strain and per sample preparation were tested. For the direct spotting, a colony fraction was removed with 200-µl tips and homogeneously spread on a MALDI-TOF target well. Dried deposits were overlaid with 1 µl of 10 mg/ml of αcyano-4-hydroxycinnamic acid (HCCA) matrix solution (Bruker Daltonics, Germany).
The MALDI BioTyper TM system was calibrated with a Bruker Bacterial Test Standard (Escherichia coli DH5α), and the spectra for proteins with masses between 2,000 and 20,000 Da were obtained and matched with the Bruker V7. 0. 0 database, composed of 8,223 mass spectrometry profiles (MSPs). Analyses were done with a laser frequency of 60 Hz, an acceleration voltage of 20 kV, and extraction delay time of 120 ns. For the isolate identification, the confidence score thresholds were as follows: (1) with a score above 2, there was high confidence identification to genus and species levels; (2) with a score between 1. 7 and 2, there was probable identification of genus level and no identification of species level; and (3) with a score below 1. 7, there normally was not any reliability of identification  (DeMarco and Burnham, 2014).
RESULTS Seven hundred thirteen bacterial strains were analyzed by MALDI-TOF MS and blasted with the reference strains, mainly clinical isolates, available in the Bruker BioTyper TM V7. 0. 0 database. Among the 713 strains studied, 78. 8% of isolates (562/713) were identified either at genus or species level (Tables  1, 2 ). For 46. 8% of the strains (334/713), identifications were carried out with a Bruker score above 2, which means high confidence identification to the species level (Table  1 ). We identified 293 strains Gram + and 118 bacteria Gram-. 2 ). We identified 95 isolates Gram+ and 56 isolates Gram-. Concerning the 334 strains identified to the species level by MALDI-TOF MS (Bruker score above 2), the identification of 65. 8% of the isolates (220/334) was confirmed at the genus level and 23.
Nevertheless, the other Psychrobacter sp. isolates (72%) have been identified at the Psychrobacter celer species. For the 12 other genera, 34 species were identified by In the 713 strains analyzed by MALDI-TOF MS, 3. 8% of isolates (27/713) had an identification at the species level with a Bruker score between 1. 7 and 2, but these results had not been confirmed by 16S rDNA sequencing results (Table  3 ). In fact, the genus level was validated by the 16S rDNA reference strain sequences but not the species level. Based on species identification levels of the 16S rDNA sequencing results, four species had no reference spectrum in the Bruker BioTyper TM database. In contrast, the results of both identification techniques were identical at the Bacillus genus level for five strains but were different at the species level.
In fact, the identified species were B. pumilus by 16S rDNA vs. Bacillus altitudinis by MALDI-TOF with a Bruker score of 1. 888 (one strain), Bacillus nealsonii by 16S rDNA vs. Bacillus circulans by MALDI-TOF with a Bruker score of 1. 713 (two strains), Bacillus Safensis by 16S rDNA vs. Bacillus pumilus by MALDI-TOF with a Bruker score of 1. 725 (one strain) and Bacillus aquimaris by 16S rDNA vs. Bacillus vietnamensis by MALDI-TOF with a Bruker score of 1. 865 (one strain). There had been a disagreement of species identification between the two techniques used, although the   4 ). Two-sample preparation procedures have been applied to the 124 strains: direct transfer (spotting) and acid extraction. We observed that the best MALDI-TOF MS identification scores (the score nearest 1. 7) were obtained with the direct transfer procedure and with the acid extraction procedure for 73 strains and 51 strains, respectively (data not shown).
Among these 124 strains, the identification of 41. 9% of isolates (52/124) was validated at the genus level by 16S rDNA sequencing (40 Gram+ and 12 Gram-). For three of these 52 strains, the identification had even been down to the species level by both identification techniques (Bacillus firmus, Bacillus vallismortis, and S. rhizophila). In contrast, for two genera (124 strains) and 10 species (50 strains) identified by 16S rDNA sequencing, the reference spectrum of these genera and species was not present in the Bruker database. These genera were Planococcus sp. , and Salinicoccus sp. , and the species were Bacillus oceanisediminis, Desemzia incerta, Exiguobacterium acetylicum, Exiguobacterium aestuarii, Exiguobacterium profundum, Microbacterium esteraromaticum, Psychrobacter alimentarius, Psychrobacter celer, Psychrobacter faecalis, and Shewanella indica.
To our knowledge, this was the first study to identify a great number of seafood-isolated strains (713 isolates) with MALDI-TOF MS in association with the Bruker Biotyper V7. 0. 0 database vs. 16S rDNA sequencing. In fact, the studies of identification comparison between the conventional microbiological and/or molecular biological methods and the MALDI-TOF MS were mostly for clinically relevant bacteria and/or for the specific species or genera of bacteria (Bacillus, Pseudomonas, Vibrio. )  (Popović et al. , 2017). MALDI-TOF MS has been shown to be a competent tool for bacterial species differentiation due to the resulting highly specific spectrum, named fingerprints  (Giebel et al. , 2010). The Bruker BioTyper TM database was mainly composed of human pathogen spectra, and there are few other databases with specific spectra of food or environment isolates.