dataesr/scientific-paragraphs-categorization huggingface dataset:
The DNA of the aphids and their microbiota was extracted using the Qiagen DNeasy Blood and Tissue Kit (Qiagen, Hilden, Germany) following the manufacturer's instructions and sequenced in paired end using Illumina HiSeq 2000 instruments, resulting in 2× 100 bp reads with a mean insert size of 250 bp. The average read depth for the pea aphid genome was 15× for individual sequencing (42. 5 million reads on average) and ranged from 20 to 50× for pool sequencing (197. 5 million reads on average). FastQC files were generated for each sample, and no anomaly in the sequencing data was observed. The FastQ files of the paired reads from the 50 samples are stored and publicly available at the Sequence Read Archive of the National Center for Biotechnology Information database, under the BioProject IDs PRJNA255937, PRJNA385905, and PRJNA454786.
Mapping-based disentanglement of holobiont genomes Sequencing of both host and microbial DNA produces metagenomic datasets, containing reads originating from different organisms. This metagenomic context was dealt with by mapping read sets using BWA-MEM  [43]  with default parameters against a set of reference genomes, including the pea aphid nuclear and mitochondrial genomes, the primary symbiont genome (Buchnera aphidicola), and genomes of known pea aphid secondary symbionts, when available. This was the case for Hamiltonella defensa 5A, Serratia symbiotica Tucson, and Rickettsiella viridis and Regiella insecticola 5. 15. For the Rickettsia symbiont, no closely related reference genome was available and we produced our own reference genome by de novo assembly, as explained in the paragraph below.
bellii is 1. 5 Mb long and Rickettsia sp. strain MEAM1 is 1. 24 Mb), organized in 327 contigs, and had a N50 of 4483 bp. Eighty-two percent of complete genes were found using Busco v3. 0. 1 and the bacteria_odb9 gene set, which is very close to the 83. 7% obtained for the reference genome of Rickettsia bellii. Compared to Rickettsia bellii, we observed a major improvement of the genome coverage as 84% more reads mapped on the newly assembled genome across the whole dataset.
Analysis and taxonomic assignation of unmapped reads Unmapped reads were extracted using Samtools  [47] , and low-quality reads were removed using Trimmomatic  [50]  with the following parameters: LEADING:3, TRAIL-ING:3, SLIDINGWINDOW:4:15, and MINLEN:36. Remaining unmapped reads were taxonomically assigned using Centrifuge  [51]. Only assignation hits larger than 40 base pairs were kept. Results were visualized using the Pavian R package  [52].
Samtools mpileup  [47]  was used with options "-t DP,DPR" on the alignments to detect both SNPs and indels, and the coverage of the different alleles was reported. The generated bcf file was processed using bcftools  [53]  with options "-mv -Ov". Abundance tables of reference and alternative alleles for each polymorphic site and for each sample were extracted for further filtering using vcftools  [54]  and processed using a custom R script (available on the https://aphid-microbiome. netlify. com). In order to remove false positive variants due to sequencing errors, rare variants were removed by applying two coverage filters: for each sample, variants covered by less than four reads or with less than 10% frequency were removed. Regions with exceptionally high or low coverage were excluded from the analysis.
Phylogenetic inference Variant frequencies were used to compute the variant profile of each sample by selecting the most abundant allele at each site. In the case of equally covered alleles, the reference allele was kept. This situation made it difficult to determine the most abundant genotype in the sample but was rare in our dataset. We therefore decided to remove from the analysis samples in which more than 5% of variable sites yielded alleles with equal abundances. It was the case for three pool sequencing samples with low symbiotic coverage. To investigate the evolutionary relationships between the genomes of the different samples, a phylogenomic analysis on a set of gene encoding membrane proteins was performed when an annotated reference genome was available. We first selected a list of genes, in order to compute the putative sequences for these genes in all samples.
The Uniprot database was queried to retrieve DNA sequences of membrane protein transcripts (under the "Cell membrane" keyword) for the different studied symbionts (the complete list of genes used can be found in Additional file 3). Membrane proteins were selected as they are assumed to show a higher mutation rate than usual phylogenetic markers  [55]  and therefore are more appropriate to capture recent phylogenetic events. This query resulted in sets of 96, 118, 141, and 96 genes for B. aphidicola, H. defensa, S. symbiotica, and R. insecticola, respectively. For each sample, the putative sequences of the selected proteins were inferred by replacing the reference alleles by the alternative alleles associated to the different variant profiles. The gene sequences of each selected protein were aligned using MAFFT  [56]  (v7.
310, linsi mode), and the resulting multiple alignments were concatenated. The lengths of the alignments for the analyzed symbionts were 92,293 bp for B. aphidicola, 118,344 bp for H. defensa, 100,027 bp for R. insecticola, and 144,360 bp for S. symbiotica. To validate that our alignments were not subject to substitution saturation, a Xia's test was run, as implemented in DAMBE6  [57]. Because most software of phylogenetic inference struggle to estimate branch lengths for identical sequences, we pre-processed our concatenated alignments by keeping only one sequence for each set of identical sequences. We used RaxML  [58]  (version 8. 2. 10, options -f a -# 1000 -m GTRGAMMA), a phylogenetic inference program based on maximum likelihood method, to infer the phylogeny of the samples of the considered genes.
Tree topologies were visually compared between the gene set and whole genome approaches. For F. symbiotica, Rickettsia sp. , R. viridis, and Spiroplasma sp. , we did not perform a gene-based phylogeny since their reference genomes are not well assembled nor annotated. In that case, neighbor joining was performed on whole genome variant profiles to infer phylogenetic relationships between samples. Outgroups were used to root the phylogenetic trees. For B. aphidicola, we used sequencing data of two Japanese A. pisum lineages, known to be highly divergent from European lineages  [33]. For other symbionts, we used close-related symbiont species: H. defensa from the whitefly Bemisia tabaci (GenBank 2,777,848), S. symbiotica SCt-Vlc from the conifer aphid Cinara tujafilina (FR904230), Spiroplasma melliferum KC3 from Apis mellifera (GCA_000236085.
69%. The nature of those unmapped reads was further explored by conducting a taxonomic assignation of such reads with Centrifuge (version 1. 0. 3) [51]  and its default database. Overall, only 4. 9% of the unmapped reads were assigned to a taxon. The taxonomic assignation of unmapped reads is summarized in Additional file 4 and can be explored for all samples on the website https://aphid-microbiome. netlify. com/. It is in accordance with mapping results. Some reads of host or symbiotic origins that were not mapped to the appropriate reference genome were however accurately assigned by Centrifuge. Other taxa were also found by Centrifuge assignation, either because of over-assignation by the program or because some environmental organisms were sequenced along with the pea aphid and its symbionts.
Different levels of intra-specific diversity for the pea aphid symbionts The overall genomic diversity of the selected samples was estimated for each symbiont by measuring the density of variable sites between the two most different symbiont genomes in the dataset. Only pooled samples were considered in this analysis, in order to have a more comparable sample size for each symbiont. Variant calling results are summarized in Table  2. They show strong contrasts in genomic diversity between the different symbiont taxa associated with the pea aphid complex. H. defensa and R. insecticola showed the highest diversity, with 12. 6 and 16. 8 variants per kilobase (kb), respectively. Conversely, genomic diversity was extremely low for R. viridis, with an average of 0. 027 variants per kb. The other symbionts (B.
aphidicola, F. symbiotica, Spiroplasma sp. , Rickettsia sp. , and S. symbiotica) showed intermediate levels of genomic diversity (with respectively 3. 0, 1. 59, 1. 28, 1. 19 and 1. 0 variants per kb). Consequently, the Fig. 1  Presence/absence pattern for bacterial symbionts as detected in the metagenomic dataset. Pea aphid individuals (ind) and populations (pool) were analyzed. Empty circles indicate a coverage greater than 1×. Filled circles indicate a coverage greater than 10×, enabling phylogenetic analysis. A. pisum and Buchnera aphidicola genomes were detected in every sample lengths of the branches of the phylogenetic trees built for these various symbionts were highly variable.
Phylogenomic analysis of Buchnera aphidicola from the pea aphid complex By analyzing genomic variation over the whole genome of B. aphidicola, we built a well-supported phylogeny of the pea aphid obligatory symbiont. No substitution saturation was detected using the Xia's test  [57]  (see Additional file 5). Figure  2  shows the results of the phylogenomic analysis for B. aphidicola across all datasets, using maximum likelihood-based inference on a 96 gene set alignment. The tree topology obtained from the gene set was compared with a whole genome variant profile clustering. Overall, the two phylogenetic methods gave similar results, as shown in Additional file 6. The few mismatches observed between the two topologies mainly involved nodes with low support in both trees. As previously observed using partial sequences of pseudogenes data  [33] , B.
Phylogenetic insights on the evolutionary histories of host-secondary symbiont associations We then examined the evolutionary histories of the associations between secondary symbionts and their pea aphid hosts by comparing one by one the matriline phylogeny reconstructed from B. aphidicola with the phylogeny of each of the seven secondary symbionts detected with sufficient coverage in our metagenomics dataset. Visual comparison of the matriline phylogeny with H. defensa phylogeny revealed some congruent nodes but also several differences in tree topologies indicating frequent horizontal transfers of this symbiont in the pea aphid complex (Fig. 3 ). Reconciliation analyses detected nine possible events of host shifts and six cospeciation events, which yielded a co-diversification scenario that is less costly than expected by chance.
Intra-host coexistence of two Regiella insecticola strains Investigation of inter-sample phylogenetic relationships was led by considering the most abundant alleles for each sample. However, some samples may be polymorphic at a b some sites, with both the reference and alternative alleles covered in metagenomic dataset. While intra-sample genomic variability is expected for pooled samples, which originated each from a diverse host population, it would be more surprising for individual sequencing samples. However, we observed that genome sequences from two distinct clones of the Trifolium biotype (Tp_ind1 and Tp_ind2) showed a high number (32,000) of intra-sample polymorphic sites along the R. insecticola genome. These two samples showed no sign of polymorphism for the primary symbiont and mitochondrial genomes, excluding the hypothesis of contamination during the sequence data production.
Discussion We present here a framework to explore multi-scale genomic diversity in holobiont systems of low complexity, which is generally the case of insect holobionts. We applied this approach to metagenomic datasets of the pea aphid complex by considering microbial variation across host biotypes, among individuals of the same biotype and within individual aphids. This work allowed to extract more than 99% of the metagenomic information and to draw a complete inventory of microbes associated to the pea aphid complex, revealing a microbiota dominated by a few bacterial symbionts. Our approach also revealed for the first time a large genomic diversity among A. pisum symbionts, with different diversity patterns between symbiont taxa presumably reflecting distinct evolutionary histories, genomic features, transmission patterns, and ecological influences across pea aphid biotype-symbiont associations.
When distant reference genomes are used for mapping, highly divergent regions and large insertions or deletions obviously limit the assignation success rate. Overall, mapping of metagenomics reads on a set of reference genomes (when available) or de novo assembled genomes (when coverage is sufficient), followed by a strain-level analysis of genomic variation appears to be an appropriate characterization method in the case of the pea aphid holobiont. A large number of aphid samples were sequenced in order to investigate microbial diversity across the pea aphid complex of biotypes. However, sequencing data from host aphids did not allow accessing directly to individual bacterial genotypes, and we had to build genotypes based on the most abundant alleles in each bacterial population. In our dataset, individual sequencing samples had either a low intra-sample polymorphism or a mix of genotypes we could easily disentangle (as for example R.
Multi-scale diversity inventory of an holobiont Previous studies on the pea aphid's microbiota focused on the detection of symbionts using 16S rRNA PCR-based detection or 16S amplicon sequencing  [34, 39]. The drawbacks of these methods are that they are restricted to bacteria, have generally low taxonomic resolution, suffer from several biases due to DNA amplification, and may be unable to identify new microbial partners  [68]. To overcome these limitations, we used shotgun metagenomic sequencing, which captures whole genomic information about the host and its associated microbial community. We successfully assigned most of the reads to host and symbiont reference genomes forming the pea aphid holobiont and checked that no new bacterial symbiont was abundant in unmapped reads. Also, we found no evidence for the occurrence of Wolbachia in our large metagenomic dataset though this symbiont has been reported in A.
pisum in three previous studies  [34, 69, 70]. One explanation could be that none of the pea aphids used for individual or pooled resequencing projects was infected by this symbiont. Alternatively, detection of Wolbachia in previous studies could result from artifacts or DNA amplification from aphid endoparasitoids which may be infected by this symbiont. Because DNA was extracted from aphid clonal lineages cultured in laboratory conditions for two generations (to avoid contamination from aphid parasite microbiota and limit environmental microbes), only the inherited part of the microbiota was sequenced. In contrast with a previous study based on 16S rRNA sequencing  [34] , no gut associate microbe was found in our metagenomic dataset, suggesting either a low prevalence of such microbes in pea aphid populations, their loss in culture because of poor vertical transmission, or an artifact of 16S rRNA data.
This work paves the way for new studies relying on metabolic and functional approaches and aiming to examine how genomic variation in microbiota affects host fitness and phenotypic traits. Moreover, a full understanding of the evolutionary history and ecology of symbiotic associations requires a larger investigation of the sources of genomic diversity at different geographical, temporal, and trophic scales. Although the metagenomic framework we developed here for the pea aphid system yielded significant knowledge improvements in patterns of genomic diversity and evolution in host-symbiont associations, we pinpointed some limitations in our approach such as the availability of reference genomes and the difficulty to handle metagenomic data of high complexity. Methods to analyze fine-scale diversity from metagenomic dataset are still rare and require either well annotated reference genomes or simple communities where organisms are easy to disentangle.
Acknowledgements Authors warmly thank  Jean Peccoud  for advice on approaches to explore the evolutionary dynamics of pea aphid symbiont associations and the  GenOuest BioInformatics Platform  that provided the computing resources necessary for bioinformatics analyses. We also thank the two anonymous referees for their helpful constructive comments that greatly contributed to improving this paper. Funding CG was supported by  Université of Rennes  1 through a PhD grant. This work was supported by the  Plant Health and Environment division of INRA  and the  ANR Speciaphid  ( ANR-11-BSV7-005-01 ) to JCS.
Patients will be randomly allocated in a 1:1 ratio to receive either combined oral and intravenous antimicrobial prophylaxis or intravenous antibiotic prophylaxis alone, stratified by centre, the surgical procedure (laparoscopic or open surgery) and according to the surgical skin antisepsis (chlorexidinealcohol or povidione-iodine alcoholic solution). The primary endpoint is the rate of SSI by day 30 following surgery, with SSI defined by the criteria developed by the Centers for Disease Control and Prevention. Data will be analysed on the intention-to-treat principle and a per-protocol basis. Ethics and dissemination COMBINE trial has been approved by an independent ethics committee for all study centres. Participant recruitment began in May 2016. Results will be published in international peer-reviewed medical journals.
trial registration number EudraCT 2015-002559-84; NCT02618720. bACkground Surgical site infections (SSIs) account for 30% of all healthcare-associated infections among hospitalised patients, and up to 5% of surgical patients. 1 2 ] [5] [6]  SSIs are associated with a longer hospital stay, a fivefold likelihood of postoperative readmission after hospital discharge and a twofold to threefold increase in costs of care,  [6] [7] [8] [9]  and strengths and limitations of this study Open Access are an independent predictor of mortality in surgical patients. 10 s reiterated by the 2016 WHO guidelines,  11  administration of systemic antimicrobial prophylaxis before surgical incision, aimed at reducing the bacterial load at a level that is no longer sufficient to establish an infection, is recommended to reduce the risk of SSI.
Interim analysis An interim analysis will be performed after 460 patients using the Lan and DeMets method (East software, Cytel, Cambridge, Massachusetts, USA). The Data Monitoring and Safety Committee (DSMC) may recommend that the trial should be stopped if it finds that the continued conduct of the trial clearly compromises patient safety (in case of serious adverse reactions (SARs) or suspected unexpected serious adverse reaction (SUSARs)). The steering committee (SC) will be responsible to continue, hold or stop the study based on the data monitoring and safety committee (DMSC) recommendations.
Missing values The prevalence and pattern of missing values, if any, will collected and analysed. If the frequency of missing data is >5%, additional analyses will be performed using the multiple imputation method. 41 ta registration Data will be entered into the web-based eCRF (CSOnline, Clinsight) by trial or clinical personnel under the supervision of the trial site investigators at each participating centre. From the eCRF, the trial database will be established. Paper case report form will be used in case of technical difficulties with the eCRF. Data collection will be monitored by trained research coordinators. The following data will be registered:  Postoperative data (until hospital discharge) Patients will be assessed once daily during the entire hospital stay (until hospital discharge) and at the time of follow-up evaluation (30 days after surgery).
In case of patient's discharge from hospital prior to day 30 after surgery, trial or clinical research staff members will call the patients, using a dedicated questionnaire (online supplementary file 3), once a week during the 30-day follow-up period and will Prerandomisation Open Access arrange for prompt clinical evaluation in case of suspected infection. Whenever infection will be suspected or diagnosed, site investigators unaware of the patients' group assignments will assess the seriousness of all events, and all clinically relevant microbiological samples will be cultured. The following data will be collected: ► postoperative care pathway ( Ninety days after surgery ► survival status (if the patient is deceased, date of death).
If this is achieved, the participant will be included in the final analyses. If the participant declines, all data from that patient will be destroyed, and a new patient will be randomised to obtain the full sample size. All randomised patients will be reported, and all data available with consent will be used in the analyses. If appropriate, missing data will be handled in accordance with multiple imputation procedures if missing data are greater than 5%. safety All adverse events thought to be related to the trial medication will be reported to the trial coordinating centre. According to the French Public Health Code, all suspected unexpected serious adverse events will be reported to the Agence National de Securite du Medicament (ANSM). In addition, this information will be submitted to the DMSC.
No formal criteria are set for stopping the study. Nevertheless, recommendations for pausing or stopping the study will be made by the DMSC in case of SARs and SUSAR. Most adverse reactions are specified in the product characteristics of ornidazole. The SC will be responsible to continue, hold or stop the study based on the DMSC recommendations. The DMSC will be advisory to the SC. The SC will be responsible for promptly reviewing the DMSC recommendations, to decide whether to continue or terminate Open Access the trial, and to determine whether amendments to the protocol or changes in trial conduct are required. data handling and retention Data will be entered into an web-based eCRF by trial personnel. Each site will only have access to site-specific data. Each patient will receive a unique trial identification number.
Only the investigators and research team will have access to any protected health information of study participants and any study data. Data will be handled according to the French law. All original records (including consent forms, reports of SUSARs and relevant correspondences) will be archived at trial sites for 15 years. The clean trial database file will be anonymised and maintained for 15 years. Only the principal investigators and the statistician will have access to the final data set. Ethics and dissemination COMBINE trial is approved by the Institutional Review Board of the University Hospital of Clermont-Ferrand (France). COMBINE trial is registered in the European Clinical Trials Database (EudraCT 2015-002559-84) and at ClinicalTrials. gov with the trial identification number NCT02618720.
Enrolment and timeline The patients are expected to be included from 14 French university and non-university hospitals during a 2-year period starting in May 2016. ► 2014-2016: protocol, approvals from ethics committee and trial tool development (eCRF and randomisation system). ► 2016 to 2018: inclusion of patients. ► 2018: cleaning and closure of the database. ► Early 2019: data analyses and writing of the manuscript and submission for publication.