dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparisons were performed within generation/sex specific subdatasets (see Table  S3  for full results). Primers (Eurofins Genomics, Germany) were designed using Primer3Plus (http://www. bioinformatics. nl/cgi-bin/primer3plus/primer3plus. cgi) (Table  S2 ). cDNA was synthetized from 400-500 ng of RNA template and each qPCR reaction included 2X SYBR® FAST MasterMix (KAPA Biosystems), 200 nM of each primer, 2 μL of 1/10 diluted cDNA completed to 12 μL with MilliQ water. Potential gDNA contamination was controlled with purified RNA (no reverse transcriptase control) and each reaction was run in technical duplicates. Results were normalized to the expression of rpl13a and b2m as they showed the highest stability among 4 investigated genes (i. e. eef1a1, b2m, actb1 and rpl13a) (data not shown).
RNA-Sequencing data analysis Quality of the fastq files was checked using the FastQC software (v0. 11. 8)  (Andrews, 2010)  and reads were mapped to the last version of the zebrafish genome (Ensembl GRCz11, annotation GRCz11. 97) with StAR (v2. 7. 0)  (Dobin et al. , 2013). QC statistics are available in Excel Table  S1. Read count was performed with HTSeq (v0. 11. 2)  (Anders et al. , 2015)  and differential expression analysis with DESeq2 (v1. 24. 0)  (Love et al. , 2014). A gene showing an adjusted p-value for multiple comparisons (padj)≤0. 05 (Benjamini-Hochberg correction) was considered as a significantly differentially expressed gene (DEG). Gene Set Enrichment Analysis (GSEA v4. 0. 3) was used for functional enrichment against Gene Ontology (GO, 2019-07-31) and REACTOME (2019-10-02) databases  (Reimand et al.
RRBS data analysis All analyses were done on the Galaxy instance of the UMR 5244 -"Interactions Hôtes-Pathogènes-Environnements" laboratory http://bioinfo. univ-perp. fr (Afgan et al. , 2018). Quality of the sequencing data was evaluated using FastQC (v0. 71), and one replicate from F0 males was discarded due to poor sequencing quality. Adaptors were automatically trimmed using TrimGalore! (v0. 4. 3. 1) (Krueger, 2012)  and reads were aligned with Bismark (v0. 22. 1) (Krueger and Andrews, 2011)  to the GRCz11 version of the zebrafish genome, with the adjusted parameter "L,0,-0. 6" for optimal alignment  (Kamstra et al. , 2017). Methylation calling was performed with Bismark extractor. QC statistics are available in the Excel Table  S8. Data were normalized to median read count and cytosine positions showing a coverage below 5 and above the 99.
9 percentile were discarded. At this stage, we observed a batch effect between the two flow cells that were used in the experiment (Figure  S1A ). The built-in methylKit batch correction (R package v1. 12)  (Akalin et al. , 2012)  is based on a principal component association of available metadata. This type of batch correction has limited application, particularly with high dimensional datasets, as it focuses on the removal of a whole principal component that may contain more than just variation associated to batch. For this reason, the authors provided ways of extracting and reimporting batch-corrected values into their framework. We tested the built-in version and found that overall batch specific differences were still observed (Figure  S1B ). Currently, no fully appropriate batch correction methodology has been published for methylation data.
For this reason, we extracted the percentage of methylation for each identified position and used an Empirical Bayes Framework previously developed to correct for batch effects in continuous datasets, ComBat  (Johnson et al. , 2006). We provided the methodology with the batch information as well as the model matrix relating to sex, treatment, and generation of the fish to remove as much technical information as possible while preserving biological information (Figure  S1C ). The resulting matrix was rescaled to 0-100 to be integrated back into a methylKit object. To test whether the correction removed any biological information, we compared the results from a within batch differentially methylated position analysis. This showed that 100% of the regions identified after correction were also identified in the dataset before correction and that there was a >97% correlation between differential methylation values from both datasets (Figure  S2 ), showing that the batch correction was successful and did not create false-positive DMRs.
DMRs were associated to the closest transcriptional start site using the genomation package with default parameters (v. 1. 18. 0) (Akalin et al. , 2015). Functional enrichment was performed using gprofiler2 (v. 0. 1. 9) (Reimand et al. , 2018)  against the Gene Ontology and Reactome databases. A custom background containing associated genes from the full list of 300 bp tiles was used. A gene set was considered significantly enriched at a padj≤0. 05. Both RNA-Seq and RRBS data have been deposited in NCBI's Gene Expression Omnibus  (Edgar et al. , 2002)  and are accessible through GEO Series accession number GSE154020 (reviewer access token czwvcykovjajxkj) and GSE154206 (reviewer access token sxuhiycgbryxhkv), respectively.
| Data set Our primary genomic data set consisted of 26 genomes from samples selected to best represent the known global geographic and genetic diversity of this species (Figure  1 ;  Foote et al. , 2019). In addition to previously published, low-coverage whole genome sequence data, new paired-end libraries were built from 20 of the samples using the BEST library build method  (Carøe et al. , 2018)  from genomic DNA that was sheared to an average size of 350-550 bp using a Covaris ME220. Libraries were subsequently dual indexed and amplified for 12 cycles, then purified using ampure beads (Agilent Technologies). The DNA concentration of the libraries was measured using an Agilent TapeStation (Agilent Technologies); these were then equimolarly pooled and sequenced across eight lanes of an Illumina HiSeq4000 platform.
| Estimating theta (θ) from individual genomes To understand the impact of ancestral demography on contemporary killer whale populations, we estimated variation in heterozygosity among our global data set of individuals. We estimated heterozygosity of autosomal regions from individual diploid genomes. Under the infinite sites model  (Kimura, 1969) , individual heterozygosity is a good, unbiased estimator of the population mutation rate, theta  (θ; Watterson, 1975). Theta was estimated directly from the filtered bam files using the maximum likelihood estimator  (Lynch, 2008)  implemented in mlrho v. 2. 9. (Haubold et al. , 2010).
The called genotypes in T-ped format and normalised genotype likelihoods in Beagle format were compared to assess confidence in the PLINK input file. Additional filtering steps used during the calling of SNPs were the removal of reads of poor mapping quality (MAPQ < 30), removing sites with low base quality scores (q < 20), discarding reads that did not map uniquely, adjusting q-scores around indels, adjusting minimum quality score to 50 for excessive mismatches, and discarding bad reads (flag ≥ 256). Inferred SNPs were lightly pruned based on linkage disequilibrium (LD) r 2 > 0. 9 using PLINK, which has been found to improve the accuracy of detecting autozygous ROHs  (Howrigan et al. , 2011). We estimated ROH from pruned and unpruned data and found minimal qualitative difference with our data, and therefore proceeded with unpruned data.
| Genome sequences Previously published short-read data sequenced to ~5× effective coverage (depth of sequencing post QC filtering) of the autosomal region of the killer whale genome  (Foote et al. , 2019)  were combined with published data from a Norwegian  (Foote et al. , 2015)  and Pacific resident killer whale  (Moura et al. , 2014)   and PRJNA236163 respectively. A complete table of BioProject, BioSample and SRA accession numbers are provided in Table  S1.
| MATERIAL S AND ME THODS The majority of experimental work on facultative symbionts has been done using aphids (Hemiptera: Sternorrhyncha: Aphidoidea), but there are studies on whiteflies (Hemiptera: Sternorrhyncha: Aleyrodoidea) and, increasingly on Heteropterans (Hemiptera: Heteroptera). We searched for relevant literature using keyword searches in Web of Science finding articles published until the end of 2019 (details for each group below). Literature and data pertaining to other herbivorous sucking insects was searched for, but with few articles relevant to our meta-analyses; data for three Auchenorrhyncha species hosting symbionts was extracted but this taxa does not belong to any of the three identified groups and was insufficient to analyse independently  (Li et al. , 2019; Shentu et al.
, 2019; Yoshida et al. , 2019). The main inclusion criteria across the groups were: (a) data on at least one insect species, (b) an experimental test of facultative symbiont effects on insects, either experimentally cured of a symbiont, or inoculated with one (Experimental), or a comparison of fieldcollected infected and uninfected aphids (Natural), (c) any of the following types of variables tested: any behaviour, growth, fecundity, survival, or parasitism-resistance related variable, and (d) data on means, an estimation of variation, and sample size. In addition, we only included studies that used artificial curing methods (e. g. , antibiotics) with demonstrated negative effects on symbionts, but not their insect hosts. If novel methods to remove symbionts were used, we only included studies that demonstrated that the technique used had no direct negative effects on insect fitness.
Among them, 16 articles included obligatory associations as suggested by the authors, or as revealed by total nymph mortality, or adult infertility when the symbiont was removed (Supporting Information Appendix 1b). We therefore exclude these obligatory associations; this criterion inherently requires studies to report data on insect survival after symbiont removal. The final data set consisted of 22 articles (from 2002 to 2019, see Appendix 1a), and the only variables of interest with sufficient data were adult mass (fresh or dry weight), adult size (length of abdomen, thorax, tibia or total body), time to adulthood, fecundity and survival.
| Aphid meta-analysis (Hemiptera: Sternorrhyncha: Aphidoidea) We used the terms: ("aphid*" AND ("Hamiltonella" OR "Regiella" OR "Serratia" OR "Rickettsia" OR "Rickettsiella" OR "Sprioplasma" OR "Arsenophonus" OR "Wolbachia" OR "X-type" OR "PAXS")). This resulted in 512 potential articles. Following the inclusion criteria, we extracted data from 68 articles (1997-2019; Appendix 3) and were able to analyse the effect of bacterial symbionts on fresh adult weight, age at first reproduction, lifespan, number of offspring, and the proportion of aphids surviving parasitic wasp attack. We were also able to extract data on the experimental host plant and plant from which the aphid was originally collected (related to aphid biotype in pea aphids). We pooled data within studies across aphid genotypes and symbiont strains removing effects of within-species genetic variation, yet data remained separated by aphid species, symbiont species, and host plant (experimental and of original collection).
| Statistical analysis The meta-analysis was conducted in R v3. 6. 3 (R Core Team, 2020) in RStudio v1. 2. 5033 (RStudio Team, 2020) using the package metafor  (Viechtbauer, 2010) ; all data and R scripts can be accessed in Dryad  (Zytynska et al. , 2021). The standardised mean difference was used with unbiased estimates of the sampling variances (SMDH, giving Hedges' g). This measure was used since it gives a direct effect size comparison of the treated (infected with a symbiont) to untreated (no symbiont control) data. For the analysis of the main variables, we only present data where there are at least three data points from three independent studies (i. e. , not from the same research group). A priori power analyses (medium effect size d = 0. 5) (Table  S1 ) were performed in R following the script of  Quintana and Tiebel (2019)  for the main variables, and subsets of data by insect and symbiont species.
Data bias was assessed by testing the funnel-plot asymmetry for a random-effects model and Eggers test for publication bias in the metafor R package (Figures  S1, S2 ). We also used P-curve analysis in the R package dmetar  (Harrer et al. , 2019)  as an alternative to test for bias that shows the distribution of significant effect sizes to identify potential p-hacking (Figure  S1 ). P-hacking occurs when data or experimental design is manipulated to produce significant results below significant p-values (usually 0. 05) and can be detected when there is a large proportion of values close to the significance level so that P-curves are skewed towards these values (Figure  S1 ). For the results figures, the mean effect size and 95% confidence intervals are presented; the mean effect size was considered significantly different from 0 if its 95% CI did not include zero, and level of significance given from model outputs.
For the aphid data set, data were also subset into those where insect lines had been directly compared through experimental curing/ infecting (Experimental) or a comparison of field-collected infected and uninfected insects (Natural), and analyses run as above on each separate data set. We further subset the data by aphid trait (i. e. , one model for each trait) and explored differences across aphid or symbiont species within these by including "aphid species" or "symbiont species" as fixed effect moderators. An interaction model was considered but in no case was there sufficient data for this to be meaningful, thus we analysed aphid species and symbiont species separately. For cases where there was sufficient data (here deemed as ≥3 data points), we also analysed the effect of symbiont species within aphid species; however, as the data subsets get smaller the interpretation needs to be done with caution.
For the model pea aphid (Acyrthosiphon pisum) we also analysed the effect of symbionts across host plants (experimental host plant and host-plant from which the aphid was originally collected) as pea aphids belong to genetically differentiated host-plant associated populations and these particularly associate to specific symbiont species. The effect of experimental host plant could only be analysed for plant hosts Medicago sativa and Vicia faba (a universallyaccepted host plant for all pea aphid biotypes  (Ferrari et al. , 2006) ). We restricted our analyses to aphid lines collected from M. sativa (Medicago/alfalfa biotype) to reduce variation due to aphid biotypes. We analysed the effect of all symbionts (combined due to lack of sufficient replication of individual symbionts) on aphid fecundity and parasitism, for which there was more than three data points from at least two independent studies.
The effect of hostplant of collection was analysed for pea aphids hosting Hamiltonella defensa symbionts for aphids collected from M. sativa and Ononis spinosa plants. We restricted the analysis to experiments using the universal V. faba plant as the experimental host plant to reduce confounding effects of unbalanced replication across other host plants. There was sufficient data to analyse aphid fecundity and parasitism, with at least three data points from three independent studies for each comparison. Lastly, the effect of hosting multiple symbionts was also possible to analyse for pea aphids. This was possible for data where either a second symbiont was added by microinjection (N = 14 data points), or where collected clones of aphids varied in symbiont status (N = 12 data points). Due to lack of data, it was not possible to analyse an interaction between any two symbionts.
These studies primarily test the effect of the symbiont Burkholderia, with some studies on Pantoea and Pandoraea, all of them located in specialized structures in the gut; however, in most studies the symbiont species was not fully identified. The whitefly data  (years 2011-2015)  contains studies on different biotypes of a single whitefly species (Bemisia tabaci), hosting Hamiltonella, Rickettsia and Wolbachia symbionts; we found no additional relevant studies since 2016. Experimental manipulation of aphid symbionts increased strongly in 2010 and, while new studies are still being published, the numbers per year have remained similar in the last four years (probably due to research now focusing on understanding the mechanism of effects). The aphid data set included 13 aphid species, from eight genera (Acyrthosiphon, Aphis, Macrosiphum, Megoura, Myzus, Obtusicauda, Rhopalosiphum, Sitobion) and eight symbiont species (Hamiltonella defensa, Regiella insecticola, Serratia symbiotica, Rickettsia, Ricketsiella, Spiroplasma, Fukatsia symbiotica (X-type, or PAXS), and Arsenophonus).
Hosting of multiple symbionts was expected to increase costs, but despite the limited amount of data available at least one symbiont pairing (Hamiltonella defensa + Fukatsia symbiotica) maintained benefits while potentially reducing overall fitness costs. Aphid studies contributed 60% of the total data points, with a strong bias towards a few well-studied species, such as the model pea aphid (Acyrthosiphon pisum), black-bean aphids (Aphis fabae), and cereal aphids (Sitobion avenae). All whitefly studies were performed on the model species Bemisia tabaci, despite this group containing many other economically important pest species  (Martin et al. , 2000). In contrast, the Heteroptera data set represents a broader taxonomic scope comprising six different families and 14 genera despite making up just less than a third of all data points in our analyses.
Despite the interest in this question, we were able to extract little data to test this and had to restrict analyses to specific combinations. The results suggested that Medicago/alfalfa biotype aphids experienced higher fecundity costs when tested on Medicago plants than on the universally-accepted V. faba bean plants. Whether this is common across other biotypes for their native plants, and/or due to the aphids with symbionts eliciting reduced plant defences in the universal plant, thereby mediating any fecundity cost, is to be determined  (Sanchez-Arcos et al. , 2016; Wang, Yuan, et al. , 2020). Two aphid biotypes (collected from Medicago and Ononis) hosting H. defensa symbionts were also able to be compared on V. faba, with no difference in fecundity but potentially higher parasitism resistance for Medicago aphids (not statistically significant due to low sample sizes).
Summary of number of data points included in the three meta analyses datasets, separated by the five commonly measured life history traits. reduced development time of three days (Hedges' g =. 60, p <. 001, N = 31), increased fecundity (Hedges' g = 2. 05, p =. 032, N = 6) and increased survival (22% more individual surviving to adulthood with a symbiont; Hedges' g = 3. 33, p <. 001, N = 14) (Figure1b). Whiteflies benefited from reduced development time of a day (Hedges' g = 0. 70, p =. 011, N = 16) and increased fecundity (Hedges' g = 1. 22, p <. 001,.
Thank you to Nina Fatouros for insect images and Dirk Saunders for sharing code to produce Figure 1a. SEZ was supported by a British Ecological Society small research grant (SR16/1069) and BBSRC (UKRI) via a David Phillips Fellowship BB/S010556/1. Karim Tighiouart and Enric Frago were funded by the Regional Council of Reunion, the Departmental Council of the Region Reunion, the European Union (EAFRD) and CIRAD. Enric Frago was also funded by the Agence Nationale de la Recherche (ANR) via the PRIMA S2 2018 project INTOMED. AUTH O R CO NTR I B UTI O N S Sharon E. Zytynska and Enric Frago conceived the idea and designed the study. All authors extracted data, Sharon E. Zytynska and Enric.
TA B L E 1 1 Summary of number of data points included in the three meta analyses datasets, separated by the five commonly measured life history traits. reduced development time of three days (Hedges' g =. 60, p <. 001, N = 31), increased fecundity (Hedges' g = 2. 05, p =. 032, N = 6) and increased survival (22% more individual surviving to adulthood with a symbiont; Hedges' g = 3. 33, p <. 001, N = 14) (Figure1b). Whiteflies benefited from reduced development time of a day (Hedges' g = 0. 70, p =. 011, N = 16) and increased fecundity (Hedges' g = 1. 22, p <. 001,.
Bennett. Thank you to Nina Fatouros for insect images and Dirk Saunders for sharing code to produce Figure 1a. SEZ was supported by a British Ecological Society small research grant (SR16/1069) and BBSRC (UKRI) via a David Phillips Fellowship BB/S010556/1. Karim Tighiouart and Enric Frago were funded by the Regional Council of Reunion, the Departmental Council of the Region Reunion, the European Union (EAFRD) and CIRAD. Enric Frago was also funded by the Agence Nationale de la Recherche (ANR) via the PRIMA S2 2018 project INTOMED. AUTH O R CO NTR I B UTI O N S Sharon E. Zytynska and Enric Frago conceived the idea and designed the study. All authors extracted data, Sharon E. Zytynska and Enric.
Dataset Species number Number of data points (number of articles) Body size Development Survival d / time Lifespan Fecundity parasitism e Heteroptera a 18 68 (13) 31 (10) 4 (1) 6 (5) 14 (8) Whitefly b 1 18 (4) 16 (4) 3 (2) 8 (5) 4 (4) Aphids c 13 32 (16) 34 (16) 34 (15) 124 (47) 57 (29) a Acrosternum heegeri, Adomerus rotundus, Adomerus triguttulus, Blissus insularis, Brachynema germari, Dolycoris baccarum, Elasmostethus humeralis, Eurydema rugosa, Eurygaster integriceps, Graphosoma italicum, Graphosoma lineatum, Halyomorpha halys, Megacopta cribaria, Megacopta punctatissima, Murgantia histrionica, Nezara viridula, Riptortus clavatus, Riptortus pedestris. b Bemisia tabaci. c Acyrthosiphon kondoi, Acyrthosiphon pisum, Aphis craccivora, Aphis fabae, Aphis glycines, Aphis gossypii, Macrosiphum euphorbiae, Megoura crassicauda, Myzus persicae, Obtusicauda frigidae, Rhopalosiphum padi, Sitobion avenae, Sitobion miscanthi.
MS spectra were acquired in the Orbitrap analyzer with a mass range of 400-1500 m/z and 120,000 resolution. HCD fragmentation was performed at 35 normalized collision energy and MS/MS spectra were analyzed at 30,000 resolution in the Orbitrap. Data analysis was performed with Xcalibur 2. 2 (Thermo Scientific). Peptide identification from MS/MS spectra was performed by open search on Comet-PTM  [26]  against a human database, build by concatenating the human reference proteome database (Uni-ProtKB/Swiss-Prot July 2014, 40555 sequences), with the inverted database constructed from the same target database using DecoyPYrat  [27]. Search parameters were: full trypsin digestion with 2 missed cleavages, fixed modifications Cys carbamidomethylation (57. 021464 Da) and TMT labeling at N-terminal end and Lys (229.
162932 Da). Precursor mass tolerance was set to 500 Da and fragment mass tolerance at 0. 03 Da; precursor charge range was set to 2-4; and 3 was the maximum fragment charge. Only y-and b-ions were used for scoring. The FDR (<1%) of PTM identification was controlled at the global, local and peak levels by SHIFTS  [26]. Quantitative information was extracted from MS/MS spectra of TMT-labeled peptides using Proteome Discoverer 2. 1 (Thermo Scientific). Modified peptides and protein quantification was performed using the Generic Integration Algorithm  [28, 29]  on the basis of the WSPP model  [30]  with some modifications  [26]. Significant abundance changes of modified peptides across the different samples were detected by Student's t-test. An in-house developed script was used for the semisupervised annotation of peptide modifications as described  [26].