dataesr/scientific-paragraphs-categorization huggingface dataset:
Literature-based validation of predicted interactions To compare the performance and sensitivity of FW to similar cooccurrence network inference methods such as SPIEC-EASI (99), we estimated the graph accuracy by comparing edges with known (marine) biotic interactions. We limited our comparisons to Polar networks and compared edges with known interactions from the PIDA (83) (https://github. com/ramalok/PIDA) and GLOBI (  101 ) databases (www. globalbioticinteractions. org/). We used the National Center for Biotechnology Information (NCBI) taxonomy for prokaryotes and PR2 taxonomy for eukaryotes to identify superkingdom, family, genus, and species levels. Then, we searched for known interactions from these databases in the networks by detecting all combinations of OTUs at the four taxonomic levels considered   15  (fig.
S12 ). Conserved associations across taxonomy ranks were estimated as follows. First, taxonomic ranks were extracted from NCBI Taxonomy database for prokaryotes and from PR2 database for eukaryotes. Next, for each pair of ranks, we counted the number of edges between nodes of each rank. Next, we repeated the procedure but now applied to the subnetwork induced by considering only nodes from a particular biome. Last, we calculated the proportion of edges for each rank pair in each biome with respect to the total network. S C I E N C E A D V A N C E S | R E S E A R C H A R T I C L E 11 of.
Marine biome assignations to OTUs In the Tara Oceans dataset, each sample is associated with one specific marine biome (Coastal, Trades, Westerlies, or Polar). Using this information, we assigned each OTU to a biome or a combination of biomes according to its abundance profile. We did this by identifying biome(s) in which a given OTU is overrepresented, based on relative abundance, compared to other biomes using a Kruskal-Wallis (KW) test implemented in the Python package SciPy (version 1. 2. 1). Adjustments for multiple testing were performed using the Benjamini-Hochberg (BH) procedure implemented in statsmodels (version 0. 9. 0). For significant tests (FDR < 0. 05), a post hoc Dunn's test implemented in scikit-posthocs (version 0. 6. 1) was performed to determine in which biome(s) a given OTU was significantly overrepresented (FDR < 0.
Network community detection and biome assignation to communities We detected five communities in the GPI using an eigenvector-based network community detection algorithm (102) implemented in the networkx 2. 3 python package. To assign biomes to these communities, OTU abundance tables were CLR-transformed and aggregated by community for each size fraction. CLR values for each community were grouped by biome, and a KW test was run to verify mean differences of communities among biomes (KW test column in table  S4 ). As all P values were significant while controlling the FDR using the BH procedure, post hoc Dunn tests were performed to detect community pairwise differences between biomes (Dunn test P value columns in table  S4 ). Biomes that were found significantly lower via the Dunn test were discarded from the biome assignation (Dunn test z score column in table  S4 ).
General keystone index The generalized keystone index (59) combines several centrality metrics in a single measure, which can then be used to rank nodes, revealing their topological importance in the network. Degree, betweenness, closeness, and subgraph centralities have been calculated using the Python library networkx (version 2. 3), capturing the relevance of each node at different topological scales. Factor analysis was performed with the Python library sklearn (version 0. 20. 3) on those centralities to get the generalized keystone index associated with each node.
Statistical analyses Spearman correlations, followed by BH procedure (FDR < 0. 01), were performed to test associations between network topology metrics and environmental parameters (Fig. 2A ). KW tests followed by post hoc Dunn's tests were performed using R (version 3. 2. 2) to determine significant differences across biome-specific interactome topological metrics (Fig. 2C ). A Pearson's chi-square test was performed to detect taxa associations enriched in each interactome community (fig. S3 ). Here, only pairs of taxa that co-occur in at least three communities and occur at minimum 50 times in total were tested. For these pairs, we performed a post hoc analysis for Pearson's chisquare test on the residuals using the chisq. posthoc. test R package (https://chisq-posthoc-test. ebbert. nrw/) to identify within each community taxa pairs with a number of associations significantly diverging from random expectation.
It therefore seems relevant to study the potential interaction of these two intestinal protozoa in the same population in order to determine whether their presence has a comparable impact on the gut microbiota from colonized individuals. To achieve this, given that Entamoeba colonization is rare (if not absent) in industrialized countries, our study focused on the intestinal microbiota of healthy adults from rural, semi-urban and urban areas in Cameroon. In this study, we used the 16S rRNA gene amplicon sequencing data from 134 Cameroonians generated in a previous survey  (Lokmer et al. , 2020)  to profile the bacterial composition and diversity of their gut microbiota. The detection and identification of Blastocystis STs and Entamoeba species was performed by analyzing shotgun metagenomic data from the same individuals generated for this study.
Finally, we obtained the authorization to store personal data in France from the CNIL (Commission Nationale Informatique et Liberteś, n°1972648). We obtained the informed consent of each participant for contributing to this research. These samples are part of the dataset previously published  (Lokmer et al. , 2020). The average age was 38 years with a range between 18 and 64 years. Body mass index (BMI) was calculated for all the individuals who were categorized as underweight, normal weight, overweight or obese if they had a BMI < 18. 5 (n = 9), from 18. 5 to 24. 9 (n = 89), ≥ 25 (n = 26) or ≥ 30 (n = 9), respectively. BMI information was not available for one subject and age information was missing for another one (Supplementary Table  1 ). Age and sex distribution as well as demographic data according to the colonization by Blastocystis STs and Entamoeba species are given in Supplementary File 1.
Sequencing Data Quality Control and Preprocessing To process raw amplicon reads, QIIME2  (Bolyen et al. , 2019)  microbiome analysis package has been used. Full documentation of all analysis code in this project can be found at https:// rachaellappan. github. io/VL-QIIME2-analysis/. Briefly, raw fastq files were first converted into QIIME2 compatible files. After a quality check, DADA2  (Callahan et al. , 2016)  was used for calling amplicon sequence variant (ASVs). DADA2 uses a statistical error correction model and attempts to remove or correct reads with sequencing errors and then remove chimeric sequences originating from different DNA templates. We used SILVA database v132  (Quast et al. , 2013)  as the reference database in combination with QIIME2's pre-fitted sklearnbased taxonomy classifier tool for ASV taxonomic classification.
De novo phylogenetic trees used to calculate phylogenetic diversity were also built using default QIIME2 provided methods, i. e. , align-to-tree-mafft-fastree (MAFFT multiple sequence alignment program)  (Price et al. , 2010; Katoh and Standley, 2013). A total of 2768 ASVs were obtained after pre-processing. WGS data were preprocessed as described in  (Lokmer et al. , 2019). Briefly, we filtered the reads from low-quality areas of the flowcell, trimmed the adapters and low quality bases (Phred Q<10) using BBMap package  (Bushnell, 2017) , leading to 42 ± 5. 5 million reads per sample.
Statistical Analyses Alpha (within-sample) diversity was measured using observed ASVs, Pielou's index, Shannon's H and Faith's Phylogenetic Diversity (PD) calculated in QIIME2 from rarefied counts. Although the rarefaction curve (Supplementary Figure  1 ) of Shannon's H reached a plateau after 2,000 reads, we rarified the data to 10,000 in order to provide robustness in diversity analyses. This resulted in loss of 20 samples with read depths <10,000 reads, leaving a sample size of 114 for downstream analyses. In order to visualize the relationship between gut microbiome diversity, colonization by protozoa and contextual variables, we performed a FAMD (Factor Analysis of Mixed Data) including quantitative (Age, BMI, Faith's PD index) and qualitative variables (Blastocystis and Entamoeba colonization status, sex and lifestyle).
FAMD was calculated using R (R Development Core Team and Team, 2005) and the package FactoMineR  (Leê t al. , 2008). Boxplots showing alpha diversity were created in QIIME2. To test for the conditional effect of each protozoan as well as the interactions of both protozoa on alpha diversity, we fitted generalized least squares (gls) linear models (~Blastocystis +Entamoeba + Blastocystis : Entamoeba), after testing for heteroscedasticity and specifying the variance structure in case it was significantly different, using the nlme package in R  (Pinheiro et al. , 2019). To assess beta (between-sample) diversity, Bray-Curtis dissimilarities, weighted and unweighted UniFrac distances were calculated on rarefied counts. Principal coordinates analysis (PCoA) 2D plots for these indices were generated in QIIME2 using the EMPeror graphics tools  (Vázquez-Baeza et al.
, 2013). The effect of protozoan colonization on the beta diversity has been examined by PERMANOVA implemented in the R package vegan  (Oksanen et al. , 2013) , number of permutations = 999). We used ANCOM in QIIME2 to determine differences in ASV abundances between four groups of individuals (i. e. , B-/E-, B+/E-, B-/E+, and B+/E+) or between B+/B-and E+/E-. Relative differential abundance test was conducted in STAMP v 2. 0. 9  (Parks et al. , 2014)  at taxonomic levels from species to phylum, using two-sided Welch's t-test for pairwise comparisons. The taxa have been considered as differentially abundant if the Benjamini-Hochberg-corrected p-value (i. e. , q-value) ≤ 0. 05. Cooccurence analysis was performed using the R package coocurr  (Griffith et al. , 2016).
Relationship Between Protozoan Colonization and Alpha Diversity of the Gut Microbiome To evaluate different aspects of alpha diversity, four diversity indices have been calculated from the 16S data (mean 50,802 raw reads per sample): observed ASVs, Faith's Phylogenetic Diversity (Faith's PD), Shannon's H, and Pielou's evenness. A FAMD (Factor Analysis of Mixed Data) was first used to identify which contextual variables were positively or negatively associated with the bacterial microbiome diversity. Overall, age, BMI, sex and urbanization level were not correlated with Faith's PD (Supplementary Figure  2 ) or any other diversity indices (results not shown) in our study. We then assessed the effect of Blastocystis and Entamoeba jointly (using gls linear models taking into account both variables and their interaction) on the alpha diversity of the gut microbiota (Figure  4 , Figure  5 , Supplementary Figure  3 ).
Data analysis Raw RNA-Seq reads were assessed for quality, per-base sequence content, adapter content and overrepresented sequences using FastQC (Version 0. 11. 5) [40]. The raw sequence data has been submitted to the SRA database and can be accessed using BioProject id: PRJNA724741. Contamination due to other genomes such as human and bird was estimated using FastQ Screen (Version 0. 11. 1) [41]. FastQ Screen maps the raw-reads to the indexed reference genome using Bowtie 2  [42]  or BWA  [43]  to calculate an estimate of the reference genome contamination in query sequences. Birds infected with the parasites in our study was Serinus canaria (GCF_000534875. 1_SCA1) hence we used this genome for screening transcripts along with Zebra Finch (GCF_000151805. 1_Taeniopygia_guttata-3. 2. 4) genome which is widely used as bird model genome, along with mosquitoes (GCF_000209185.
The choice of parameters was made after examining the reads for quality and presence of adapter sequences. Once trimmed, the sequences were again evaluated for quality (FastQC) before proceeding to the following steps. MultiQC  [45] , which summarizes results from various tools and generate a single report, was used for illustrative purpose. The filtered and trimmed sequence files were aligned to P. relictum published genome (GCA_900005765. 1_PRELSG) using HISAT2 (Version 2. 1. 0) [46]. All the sequence files were aligned as paired end reads and with default parameter settings. HTseq-count  [47]  (parameters: -s no -t gene -i locus_tag) was used to calculate the read count for each sample and custom bash scripts were written to post-process the files to generate gene count matrix. Gene count table is submitted in the supplementary files.
Scripts and more detailed parameter explanation for this process can be provided upon request. The differential gene expression analysis was performed in DESeq2 (Version 1. 16. 1) [48]  package in R. Genes with no expression across all samples i. e. genes with zero read across all samples were filtered out from the gene count table before analysing the data further. To avoid having any bias in the analysis and to examine the data further, variance stabilized transformation of counts  [49]  was used to perform PCA. Plots and visualization were made using RColorBrewer, genefilter, diplyr, and ggplot2 packages in R. Differential gene expression (DGE) was performed with the filtered gene count matrix. The bird samples were considered as a control to perform DGE analysis. The four cases were defined for the DGE analysis and genes were considered differentially expressed for adjusted p-valuse <0.
Data preprocessing and sequence alignment We assessed the sequence data for quality and contamination. V. Sekar et al. FastQC did not report any sequences having an overall Phred Score lower than 24, however, six samples had a drop in the quality for last 8-18 bases (Fig. S2 , Table  S2 ), which were trimmed using Trimmomatic. FastQ Screen mapped the reads with an average of 13% to the Zebra finch, 20% to Canary and 91% to the malaria vector (mosquito) reference sequences. Majority of the reads from the blood samples obtained from infected bird immediately before the mosquito feed mapped to the bird reference sequences (Fig. S3A ; Table  S3 ). After removal of reads that uniquely mapped to the birds and vector genomes, an average of 2% of the remaining reads mapped to the parasite genome (around 0.
Transcript assembly and differential gene expression analysis Transcript assembly and abundance calculation reported 5286 genes (that we found a higher number of genes than present in the annotated genome is due to that alternative splice variants is interpreted as different genes) in the gene count table, which include mitochondrial and apicoplast genes (Table  S8 ). Of these, 160 of the genes found in the annotated genome had no expression data in any of the samples and were dropped from further analysis as they did not contribute to any information to the analysis. The remaining 5126 genes were analyzed for differential gene expression using the DESeq2 package in the R statistical suite. A principal component analysis of expression clustered parasite samples belonging to the same time point together (Fig.
Gene ontology enrichment Annotations for 2583 P. relictum genes were retrieved from Plas-moDB database. Gene ontology (GO) enrichment analysis revealed distinct GO enriched at different time points (Fig. 5 ; Fig. S5 ; Fig. S6 ). GO terms associated with pseudouridine synthesis were enriched for the 30mpi time point, several GO terms related to oxidoreductase activity were enriched for 8dpi, GO terms related to rhoptry neck, entry into host cell (vector in our study) and membrane part were enriched for 12dpi, and GO terms related to locomotion, regulation of RNA metabolic process and lipid metabolic processes were enriched for 22dpi. GO terms related to DNA-binding transcription factor was enriched from 8dpi to 22dpi. GO terms related to exit from host, movement in host environment (vector in our study) and protein kinase activity were enriched during the late stages of infection (12dpi and 22dpi).
PharmGKB Clinical Annotations PharmGKB, is a free access database created, curated, and managed by the University of Stanford and funded by US National Institutes of Health (NIH) and National Institute of General Medicine Science (NIGMS)  [8]. It compiles most of the existing PGx information, from many different databases, including PubMed, under a Creative Commons license. It counts with a group of experts working on the dissemination of knowledge about the impact of human genetic variation on drug responses and on the translation of PGx into clinical practice. In fact, the website (www. pharmgkb. org, accessed on 25 June 2021) includes not only the information of its own curation from published data, the 'Clinical Annotations', but also that of the Drug Labels and the reports by Experts Consortia.
Study population The study population included participants from the MHAS  [18] , a national representative panel study of Mexican residents aged �50 years with four follow-up waves  (2003, 2012, 2015, 2018)  of the baseline conducted in 2001. The aim and methodological design of the MHAS is published elsewhere  [19]. We analyzed data from the third and fourth MHAS waves collected between October 2012 and December 2015. For ethical approval, the MHAS protocols and instruments were reviewed by the Institutional Review Board of the University of Texas Medical Branch, the National Institute of Statistics and Geography (INEGI for its acronym in Spanish) and the National Institute of Public Health (INSP) in Mexico. MHAS data files and documentation are of public use and available at www. mhasweb.
Experimental design and gamma irradiation Effects of ionizing radiation were evaluated on four generations of nematodes continuously exposed (F0, F1, F2, F3) from synchronized embryos to L4 young adult stage (65 h). Several irradiations were carried out, from 3 to 10 replicates with 1000-5000 worms. Chronic irradiation to 50 mGy h -1 of gamma radiation was performed in incubators under controlled conditions using the platform MIRE (Mini irradiator for Radioecology) and MICADO irradiation facility (IRSN, Cadarache, France). Data loggers were used to measure humidity and temperature during irradiation. Dose rates were calculated based on the Monte-Carlo N-Particle model and validated during irradiation with radio-photo luminescent dosimeters (RPL, GD-301 type, Chiyoda Technol Corporation Japan) placed on each incubator to measure the delivered cumulative dose received from the Cesium-137 source.
Libraries were sequenced using Illumina HiSeq 3000 as paired-end 150 base reads with high quality score (Q30 higher than 98% for all samples) at the Norwegian High Throughput Sequencing Centre (Oslo, Norway). Reads were mapped to the C. elegans genome (Ensembl 91/WormBase235) using RNA-STAR. Hierarchical clustering of the variance stabilized data with Pearson's correlation and complete-linkage method was generated and showed a good correlation between biological replicates, except for one exposed and one control sample in F3 generation (Fig. S1 ), which were removed for the subsequent analysis. Deseq2 v1. 10. 2 and rlog R packages were used for normalization and differential expression analysis. Genes with a log2 fold change (Exp/Ctrl) > 0. 5 and adjusted p-value < 0. 05 (False Discovery Rate, FDR) were considered as significantly regulated.
Enrichment of transcriptomic data In order to analyze biological processes impacted by gamma irradiation, Differentially Expressed Genes (DEGs) obtained from DESeq2 analysis were subjected to enrichment analysis. Gene Ontology (GO) enrichment analysis of biological functions was performed using the R package topGO and ViSEAGO. Enriched functions with p-value from Fisher's exact test < 0. 1 were considered significant. Heatmap was generated using the R package ggplot2. Phenotype enrichment analysis was obtained from the DEGs from DESeq2. DEGs were analyzed using WormBase Phenotype Ontology database and modPhEA. Significantly enriched phenotypes were validated by Bonferroni-corrected p-value < 0. 05 from Fisher's exact test. Network analysis was performed with a combination of Gene Ontology analysis and GeneMANIA 3.
Transcription factor binding motif analysis Promoter sequences of DEGs from four generations were analyzed in order to identify potential enrichment of transcription factor binding motifs after chronic exposure to gamma radiation. A set of 4500 randomly chosen promoters' sequences was selected as background. Promoter sequences are defined as DNA genomic sequences from 1500 bp upstream to 200 bp downstream of transcriptional start site and were retrieved via Regulatory Sequence Analysis Tools (RSAT) from Ensembl 91 release. Promoter sequences were analyzed with OPOSSUM v3 Single Site Analysis (SSA) and 43 non-redundant transcription factor binding profiles stored as position frequency matrices from JASPAR database. Binding sites with Z-score > 5 and Fisher-score > 2 were considered significantly enriched.
qRT-PCR RNA from four generations of irradiated nematodes was extracted with Qiagen mRNA kit and integrity was assessed with Bioanalyzer® (in conjunction with RNA 6000 Nano LabChip®). cDNAs were synthesized using the superscript affinity cDNA from Qiagen. Quantitative PCR (qPCR) analysis was performed in order to quantify the expression of egl-1. For this purpose, Mx4000 multiplex quantitative PCR system (Stratagene) with 1X MasterMix Agilent and 2 µM of primer set were used. The PCR reactions were amplified for 15 s at 95 • C and 1 min at 60 • C for 45 cycles. Transcript expression was normalized to PMP-3, CDC-42 and ACT-2 housekeeping genes. Primer sequences used are in Supplementary Data (Fig. S3 ). qRT-PCR were performed three times on three independent biological replicates. Statistical analysis was performed using dataassist software.
9 and Fisher score = 3) (data not shown). These data strongly suggest that DAF-16 is a key regulator of the stress response imposed by chronic gamma radiation. Based on experimentally validated interactions, we built a network between the transcription factors and the shared DEGs. This showed the connection between DAF-16, PQM-1, ZTF-2, CES-2 and egl-1. A part of this network governs stress, apoptosis response and it was shared by four continuously exposed generations (Fig. S4 ). To verify the responsiveness and activation by gamma radiation of DAF-16, the cellular localization was investigated in irradiated L4young adult hermaphrodites using a daf-16:gfp reporter strain. Basal expression of DAF-16 is predominantly localized in the cytoplasm, while activation requires translocation to the nucleus.
Thus, we further investigated the implication of DAF-16 by comparing the presence of class I and II genes and proteins under DAF-16/PQM-1 control with previously published omics datasets from chronic irradiation of C. elegans. In the current study, between 12. 5% and 40. 3% of class I and II genes were identified over the 4 exposed generations, where 52. 8% were shared by 3 or 4 generations. In recent transcriptomic analysis carried out after 48 and 72 h of irradiation at 10 and 100 mGy h -1  (Maremonti et al. , 2019) , similar rates from 17. 6% to 50. 9% were observed (Table  2 ). Moreover, a proteomic analysis on nematodes exposed to similar doses of chronic gamma radiation showed that 22% of proteins from class I and II genes were overexpressed  (Dubois et al. , 2019). Thus, these findings indicate that DAF-16 and PQM-1 constitute major regulators in response to single or multigenerational exposure to chronic irradiation.