dataesr/scientific-paragraphs-categorization huggingface dataset:
Read Alignment and Gene Quantification As an apricot reference genome is not available, and peach and apricot are closely related, the peach reference genome was used as a reference for both species. Initial read alignments revealed the peach RNAseq libraries contained significant portions of reads derived from rRNA. rRNA filtering was conducted before realigning to reference genome. Raw reads were mapped to small and large subunit rRNAs from SILVA ribosomal RNA database  (Quast et al. , 2013)  and the mapped reads were removed. STAR  (Dobin et al. , 2013)  was used to align the clean apricot and peach RNA-seq reads to both Prunus persica genomes: Prunus persica v1. 0 (The International Peach Genome  Initiative et al. , 2013)  and Prunus persica v2. 0  (Verde et al. , 2017). The aligned reads were analyzed by  HTSeq-count (Anders, Pyl et al.
Differential Co-Expression Analysis and Network Enrichment Analysis Weighted Gene Correlation Network Analysis (WGCNA)  (Zhang and Horvath, 2005; Langfelder and Horvath, 2008)  was used to identify modules of highly correlated genes based on the DESeq2 normalized gene expression data following the steps in the documentation for the WGCNA Package  (Langfelder and Horvath, 2014). Genes with 0 normalized count were removed. The expression profiles of the remaining genes were hierarchically clustered into modules containing at least 100 genes. The expression profiles of each module were summarized using the first PC as the module eigengene (ME). Module-traits correlations were calculated by Pearson correlation coefficient (PCC) where each module was represented by its ME, and each developmental stage was represented with a numeric vector with "1" for the trait, and "0" for all the others.
Genes from selected modules were annotated with the Arabidopsis gene as described above. The GO enrichment networks were analyzed by BiNGO in Cytoscape  (Maere et al. , 2005). Reverse Transcription Quantitative PCR (RT-qPCR) Total RNA was extracted as previously described  (Conrad et al. , 2019). One RNA sample from each of three apricot genotypes (A2137, A1956, A2312) at time points 100, 400, 800, and sepal was subjected to real-time qPCR analysis using the Luna ® Universal one step RT-qPCR kit (E3005S, New England Biolabs Inc, MA) with the primers listed in Table  2. Expression levels of mRNAs of interest were normalized for the 18S RNA levels. Three technical replicates were used for each sample.
Co-Expression Analysis Identified Endodormancy and Ecodormancy Related Genes Co-expression clustering was performed to provide better insight into coordinated gene expression modules that contribute to the endodormancy to ecodormancy transition. 25,960 genes detected by RNA-seq were clustered into 23 co-expression modules based on the TOMsimilarity algorithm in the WGCNA R package  (Langfelder and Horvath, 2008). Module size ranged from 126 genes (ME22) to 5,967 genes (ME1) (Figure  3B ). Module-factor relationship analysis correlated the expression level of the modules to the five developmental stages. Higher correlation p-values of a module at a particular stage indicate the genes in that module have higher expression at that stage. According to the module-factor relationship, we identified two modules, module 2 (ME2) and module 15 (ME15) most correlated to dormancy stages (Figure  3A ).
To further investigate the common expression patterns in both apricot and peach, a co-expression analysis integrating the peach and apricot datasets was performed. Twenty-two thousand three hundred seventeen genes expressed in more than half of the samples in the dataset were clustered into 16 modules spanning from 215 genes (ME15) to 6,089 genes (ME1). DEGs were distributed unevenly across the 16 modules, with the largest number as 174 DEGs in ME11, and no DEGs in ME7 or ME15 (Figure  7B ). To understand the major biological processes that genes are involved in for each module, genes were then enriched by AgriGO v2. 0 using the Prunus persica background. Three hundred thirty-nine significant GO terms from the biological process category were enriched across the 16 modules. ME1 had the most with 84 enriched GO functions, while ME0, ME6, ME13, and ME14 had no significant GO terms (Figure  7D ).
FUNDING This project is supported by the  Agriculture and Food Research Initiative Competitive  Grant No. 2016-67014-24577  from the  USDA National Institute of Food and Agriculture  ( Albert G Abbott ,  Christopher Dardick ,  Zongrang Liu ,  Margaret E Staton ) and the  French ANR CHEX   ABRIWG  No. ANR-11-CHEX-0002  ( Albert G Abbott ,  Véronique Decroocq , and  Jean-Marc Audergon ).
DATA AVAILABILITY STATEMENT All reported raw read data is available at the National Center for Biotechnology Information (NCBI) under the study of project accession PRJNA567655. Raw reads for apricot samples are available from the Sequence Read Archive (SRA) with accession IDs SAMN12791244 to SAMN12791303. Peach samples are available under the accession IDs SAMN12791304 to SAMN12791372. The raw counts of reads per gene derived after read mapping were deposited to NCBI Gene Expression Omnibus (GEO) with accession GSE138792.
cRNAs were hybridized to the Agilent 4 × 44K Whole Human Genome Microarray (Agilent Technologies, France) at 65 °C for 17 h in a rotating incubator. After hybridization, slides were washed and then scanned using an Agilent G2565CA scanner (Agilent Technologies, France). Raw data were extracted using Feature Extraction software version 10. 5. 1. 1 (Agilent Technologies, France). Microarray analysis R 3. 0. 2 software and the LIMMA package  [15]  were used to analyze microarray data (within-array normalization by loess method followed by between-array normalization by quantile method) and to generate lists of differentially expressed genes. Microarray data were deposited in the NCBI-GEO database (accession number GSE72048). To create gene lists, we ltered by expression levels (|(FC)|>1. 5 as a cut-off) as well as by adjusted p-values, using a 0.
05 threshold with the Benjamini and Hochberg false discovery rate [16] as multiple testing correction. The complete data set is available through the Gene Expression Omnibus GSE72048. Selected gene lists (log ratio and p-value data) were loaded into Ingenuity Pathway Analysis (IPA) and Multiarray Experiment Viewer  [17]  to analyze pathways and generate data displays. Quantitative real time RT-PCR (qRT-PCR) Three µg of DNase I-treated total RNAs were reverse-transcribed using Oligo(dT) primers and 1 µl of SuperScript II reverse transcriptase (Invitrogen, France). The resulting cDNAs were quanti ed by Nanodrop (Thermo Fisher Scienti c Inc. , France) and diluted to a working concentration of 100 ng/µl. Reactions were performed in a nal volume of 25 µl with 500 ng cDNA, 10 pM primers, and SYBR Green PCR Master Mix (Applied Biosystems, USA), using a Mastercycler Realplex (Eppendorf, France).
Finally, we introduced F. prausnitzii SN. There were extensive changes in gene regulation: 913 genes were upregulated and 585 were downregulated after TNF-α stimulation and SN treatment (Fig. 1A ). Interestingly, these genes are involved in several in ammatory pathways such as NF-κβ, p38 and other ERK/MAPK pathways. Among these, 25% of the genes that had been activated by TNF-α exposure (in our rst comparison) were inactivated by treatment with F. prausnitzii SN (70 out of 287). To explore this complex gene regulation network, we submitted this dataset to IPA, which highlighted the SN-affected pathways. This analysis con rmed the ability of F. prausnitzii SN to regulate ERK/MAPK signaling pathway as the most important pathway related to in ammation modulated speci cally by F. prausnitzii SN (Fig.
1C ). Notably, 7% of all genes regulated by F. prausnitzii SN were linked to MAPK pathways, including JNK. The 10 genes with the largest foldchange in regulation are shown in Fig. 1B  and Table  S3 , IL-8 was found to be downregulated, while the most upregulated gene was Dact3 (FC = 17. 2). Dact3 belongs to the Dact gene family, whose members interact with the Dsh protein to inhibit Dsh-induced activation of the JNK pathway  [26, 27]. The Dact3/JNK pathway is presented in Fig. 2A. Finally, in order to validate our transcriptomic data, the differential expression of up-and downregulated genes, including Dact3, was validated by RT-qPCR (only Dact3 data shown). B2M was used as reference for data normalization as described in  [28]. Compared to LYBHI, F. prausnitzii SN led to signi cant Dact3 upregulation in TNF-α-stimulated HT-29 cells (Fig.
prausnitzii in an in ammatory context too. Finally, to assess the effect of the gut microbiota on Dact3 modulation in in vivo physiological conditions, we investigated a dataset that we previously published comparing the colonic transcriptome of germ-free and conventionalized (Conv) mice  [21]. We found that Dact3 expression was signi cantly reduced in germ-free compared to Conv mice (Fig. 7 ). These observations con rm that gut microbiota has a major impact on Dact3 expression.
Funding This study was a part of FPARIS collaborative project selected and supported by the  Vitagora Competitive Cluster  and funded by the  French Fond Unique Interministériel ,  n°F1010012D , the  Fonds Européen de Développement Régional  (Bourgogne:  34606 ), the  Burgundy Region , the  Conseil Général 21 , and the  Grand Dijon. This work was also supported by  Merck Médication Familiale (Dijon, France)  and  Biovitis  ( Saint Etienne de Chomeil, France ). RM received a salary from these grants and ML received a PhD grant from  ABIES.
Several related groups of genes or gene products involved in amino acid FIG 8 Legend (Continued) carrying a chromosomally -attB site-inserted clbR promoter-luciferase fusion that included either a 5-repeat or 20-repeat VNTR region was transformed with pBAD24 derivatives, enabling overexpression of clbR or clbQ. Luminescence as a measure of clbR promoter activity was quantified in response to increased expression of clbR and clbQ. Data are based on results from three biological replicates performed with three technical replicates. Means with standard deviations are shown. Except for E. coli M1/5 rpsLK42R with and without the vector control, the clbR promoter activities measured differed significantly in response to clbR and clbQ overexpression (P Ͼ 0. 0001, unpaired t test). (B) HeLa cells were either infected with E.
coli strain M1/5 rpsLK42R or derivatives (MOI of 200) or not infected. At 4 h postinfection, bacteria were removed and the cells were cultivated for another 4 h and subsequently washed with PBS and lysed. A total of 6 g protein per lane of the indicated samples was analyzed by SDS-PAGE and afterwards transferred onto a PVDF membrane. ␥-H2AX was detected using anti-gammaH2A. X (phospho S139) antibody (Abcam). ␤-Actin served as a loading control. Corresponding bands are marked with an arrow. For colibactin-producing strains, the ubiquitinylated band (ϳ25 kDa) could also be detected. (C) The impact of ClbR and ClbQ on colibactin production of M1/5 rpsLK42R was also analyzed by UPLC-HRMS-based comparison of N-myristoyl-D-asparagine levels. The data presented in the graph were obtained from three biological replicates.
This strain also lacks other important E. coli virulence factors, such as type 3 secretion systems as well as typical virulence-related fimbrial adhesins of intestinal and extraintestinal E. coli pathotypes. Comparison of the genome content of E. coli M1/5 with that of other completely sequenced human commensal model strains frequently used for comparative genomics or functional analyses, i. e. , E. coli isolates HS (phylogroup A, O9:H4; BioProject accession no. PRJNA13959) (41), SE11 (phylogroup B1, O152:H28; PRJNA18057)  (42) , IAI1 (phylogroup B1, O8:H19; PRJNA33373)  (43) , SE15 (phylogroup B2, O150:H5; PRJDA19053)  (44) , and ED1a (phylogroup B2, O81:H27; PRJNA33409)  (43) , indicated that these strains differ with respect to the presence of determinants for chaperone-usher fimbriae and other adhesins and of factors involved in serum resistance and iron uptake as well as in gene clusters coding for type 3, type 5, and type 6 secretion systems.
The sample was subjected to vortex mixing every minute for 10 s. Total RNA was extracted by the use of TRIzol (Invitrogen, Karlsruhe, Germany) according to the manufacturer's protocol. After DNase treatment and PCR-based quality control, the quality of the RNA samples was further assessed by RNA electrophoresis using a 2200 TapeStation system (Agilent). Strand-specific cDNA libraries processed with and without terminator exonuclease (TEX) treatment to enrich primary transcripts were prepared and sequenced (Illumina NextSeq 500, 1ϫ 75-bp single reads) by Vertis Biotechnologie AG (Freising, Germany). Obtained sequencing data were processed using BWA  (64)  for mapping of transcripts and ReadXplorer 2 (74) for visualization and utilization of differential gene expression results by the use of DESeq2  (75).
Details are provided in Text S1. Statistical analysis. Statistical analyses were performed using GraphPad Prism software (version 6. 0). The figures show mean values with standard deviations (STDEV. P). Unpaired t tests were used as indicated. A P value of Ͻ0. 05 was considered statistically significant and is indicated by one asterisk (*). A P value of Ͻ0. 01 is denoted by two asterisks (**), a P value of Ͻ0. 001 by three asterisks (***), and a P value of Ͻ0. 0001 by four asterisks (****). a Amp r , ampicillin resistance; Cm r , chloramphenicol resistance; IPTG, isopropyl-␤-D-thiogalactopyranoside; Kan r , kanamycin resistance; Sm r , streptomycin resistance; HPI, high-pathogenicity island.
ACKNOWLEDGMENTS The work of the Münster team was supported by the  Interdisciplinary Center for Clinical Research of the Medical Faculty Münster  ( Dob2/013/12 ) and the  German Research Foundation  ( DO789/11-1 ). The work of the Würzburg team was supported by the  German Research Foundation  ( SFB 479 ,  TP A1 ). We thank  K. Tegelkamp ,  O. Mantel ,  M. Bayer , and  A. M. Berg  (Münster) as well as  S. Severitt  and  N. Heyer  (both  DSMZ, Braunschweig ) for excellent technical support.
An innovative, immersive exhibit at the Exploratorium Science Museum in San Francisco, USA, used the lightsensitive behaviour of a Euglena species to give the impression that the eukaryotic microbe was dancing with visitors while in reality avoiding their shadow  (Lam et al. , 2019). More unusually, microbial metagenomics data sets have been set to music  (Larsen, 2016) , and bubbles from fermentative microbes have been used to play relaxing tunes in an interactive exhibit called a Fermentophone (Fig. 16 ). Importantly, there are also museums associated with inspirational microbiologists, e. g. Beijerinck Museum in Delft (the Netherlands), Museum at the Robert Koch Institute in Berlin (Germany), Alexander Fleming's Museum in London (UK) and Louis Pasteur's house in Arbois (France). Some discussion topics.
Acknowledgements TJM acknowledges support from the following UKRI grants:  STFC  'Trace gas bio-signatures: implications for NOMAD'  ST/S001425/1 ) and  NERC  ' FUSED -Functionality of Urban Soils supporting Ecosystem service Delivery ' ( NE/S005560/1 ). We are very grateful to the following for providing suggestions and images:  Margaret Adams ,  Geof Adams ,  Selwa Alsam ,  Judith Armitage ,  Emese Bartha ,  Ricardo Cavicchioli ,  Scott Chimileski ,  James Chong ,  Joseph Christie-Oleza ,  Graeme Churchard ,  Martha Clokie ,  Mike Dyall-Smith ,  Martha Endika ,  Alessandra Marcon Gasperini ,  Immanuel Giel ,  Charlotte Gruender ,  Myriam de Haan ,  Natalie Hicks ,  Tanja A. Hofmann ,  Mike Jetten ,  Bo Barker Jørgensen ,  Stefan Hyman ,  Patrick Jung ,  Z. Ceren Karahan ,  Roberto Kolter ,  Philippe Laissue ,  Sang Yup Lee ,  Miika Lepp€ anen ,  Andrea Lohberger ,  Derek R.
GeneChip hybridization and microarray data analysis Gene expression profiling of the samples was performed by microarray analysis as detailed previously  [18]. Briefly, fragmented cRNA was hybridized on Affymetrix Human Genome U133 Plus PM Array Strips (Thermo Fisher) containing 54,675 probe sets coding for 47,000 transcripts and variants, including 38,500 unique human genes on a single array. Chips were stained with streptavidin-phycoerythrin (Invitrogen Life Technologies, Milano) and scanned using an Affymetrix GeneChip Scanner 3000. Expression values were quantified, and data were processed by RMA normalization utilizing the ' Affy' R package. Statistical analysis using Student's t-test was performed to identify differentially expressed probe sets. Specifically, unpaired Student's t-test was used to assess whether there were significant differentially expressed probe sets between ASD and ToF, whereas paired Student's t-test was used to identify differences in the probe set expression levels between Pre-CPB and Post-CPB in ASD or in ToF.
Filtering was carried out by 'GeneFilter' R package, as described  [18, 19]. We converted the Affymetrix probe sets into the corresponding gene symbol by Netaffix tool. When multiple probe sets were associated with the same gene symbol, the probe set with the highest expression signal was considered  [19]. The full set of data from each microarray experiment has been deposited at the Gene Expression Omnibus (GEO) public repository at NCBI (http:// www. ncbi. nlm. nih. gov) and can be accessed to through GEO Series accession number GSE132176. Differentially expressed genes (DEGs) were visualized by heat-map representation obtained by Morpheus heat map building tool (http://www. broad insti tute. org/cance r/softw are/ morph eus/) available from the Broad Institute.
Gene Ontology annotation and gene network analysis Gene Ontology (GO) enrichment analysis of DEGs was carried out using the Cytoscape BINGO plugin  [20]. DEGs were classified according to the biological process and cellular component GO collections. Terms with p value and FDR lower than 0. 05 were considered significantly enriched. Correction for multiple hypothesis testing was carried out by the Benjamini-Hochberg method, as described by Maere et al. [20]. The Search Tool for the Retrieval of Interactive Gene Database (STRING-DB) Version 9. 1. (http://strin g-db. org/) was used to construct functional interaction networks among DEGs-encoded proteins  [21]. To this end, we performed a STRING-DB multiple proteins search using the DEGs as input list and extracted all the potential connection among encoded proteins.
Gene set enrichment analysis Gene Set Enrichment Analysis (GSEA) was carried out on all probe sets of the Affymetrix HG-U133 Plus 2. 0 PM GeneChip microarray to evaluate the enrichment of hypoxia-related genes in ToF and ASD expression profiles, as described  [18]. To this end, we built a custom gene set collection, named "HeartHypoxia", by selecting 109 gene sets among the curated collections of the Broad Institute Molecular Signature v5 Database (MSigDB)  [22]. Available gene sets were listed using "hypoxia" and "heart" as keywords. We considered gene sets containing between 15 and 500 probe sets and collapsed the expression set to gene symbol before running the analysis. An enrichment with FDR q-values ≤ 0. 2 and nominal p values ≤ 0. 05 was considered significant.
Real-time RT-PCR cDNA was prepared from purified total RNA using SuperScript Double-Stranded cDNA synthesis kit (Invitrogen). Quantitative real time PCR (qRT-PCR) was performed on a 7500 Real Time PCR System (Applied) using SYBR Green PCR Master Mix and sense/antisense oligonucleotide primers synthesized by TIBMolbiol (Genova), as previously detailed  [18, 23]. Expression data were normalized on the values obtained in parallel for three reference genes (actin related protein 2/3 complex subunit 1B, ARCP1B; lysosomal-associated multispanning membrane protein-5, LAPTM5; and ribosomal protein S3, RSP3), using the Bestkeeper software, and relative expression values were calculated using Q-gene software, as described  [24].
Comparative transcriptome analysis of atrium specimens from ToF and ASD patients To identify genes involved in ToF and ASD pathogenesis, we compared by microarray analysis the transcriptome of right atrial specimens collected from 10 ToF and 10 ASD patients at the time of corrective surgery. cRNA derived from total RNA isolated from each sample was individually hybridized to human Affymetrix HG-U133 Plus PM Array Strips (GEO database accession number GSE132176), and raw data were processed as described in "Methods" section. Probe set expression differences of ≥ twofold and p-value ≤ 0. 05 between ToF and ASD samples were considered statistically significant. Using these selection criteria, we identified a total of 89 differentially expressed probe sets (see Additional file 1: Table  S1 ) corresponding to 72 unique DEGs in ToF vs ASD samples.
The extent of modulation was higher according to qRT-PCR respect to Affymetrix data, in agreement with previous findings showing that microarray can often underestimate the degree of gene regulation  [23]. Previous evidence demonstrated chronic exposure to hypoxia in cyanotic ToF patients  [26]. GSEA was applied to determine the contribution of hypoxia to ToF myocardial transcriptome. One hundred nine gene sets belonging to all curated collections of the MSigDB v5 database were selected utilizing "hypoxia" and "heart" as keywords. Fourteen hypoxia-related gene sets were significantly enriched (FDR q-value ≤ 0. 2 and nominal p-value ≤ 0. 05) in the transcriptional profile of atrial tissues from ToF compared to ASD patients (Table  2 ), whereas none was found significantly enriched in the ASD compared to ToF transcriptome (data not shown).
Gene up-regulation was the predominant change that differentiated Post-CPB from Pre-CPB samples, although a large subset of genes was also found downregulated in ASD patients (Tables  3, 4 , and 5; Additional file 3: Table  S2  and Additional file 4: Table  S3 ). These data indicate that CPB induces distinct cardiac gene expression changes in these two forms of CHDs. To gain insights into the functional processes modulated by CPB, DEGs were then analyzed by GO based on the biological process collection. We identified 61 significantly enriched processes in Post-CPB compared to Pre-CBP samples, of which 46 in ToF and 15 in ASD. Figure  5a  shows a selection of the functional terms with the most significant enrichment score, the majority of which was represented in both ToF and ASD samples. Regulation of biologic and metabolic processes and response to stimulus were the top terms in post-CPB specimens from both groups of patients.
The graph shows the most relevant GO terms. The GO term name is reported on the y-axis; the number of enriched DEGs for each term is indicated on the x-axis. GO terms are listed by decreasing number of DEGs. The p value for each GO term is indicated. b Network analysis. The STRING-DB software was used to construct functional interaction networks among DEGs products. Networks are displayed graphically as nodes (DEGs products) and edges (predicted protein-protein associations). Colored nodes indicate query DEGs and first shell of interactors. Filled nodes indicate some 3D structure known or predicted. The color of the edge indicates the type of interaction inferred by experimental evidence (magenta lines), text-mining (light green lines), curated databases (cyan lines), encoded-protein homology (purple lines), and co-expression (violet lines).