dataesr/scientific-paragraphs-categorization huggingface dataset:
This first analysis revealed that the deletion of the main esterase genes triggered an unexpected modification of genome expression in the strain Fx10-ΔAE. In order to unravel this surprising expression repatterning, we sought functional connections between DEGs by searching in the protein-protein interactions (PPI) database (string-db. org). A cluster analysis of the PPI score matrix allowed the identification of 13 PPI clusters sharing functional relationships (Figure  7 ). Each cluster A gene ontology (GO) analysis identified 23 GO categories that were significantly enriched or depleted in DEGs (hypergeometric distribution with Holm-Bonferroni correction, α = 0. 05). Interestingly, most of the GO categories concerned major biological processes involved in cellular housekeeping functions, including mRNA and rRNA processing (GO:0006397, GO:0006364), cell division (GO:0051301), transmembrane transport (GO:0055085), and oxidoreduction process (GO:0055114).
The strong enrichment in genes related to RNA metabolism was also confirmed by a significative enrichment in RNA binding function (GO: 0003723), as well by the fact that half (517) of the 1124 genes were associated with the nucleus (GO:0005634), i. e. , three times more than the proportion expected for the whole genome (Figure  S3 ). This first analysis revealed that the deletion of the main esterase genes triggered an unexpected modification of genome expression in the strain Fx10-∆AE. In order to unravel this surprising expression repatterning, we sought functional connections between DEGs by searching in the protein-protein interactions (PPI) database (string-db. org). A cluster analysis of the PPI score matrix allowed the identification of 13 PPI clusters sharing functional relationships (Figure  7 ).
Each cluster encompassed more than 14 proteins and reached an average score higher than 0. 51. Most of them (clusters 1, 2, 6, 7, 8, 10, 11, and 12) were enriched in protein networks related to nitrogen and lipid metabolism (GO:0006807, GO:1901564, GO:1901566, GO:0006629). This connection with metabolism was not directly identified by the GO enrichment analysis, highlighting the interest of using a PPI score to discover functional components in genome-wide datasets. Interestingly, cluster 13 encompassed 14 proteins mostly involved in lipid metabolism and acyl transferase activity (GO:0016746), including the MAGLase Mgl2p. This result demonstrates that the lack of AATses and AEATase activities triggers the expression of many genes involved in the amino acid and lipid biosynthesis pathway. Other PPI clusters were related to RNA regulation (clusters 1, 2, and 6, GO:0016070), mitochondrion and mitochondrial translation (clusters 5 and 10, GO:0005739, GO:0032543), cell cycle (cluster 3, GO:0007049), and the site of polarized growth (cluster 9, GO:0030427), congruently with the previous GO analysis.
org/). A total of 13 clusters are visually defined (the number of proteins per cluster is shown in brackets), and the STRING PPI network connectivity is represented. For each cluster, the main functional enrichment(s) of the networks (biological process from gene ontology, local network cluster from STRING, or annotated keywords from UniProt) are represented by different colors. The presence of proteins related to chromatin modification and the wide overexpression pattern of the strain Fx10-ΔAE led us to check possible connections with histone modifications by searching in a dataset of nucleosome co-immuno-precipitation  [28]. The av-Figure  7. STRING protein-protein interaction analysis. Clustered pairwise correlation matrix (top left) of the 1124 differentially regulated genes in the Fx10-∆AE mutant.
A few other elements were identified by PPI analysis: response to stimulus (cluster 4, GO:0050896), cellular aromatic compound metabolic process (GO:0006725), and the involvement of phosphoprotein (clusters 3, 4, and 9, KW-0597). Finally, cluster 12, particularly enriched in proteins, was involved in covalent chromatin modification (CL:3190) encompassing proteins belonging to histone acetyltransferase (CL:3659) and histone deacetylase (CL:3384) complexes (https://version-11-0b. string-db. org/cgi/network?networkId=b8KiKTUhCG3J). The presence of proteins related to chromatin modification and the wide overexpression pattern of the strain Fx10-∆AE led us to check possible connections with histone modifications by searching in a dataset of nucleosome co-immuno-precipitation  [28]. The average ratios of acetylation and methylation in up-and downregulated genes were compared to those of non-significant genes (Figure  8 ).
RNA-Seq Alignment and Quantification For read alignment, we used a reference genome composed of the sequence of the S. cerevisiae laboratory strain S288c (assembly: GCF_000146045. 2, www. ebi. ac. uk) manually added with the 34 genes specific to the wine strain EC1118 (assembly: GCA_000218975. 1, www. ebi. ac. uk  [41]. Alignment was performed using the Torrent Mapping Alignment Program (TMAP) map4 module implemented in the Ion Torrent Suite 5. 0. 5. Default parameters were used to generate BAM files for each sample. A Galaxy server  [42]  was used to create tool suites called a "workflow" in a simple way: Data alignment was checked and filtered using SAMtools  [43, 44]. Unmapped reads were eliminated, as well as reads with mapping quality <20. The number of reads per gene was counted using HTseq-count  [45]  with the following options: format (-f): BAM, type (-t): mRNA, ID (-i): transcript_id, quality min.
p-Values were corrected for multiple tests with the method of Benjamini-Hochberg with R. Protein-protein interactions were analyzed using the STRING database v11 (http://string-db. org/) and the STRINGdb package ®  [48]. For each pair of DEG genes, a protein-protein interaction (PPI) confidence score was extracted from STRINGdb (http://string-db. org/). Clustering was performed using the R hclust function and the complete method. PPI clusters were defined visually and were retained when (i) they contained more than 10 genes and (ii) the mean score of the cluster was >0. 4. Individual clusters were then explored for specific functions and components using STRINGdb. The enrichment or depletion in histone modification was assessed using data from Pokholok et al. , 2005 (http://younglab. wi. mit.
Chemical Analyses 4. 7. 1. Apolar Ester Analysis In order to prevent the evolution of the ester content, all the wines were then frozen at -20 • C until analysis. Preliminary tests confirmed that freezing did not significantly affect the concentration of the investigated compounds (data not shown). Volatile compounds produced during fermentation in each condition were quantified. The concentrations of 14 esters (Table  1 ) (6 fatty acid ethyl esters, 4 higher alcohol acetates, and 4 alkylated ethyl esters) in each wine were determined using head space solid phase microextraction (HS-SPME) followed by gas chromatography-mass spectrometry (GC-MS), as previously described  [49]. 4. 7. 2. Quantification of Volatile Acids and Hydroxylated Esters by Liquid-Liquid Extraction and GC/MS Analysis Two hydroxylated esters, ethyl 3-hydroxybutanoate (3hC4C2) and ethyl 2-hydroxy-4methylpentanoate or ethyl-leucate (2h4mC5C2), were assayed according to the method previously described  [25].
Acknowledgments: The authors would like to thank  Erwan Guichoux  for his assistance in the RNA-seq experiment. The genomic facilities of Bordeaux University received grants from the  Conseil Régional d'Aquitaine n • 20030304002FA  and  20040305003FA , from the  European Union FEDER  n • 2003227, and from  Investissements d'Avenir   ANR-10-EQPX-16-01 ).
As expected, similar results were obtained in tra1 mutants with longer C-ter truncations that remove the KIN and FAT domains. We verified that the levels of the bait, Spt7, were comparable across strains. The effect of the FATC region on Tra1 incorporation into SAGA is indirect, because we previously identified a short, 40-residue region at the start of the FAT domain that mediates its interaction with SAGA (Elı ´as-  Villalobos et al. , 2019a). Examination of the S. cerevisiae Tra1 structure shows that these residues are located about 45 A ˚from the FATC region and have no direct intramolecular contacts with the FATC (PDB: 5OJS) (Dı ´az-Santı ´n et al. , 2017). TTT recognizes PIKKs in a non-native state during translation Overall, we accumulated functional and biochemical evidence confirming the structural importance of the hydrophobic residues at the C-ter end of PIKKs.
STAR+METHODS Cloning strategies and primers were designed using the online fission yeast database, PomBase  (Lock et al. , 2019). All primer sequences are listed in Table  S5. Transformants were screened for correct integration by PCR and, when appropriate, verified by Sanger sequencing or western blotting. For each transformation, 2-4 individual clones were purified and analyzed. Because the tel2+, tti1+, and tti2+ genes are essential for viability in S. pombe  (Inoue et al. , 2017; Shikata et al. , 2007) , C-ter epitope tagging was performed in diploids, to generate heterozygous alleles. Their sporulation demonstrated that all C-terly tagged Tel2, Tti2, or Tti2 strains grew similarly to wild-type controls in all conditions tested (data not shown). The same strategy was used to construct tra1-AA, tra2-AA, tor2-AA, tor1-AA, rad3-AA, and tel1-AA mutants (Figure  6 ).
Mass spectrometry and data analysis Dry TCA precipitates from TAP eluates were denatured, reduced and alkylated. Briefly, each sample was dissolved in 89 mL of TEAB 100 mM. One microliter of DTT 1 M was added and incubation was performed for 30 min at 60 C. A volume of 10 mL of IAA 0. 5 M was added (incubation for 30 min in the dark). Enzymatic digestion was performed by addition of 1 mg trypsin in TEAB 100 mM and incubation overnight at 30 C. After completing the digestion step, peptides were purified and concentrated using OMIX Tips C18 reverse-phase resin (Agilent Technologies Inc. ) according to the manufacturer's specifications. Peptides were dehydrated in a vacuum centrifuge. Samples were resuspended in 9 mL formic acid (0. 1%, buffer A) and 2 mL were loaded onto a 15 cm reversed phase column (75 mm inner diameter, Acclaim Pepmap 100â C18, Thermo Fisher Scientific) and separated with an Ultimate 3000 RSLC system (Thermo Fisher Scientific) coupled to a Q Exactive Plus (Thermo Fisher Scientific) via a nanoelectrospray source, using a 143-min gradient of 2 to 40% of buffer B (80% ACN, 0.
1% formic acid) and a flow rate of 300 nl/min. MS/MS analyses were performed in a data-dependent mode. Full scans (375 -1,500 m/z) were acquired in the Orbitrap mass analyzer with a 70,000 resolution at 200 m/z. For the full scans, 3 3 106 ions were accumulated within a maximum injection time of 60 ms and detected in the Orbitrap analyzer. The twelve most intense ions with charge states R 2 were sequentially isolated to a target value of 1 3 10 5 with a maximum injection time of 45 ms and fragmented by HCD (Higher-energy collisional dissociation) in the collision cell (normalized collision energy of 28%) and detected in the Orbitrap analyzer at 17,500 resolution. Raw spectra were processed using the MaxQuant environment (v. 1. 5. 5. 1)  (Cox and Mann, 2008)  and Andromeda for database search with label-free quantification (LFQ), match between runs and the iBAQ algorithm enabled  (Cox et al.
, (2013). Briefly, label-free quantification (LFQ) intensity-based values were transformed to a base 2 logarithmic scale (Log2), to fit the data to a Gaussian distribution and enable the imputation of missing values. Normalized LFQ intensities were compared between replicates, using a 1% permutation-based false discovery rate (FDR) in a two-tailed Student's t test. The threshold for significance was set to 1 (fold change = 2), based on the FDR and the ratio between TAP and 'no TAP' samples. The relative abundance of subunits in each purification eluate was obtained by dividing the LFQ intensity of that interactor (prey) to the LFQ intensity of the TAP purified protein (bait).
RNA-seq and data analysis All strains were done in triplicate. RNA was extracted from 50 mL of exponentially growing cells RNA using TRIzol reagent. DNA was removed by DNase I digestion, using the TURBO DNA-free kit and RNA was cleaned using the RNeasy Mini kit. Total RNA quality and concentration was determined using an Agilent Bioanalyzer. Transcripts were purified by polyA-tail selection. Stranded dual-indexed cDNA libraries were constructed using the Illumina TruSeq Stranded mRNA Library Prep kit. Library size distribution and concentration were determined using an Agilent Bioanalyzer. 48 libraries were sequenced in one lane of an Illumina HiSeq 4000, with 1x 50 bp single reads, at Fasteris SA (Plan-les-Ouates, Switzerland). After demultiplexing according to their index barcode, the total number of reads ranged from 6 to 10 million per library.
ACKNOWLEDGMENTS We thank  K. Wagner  for technical assistance and all members of the laboratory for helpful discussions. We are grateful to  R. Allshire ,  H. Masukata ,  J. Petersen ,  S. Coulon , and the  YGRC (NBRP, Japan ) for sharing strains and reagents. We thank  C. Duncan  and  J. Mata  for assistance with RIPs. D. T. was a recipient of fellowships from  Labex EpiGenMed , an  Investissements d'avenir program  ( ANR-10-LABX-12-01 ), and the  Ligue Contre le Cancer. A. E. -V. was a recipient of a postdoctoral fellowship from the  Fondation pour la Recherche Medicale. This work was supported by funds from the  Agence Nationale de la Recherche  ( ANR-15-CE12-0009-01  and  ANR-15-CE11-0022-03 ) and the  Fondation ARC  ( PJA 20181208277 ) to D. H.
The qRT-PCR cycling parameters were: 98 • C for 2 min; 45 cycles of 98 • C for 5 s and 60 • C for 10 s; and 10 min at 95 • C. To determine the amplification kinetics of each product, the fluorescence derived from the incorporation of EvaGreen into the double-stranded PCR products was measured at the end of each cycle using SsoFast EvaGreen Supermix (Bio-Rad). The Relative Expression Software Tool (REST) was used to analyse the data  (34)  and the 16S gene was used as reference for normalization.
Microarray analysis Data were further treated with R (35) using the LIMMA package from Bioconductor  (36, 37). Intra-array normalization was performed using the local Lowess (Loess) regression method with 'Normexp' background noise correction, whereas inter-array normalization was performed using a quantile method. Differentially expressed genes were extracted using the LIMMA package, using linear regression (lmFit), Bayesian models (eBayes) and Benjamini-Hochberg multiple-test correction (BH). We considered significance in the different replicates for a P-value <0. 05. Lists of genes were further restricted, applying a log 2 fold-change of 1. 584 (∼3-fold) induction or repression criterion. The microarray dataset is available in the NCBI GEO database under access number GSE152140 (M0 versus M0 tprA) and GSE152141 (M0 versus Tn38).
To strengthen our results, we also examined the impact of ndpA2 or tprA inactivation in the M0 strain on PAPI-1 transfer. As shown in Table  1 , ndpA2 inactivation reduced the ICE transfer by 40-fold whereas tprA inactivation reduced it more than 1100-fold. 6 ). The identifies are 1: PA14 + pBBR1MCS4, 2: PA14 + pBBR-ndpA2, 3:PA14 + pBBR-tprA, 4: PA14 + pBBR-ndpA2 tprA. The Y-axis represents the relative fold change between PA14 + pBBR1MCS4 and the other strains. Data were analysed using REST software in which significant differences are determined by an ANOVA using approximate tests  (34). *, **, *** and NS indicate P < 0. 05, P < 0. 01 and P < 0. 001 and a non-significant difference, respectively. Previous studies have shown that the acquisition of PAPI-1 by P. aeruginosa promotes biofilm formation  (14).
ACKNOWLEDGEMENTS We thank  Yann Denis  for technical assistance at the transcriptome platform of the  Institut de Microbiologie de la Méditerranée. We also thank  Artemis Kosta  and  Hugo Le Guenno  for technical assistance at the microscopy platform of the  Institut de Microbiologie de la Méditerranée  and  Morgane Maniveau  for her help with MvaT and NdpA2 EMSA.
Results Identification of common progenitor cells in the CPM. To identify the various populations that constitute the CPM (Fig. 1a ), we performed droplet-based scRNA-seq on fluorescenceactivated cell sorted GFP expressing cells from microdissected Mesp1 Cre ;ROSA26-GFP f/+ (f, flox) embryos at E8. 0, E8. 25, E9. 5, and E10. 5 (Table  1 , Fig. 1b , Supplementary Figs. 1a, b , 2a, b, 3, and Supplementary Table  1 ). These stages were chosen because they are the critical periods when Tbx1 is expressed, reaching the highest expression at E9. 5, and when the pharyngeal apparatus is dynamically elongating; this is coordinated with heart development and BrM specification. To better understand the developmental sequence of events, we integrated the four time point datasets (Fig. 1c ) and identified 20 cell clusters (Fig.
The intersection of these two datasets defines the CPM more precisely. The Tbx1 Cre allele has a knockin of Cre that inactivates one copy of the Tbx1 gene. The Tbx1 Cre mice do not have cardiac or aortic arch defects in the SwissWebster background  36. We then performed scRNA-seq of the Tbx1 Cre lineage at E9. 5 and integrated this with data from the Mesp1 Cre lineage at the same stage to compare the characteristics of the two lineages (Fig. 4a-c  and Supplementary Data 2). Data integration provides consistency in defining common cell types among different samples, in addition to removing batch effects  37. We found that the CPM can be identified in both populations (Fig. 4a , bold font). As expected, the Mesp1 lineage includes the FHF, which is not included in the Tbx1 lineage, while the Tbx1 lineage includes the pharyngeal epithelia and otic vesicle, not included in the Mesp1 lineage (Fig.
5g, h ). The ratio of the number of MLPs compared to the total number of cells in each replicate of Mesp1 Cre Ctrl versus cKO shows a modest variation (first replicate of WT (WT1) is 0. 07 (284/4046 cells), WT2 is 0. 129 (912/7048) vs first replicate of cKO (KO1) is 0. 101 (474/4689) and KO2 is 0. 108 (1056//9750)), nonetheless, MLPs are present in both (Fig. 5c-f ). To understand how Tbx1 affects gene expression within the MLPs and derivative cell types leading to the observed phenotypes at later stages, we analyzed differentially expressed genes (DEGs) with the scRNAseq datasets. We identified DEGs in each cluster in the two replicates of Mesp1 Ctrl vs cKO embryos (Supplementary Data 4) and separately DEGs in Tbx1 Cre Ctrl vs Tbx1 Cre cKO embryos at E9. 5 (Supplementary Data 6). The DEGs in the two Mesp1 Cre Ctrl vs cKO replicates overlapped significantly, and the final DEGs were from the pooled replicates.
Furthermore, to focus on the most reproducible alterations, we examined only DEGs shared in both Mesp1 replicates and between Mesp1 and Tbx1 scRNA-seq datasets at E9. 5, which change in the same direction, with adjusted P value < 0. 05 and have an absolute log2-fold change >0. 25 (Fig. 6a ). In the MLPs, we identified 651 DEGs; 468 genes were decreased and 183 were increased in both Mesp1 and Tbx1 cKO embryos at E9. 5 (Fig. 6b , Supplementary Data Gene ontology (GO) analysis was used to identify enriched biological functions of the downregulated genes shared in both Mesp1 and Tbx1 cKO embryos (Supplementary Data 8). Genes affected in MLPs are involved in cell migration, organ development, and muscle development (Fig. 6c ; e. g. , Sox9, Mef2c, Grem1, Hey1, Bmp7, Fgf10; Foxf1, Wnt5a). Mef2c 39 , Hey1 40 , Bmp7 41 , Fgf10 3 , and Wnt5a 42,43 are required for normal cardiac development.
These genes are at the top fold change among DEGs decreased (Aplnr) or increased (Pax8) in the MLPs of Mesp1 Cre cKO vs Ctrl embryos, with consistent results in Tbx1 Cre Ctrl vs cKO embryos (Supplementary Data 7). Ectopic Pax8 expression was observed in d Single-cell embedding graph with PAGA plot of the CPM clusters. e Single-cell embedding graph of only MLPs colored by stage. f Single-cell embedding graph of the CPM (above) and MLPs (below) colored by gene expression level (gray to blue, green, yellow, low to high). g Heatmap of expression of genes enriched in the CPM. Row indicates the expression per cell (blue to white, to red; low to high). h-j RNAscope in situ hybridization with Nrg1 (green), Aplnr (red), and Isl1 (blue) mRNA probes on sagittal sections from wild-type embryos at E8. 5 (upper panel; n = 3) and E9.
Overall, the three independent scRNA-seq datasets suggest that Tbx1 promotes lineage maturation but restricts ectopic expression of non-mesodermal genes in MLPs. x i L 3 a t a G 1 d l a z a K 2 d h f E 1 t 1 x n u R 2 b 1 1 d s H b 5 c n U 6 a t a G 2 b f g T 4 p m B 1 o d C 5 s g R 1 i n n T Mexom 1 2 f c T E10. 5 In addition to MLPs, we examined DEGs in other CPM populations (BrM, SoM+aSHF, pSHF, and CMs; Supplementary Fig. 11  and Supplementary Data 9). Of interest, Tbx1 is expressed strongly in the BrM populations. We found that there are 667 genes decreased and 163 increased in expression in the BrM population at E9. 5 (Supplementary Fig. 11a ). Lhx2 and Myf5 are representative genes that are specific for and downregulated only in the BrM populations (Supplementary Data 9). This suggests the potential of direct transcriptional regulation.
e. , overlapping with DARs), which include some TBX1-binding sites in closed regions that did not change in accessibility in the mutant data (Fig. 8c ), suggesting a diverse role of TBX1 in promoting chromatin remodeling. ChIP-seq peaks were annotated to predicted direct transcriptional target genes (443 genes, 470 peaks, Supplementary Data 14). We then intersected the DEGs reduced in MLPs, the annotated genes from Mesp1-DARs and TBX1 ChIP-seq targets (Fig. 8b ). We found 21 known genes (Fig. 8b  and Supplementary Data 14) common in all three datasets (P < 0. 001; permutation test). Among them, eight had a DAR that overlapped with a TBX1 ChIP-seq peak (Slit1-intron, Crybg3-intron, Nrg1-upstream, Trps1-downstream, Sox9-upstream, Trmt9b-downstream, Fn1-upstream, and Crtc2promoter region). Data is consistent with TBX1 binding to accessible chromatin in control embryos, but the interval is not accessible when Tbx1 is inactivated.
f Single-cell embedding graph of the CPM lineages from Tbx1 Cre Ctrl and cKO at E9. 5 datasets separated by genotype and colored by expression level (gray to blue, green, yellow, low to high). g RNAscope in situ hybridization with EGFP (green), Aplnr (red), and Pax8 (blue) mRNA probes on the transverse section from Mesp1 Cre/+ ;ROSA26-GFP f/+ ;Tbx1 f/+ (Ctrl) embryos (upper panel; n = 3) and Mesp1 Cre/+ ;ROSA26-GFP f/+ ;Tbx1 f/f (cKO) embryos (lower panel; n = 3) at E9. 5. Scale bar, 100 µm. The white dotted line indicates the position in higher magnification images shown in the right. Scale bar, 30 µm. h RNAscope in situ hybridization with EGFP (green), Aplnr (red), and Pax8 (blue) mRNA probes on the transverse section from Tbx1 Cre/+ ;ROSA26-GFP f/+ (Ctrl) embryos (upper panel; n = 3) and Tbx1 Cre/f ;ROSA26-GFP f/+ (cKO) embryos (lower panel; n = 3) at E9.
1c, d ). For RNAscope analysis, littermates were used from a cross of Tbx1 Cre/+ male mice and ROSA26-GFP f/f ;Tbx1 f/+ ; female Fig. 8  Open chromatin status in TBX1-binding regions in vivo was reduced in Tbx1 cKO embryos. a Pie chart of TBX1 ChIP-seq peaks from three biological replicates. Among 255 ChIP-seq peaks, 176 had a T-box motif. The bar chart indicates peak intersection with ATAC-seq data. b Venn diagram for overlap of genes with ChIP-seq peaks or ATAC-seq DARs-Mesp1 regions or downregulated expression in Tbx1 cKO MLPs (P value = 0. 3924). c Average chromatin accessibility at ChIP-seq peaks. Left, ATAC-seq data from Tbx1 Cre Ctrl; right, ATAC-seq data from Tbx1 Cre cKO. Top, average ATAC-seq signal in TBX1 ChIP-seq peak regions; Bottom, heatmap of ATAC-seq read densities in TBX1 ChIP-seq peaks.