dataesr/scientific-paragraphs-categorization huggingface dataset:
RESULTS TACC1 variants exhibiting differential expression between HNSCC and NHOK According to GenBank, the human TACC1 gene has a total of 31 transcript variants that encode protein (Fig. 1A ). Because TACC1 variants have been suggested to exhibit temporal and spatial expression patterns in human organs  [15] , we performed RT-PCR to compare the characteristics of their expression in HNSCC cell lines and normal human oral keratinocytes (NHOKs). Higher expression in NHOKs was detected in variants 25 and 27. Ten TACC1 variants, including variants  3, 4, 8, 9, 11, 17, 20, 22, 23 and 30,  were only expressed in HNSCC cell lines. Variants 2, 15, 26, 27, 28 and 31 were expressed in both NHOK and HNSCC cell lines (Fig. 1B ). Variants 2, 26 and 27 were expressed in each cultured cells, however, variant 27 was extremely higher expressed in NHOKs than that of in HNSCC cell lines (Fig.
1C ). Furthermore, TACC1v25 was significantly highly expressed in normal tissue compared to primary HNSCC by using the online tool TSVdb (Fig. 1D ), which was consistent with our findings. Meanwhile, using the TCGA database, we extracted AS events for TACC1 in HNSCC patients. Using SpliceSeq, 11 AS events Fig. 1  Expression of TACC1 variants in HNSCC. A Diagrammatic representation of TACC1 variants. In GenBank, human TACC1 (Gene ID:6867) mRNA has a total of 31 encoding transcript variants. B Expression analysis of TACC1 variants in NHOK and HNSCC cell lines by RT-PCR. TACC1 v25 and 27 exhibit higher expression in NHOK cells. C Venn diagram of describing the differential expression patterns of TACC1 variants in HNSCC cell lines and NHOK cells. *TACC1v27 was expressed in all cell lines, but there was a significant difference in expression levels between HNSCC lines and NHOK cells.
Protein isoforms produced by AS play an important role in cancer progression including cell proliferation, cell invasion and methylation defects  [6]. The genome-wide analysis of AS in HNSCC using the TCGA program revealed a landscape of AS events related to carcinogenesis and its clinical significance  [5]. In the current study, we extracted HNSCC-RNA-seq data from TCGA database and focused on AS in TACC1. Additionally, expression of TACC1v25 was validated using TSVdb-RNA-seq from patients with HNSCC. Among the 11 AS events identified, we noticed that the AP event was most likely associated with exon X5, which was only found in TACC1v25. This observation is consistent with previous studies in which differential promoter usage with AS was applied to the TACC1 gene  [5]. The functions of different TACC1 variants are not entirely clear.
Western blotting Cells were collected and lysed in ice-cold RIPA lysis buffer (Beyotime, Shanghai, China). The supernatant was collected after centrifugation at 14,000g for 5 min. Then, 20 µg of total protein was separated on 6-15% SDS-PAGE gels, and the proteins in the gel was transferred to a 0. 22 μm PVDF membranes (Millipore, USA). Membranes were incubated with primary antibodies against TACC1, LC3B, Beclin-1, AKT, p-AKT, PI3K, p-PI3K, mTOR, p-mTOR, ERK1/2, pERK1/2, MEK, pMEK, LaminA, Flag and GAPDH overnight at 4 °C. Details of the antibodies used are shown in Supplementary Table  S2. In particular, anti-TACC1 (ab17915, Abcam, Cambridge, UK), was against TACC1 containing mature exon 5 s and the TACC domain. Verification of TACC1v25 expression using TCGA RNA-seq data from HNSCC samples HNSCC samples were extracted from the SpliceSeq database (http:// bioinformatics.
mdanderson. org/TCGASpliceSeq/), including 387 samples (351 HNSCC and 36 normal samples) after excluding missing values. Data on percent-splice-in (PSI), the ratio of normalized read counts indicating the inclusion of a transcript element over the total normalized reads for that event, were collected from the database. The percentage of samples with a PSI value ≥75% was set as a filter to obtain a more reliable set of AS events. Modes of AS events were previously defined as follows: AA, AD, AP, AT, ES, ME and RI. 12. The analysed HNSCC samples originated from TCGA and included 564 samples (520 primary HNSCC and 44 normal samples). Normalized expression levels of TACC1v25 in HNSCC were collected from the TSVdb online tool (http://www. tsvdb. com). Data were processed using GraphPad Prism 8.
Statistical analysis All descriptive analyses were performed using STATA 15. 0 (Stata Corp LP, College Station, TX, USA) software. Quantitative variables were categorized according to statistical criteria using the rst and third quartiles and the median. Single comparisons were performed using Student's t-test for quantitative variables and the CHI2 or Fisher's exact test for qualitative variables. Bivariate analysis was used to study covariates and their relationships with the outcome measures based on the crude odds ratio and its con dence interval. The covariates that were associated with the outcomes were then included in an unconditional multivariate logistic regression model. We expressed the results as the means, odds ratios and con dence intervals. P-values < 0. 05 were considered to be statistically signi cant.
The data used in this study were collected from inpatient medical records at time of birth and the outpatient medical record at the neonatal clinic visits on days 7, 14 and 1 month of life. The data collected included demographic data, neonatal and maternal clinical presentation at the time of delivery, during hospitalization and after discharge from hospital, microbiological results (PCR-SARS-CoV-2 test). Newborns were assessed while in hospital and at home for fever, hypothermia, respiratory distress, lethargy, cough, rhinorrhea, irritability, rash, diarrhea, and food intolerance. The mothers were evaluated during their hospitalization and at home by the midwife for fever, cough, anosmia, ageusia, shortness of breath, sore throat, rhinorrhea, myalgia, vomiting and diarrhea.
, 2015). Images are acquired on hand-cut cross section with a flatbed document scanner. The anatomical quantification rate required for genetic and physiological studies is therefore achieved. However, this method does not allow to demarcate the pith of the rind  (Casto et al. , 2018). In contrast, FASGA (abbreviation of Spanish names of fucsina, alcian blue, safranina, glicerina, and aqua) staining of internode cross sections  (Tolivia and Tolivia, 1987 ) is a powerful approach that allows to distinguish highly lignified tissues and poorly lignified tissues. It was recently applied in sorghum  (Perrier et al. , 2017)  and maize  (El Hage et al. , 2018; Vo et al. , 2020)  cross sections. Moreover, the advent of several plugins  (Zhang et al. , 2013; Legland et al. , 2017)  is allowing quantification of anatomical features from FASGA-stained cross sections, increasing the throughput of data analysis.
Finally, each image was numerically analyzed on the ImageJ software to segment and quantify histological traits  (Legland et al. , 2017). The following nine histological traits were quantified: -Stem_area, which is the area in square centimeter of the cross-section; -Bundle_fraction, which is the percentage of the crosssection area occupied by the bundles; -Bundle_number, which is the number of bundles in the pith of the cross-section; -Lignified_fraction, which is the percentage of the crosssection area occupied by parenchyma stained by the safranin; -Non-lignified_fraction, which is the percentage of the cross-section area occupied by parenchyma stained by the alcian blue; -Rind_fraction, which is the percentage of the cross-section area occupied by the rind; From all these traits quantified by the plugin, we calculated composite variables to synthetize the following information: -Blue_parenchyma, which refers to the amount of blue intensity in the blue parenchyma; -Red_parenchyma, which refers to the amount of red intensity in the red parenchyma; -Red_rind, which refers to the amount of red intensity in the rind.
QTL Detection Genetic map of the population has been described previously  (Virlouvet et al. , 2019). Briefly, genomic DNA from 261 RIL was used for genotyping using the genotyping-by-sequencing (GBS) approach  (Elshire et al. , 2011). Initially, 955,720 markers were identified well-distributed on maize chromosomes 1-10. Among those, we selected 2,806 polymorphic markers between parental lines with less than 15% missing data among the RILs. These markers were then used to construct the linkage map using R scripts interacting with the CarthaGene software (de  Givry et al. , 2005) , as described in  Ganal et al. (2011). In total, 1,000 markers were retained. The total length of the framework map was 2,355 cM with an average spacing of around 2. 4 cM. By looking a posteriori at singletons in the dataset, we verified that the increase of the genetic map length when saturating the scaffold with additional markers was not attributable to genotyping errors but more likely to the rather high level of missing data, which introduces a bias in the imputation procedure (EM algorithm) of CarthaGene as a result of genetic interference.
QTL detection was performed using the multi-QTL mapping (MQM) method in the R/qtl package  (Broman and Sen, 2009; Arends et al. , 2010). The function mqmscan was used, setting the maximum cofactor marker number (number of individual minus 12, depending on the year and the condition), with a further processing by backward elimination. To quantify the explained variance, QTL above a LOD score at 3 (corresponding to approximately the 5% significance threshold using 1,000 permutations over all the quantified traits) were selected. The QTL detection by the MQM method was performed on the data corrected for the block effect per year and per irrigation scenario data. QTL confidence intervals were calculated as the genetic distance where LOD-1 from the QTL peak was reached.
FUNDING This study was supported by the  French Government  Grant  ANR-11-BTBR-0006 BIOMASS FOR THE FUTURE , managed by the  French National Research Agency  under an " Investments for the Future" program  (Grant  ANR-11-IDEX-0003-02 ). This work has benefited from the support of IJPB's Plant Observatory technological platforms. The IJPB also benefits from the support of  Saclay Plant Sciences-SPS  ( ANR-17-EUR-0007 ).
CLOCK:BMAL1 was among the top enriched DNA binding motifs, confirming that the core clock is needed for rhythmic expression of these genes (fig. S4B ). In addition, there was significant enrichment for several known liver metabolic regulators, such as HNF4, CEBPB, and FOXA (fig. S4B ). To investigate whether activities of these TFs differed between WT, KO, and Liver-RE mice, we determined chromatin accessibility by ATAC-seq (assay for transposase-accessible chromatin sequencing) at ZT8, which coincides with maximal DNA binding of BMAL1. We next merged our ATAC-seq dataset with our previously published BMAL1 chromatin immunoprecipitation followed by sequencing (ChIP-seq)  (13)  to identify regions of accessible chromatin bound by BMAL1. Consistent with a previous study  (34) , footprint analysis  (35, 36)  detected enrichment for HNF4, FOXA1, and CEBPB at BMAL1-bound sites (data S3).
S4, E  and F ). However, coimmunoprecipitation from liver chromatin fractions showed that CEBPB interacts with BMAL1 at ZT8 (fig. S4G ), suggesting that CEBPB and BMAL1 may cooperate at chromatin. To investigate genomic interplay of CEBPB and BMAL1, we performed ChIP-seq on liver from WT mice at ZT8 and ZT20. We identified 45,547 and 43,035 replicate-concordant CEBPB peaks at ZT8 and ZT20, respectively. Using our previously published BMAL1 ChIP-seq dataset (13), we calculated the peak overlap with BMAL1. This analysis revealed extensive overlap, as 82% of BMAL1 peaks overlapped with CEBPB (10,837 peaks at ZT8; Fig. 3B  and data S4 ) and the majority of CEBPB sites (97%) did not change between ZT8 and ZT20 (fig. S5, A  and B ). Thus, recruitment of CEBPB to DNA does not appear to be rhythmic.
6, B  and C , and fig. S12, B  and C ). In addition, we used a published dataset in which liver metabolites were correlated with metabolites in other metabolic tissues over the daily cycle (53) to identify connections. Metabolites that failed to oscillate in Liver-RE even under NF correlated mostly with muscle, followed closely by serum (fig. S12, D to F ). Together, these analyses point to a potential role of the muscle BMAL1 in the regulation of daily liver metabolism. To assess the role of the muscle clock in supporting liver transcriptional rhythms, we performed RNA-seq of livers harvested over the daily cycle from either WT or muscle-specific Bmal1-KO mice (mKO) (Fig. 6D  and data S8). mKO mice do not exhibit behavioral phenotypes tied to feeding-fasting  (9, 54). Liver core clock function at the protein and transcript levels was unaltered in and mKO (fig.
S13, A  and B ). JTK_CYCLE found that 60% of oscillating genes in WT were maintained in mKO livers (fig. S13, C  to E). Using JTK_CYCLE output, LimoRhyde revealed 371 liver transcripts (16% of total oscillating) displaying differential rhythmicity in the absence of the muscle clock (fig. S13F ). These genes were enriched for TCA cycle, fatty acid -oxidation, and oxidationreduction pathways, among others (Fig. 6, E  and F ), similar to network-dependent transcripts and WT-only oscillating metabolite pathways. Of these, 94 (25%) oscillated in both genotypes but with different amplitudes or phases, 84 (23%) lost oscillation in mKO, and 193 (52%) gained oscillation in mKO (Fig. 6E  and fig. S13F ). Moreover, a clear separation of peak phase is observed in a subset of genes oscillating in both genotypes (Fig.
6E ), revealing that the muscle clock is required for their proper phase alignment. Commonly oscillating genes had comparable amplitude (fig. S13G ). Overlap of network-dependent genes with those lost in mKO (by JTK_CYCLE, P < 0. 01) tied 30% of network-dependent genes to muscle BMAL1 (fig. S13, H to J ). Together, these data demonstrate that the muscle clock regulates a subset of daily transcript rhythms in the liver. ). (E) Phase-aligned heatmaps of circadian genes identified by JTK_CYCLE then subjected to LimoRhyde analysis to reveal differentially regulated transcripts. Both, genes oscillating in livers from WT and mKO; both differential rhythmicity, genes oscillating in livers from both genotypes but with different phases and/or amplitudes; lost in mKO, genes that lose oscillation in livers from mKO; gained in mKO, genes that oscillate in mKO exclusively.
Phase maps of oscillating genes and example genes from each classification displayed below corresponding heatmap. (F) GO analysis from genes designated as either unaltered or differentially regulated by LimoRhyde analysis. (G) Experimental setup for serum treatment of primary hepatocytes and individual serum samples from WT and mKO mice were assessed by RNA-seq for their impact on gene expression in cultured hepatocytes (n = 5 per genotype). (H) Volcano plot of genes significantly regulated by treatment with mKO serum versus WT (FDR < 0. 05). (I) GO analysis of significantly altered transcripts in primary hepatocytes treated with mKO versus WT serum. Next, we next sought to reveal whether the serum represents a communication route between the muscle clock and the liver. Cultured primary hepatocytes were treated with serum harvested at ZT16 from either WT or mKO mice (serum from 1 mouse = 1 biological replicate, n = 5), and transcriptional changes were assessed by RNA-seq (Fig.
Metabolic cage data acquisition Indirect calorimetry was performed with negative flow Oxymax-CLAMS (Columbus Instruments, Columbus, OH) hardware system cages. Mice were given a 24-hour acclimation period to the metabolic cage. Measurements of VO 2 , VCO 2 , food intake, water intake, and feeding activity were taken every 10 min for up to five consecutive days at room temperature. The RER (RER = VCO 2 /VO 2 ) was calculated by the accompanying Oxymax software.
Partial stub oligos for Illumina adapters were ligated on the ends, and the adapter-ligated fragments were enriched and barcoded by nine cycles of PCR using unique dualindexing primers. The resulting libraries were validated by qPCR and sized by Agilent Bioanalyzer DNA high-sensitivity chip. The concentrations for the libraries were normalized and then multiplexed together. The pooled library concentration for denaturation was 2 nM, and the final concentration for loading was 400 pM. The multiplexed libraries were sequenced on the S2 flow cell using paired-end 100 cycles chemistry for the Illumina NovaSeq 6000. The version of NovaSeq control software was NVCS 1. 6. 0 with real-time analysis software, RTA 3. 4. 4. Postprocessing of the run to generate the FASTQ files was performed at the Institute for Genomics and Bioinformatics (UCI IGB).
The paired-end reads from each replicate were separately aligned to the reference genome assembly mm10 and corresponding transcriptome using the Tuxedo protocol (Illumina)  (86). Reads uniquely aligned to known exons or splice junctions extracted with no more than two mismatches were included in the transcriptome. Reads uniquely aligned but with more than two mismatches or reads matching several locations in the reference genome were removed. For time series datasets, rhythmic transcripts were detected with the nonparametric JTK_CYCLE algorithm  (28)  incorporating a period of 24 hours. Genes were considered rhythmic over the circadian cycle if their permutation-based, adjusted P value was <0. 01. Metabolites were evaluated with an adjusted P < 0. 05 given the inherent variability of metabolite quantification across mice and the n value of this experiment.
Additional detection of rhythmicity for transcripts was conducted using a different, independently generated algorithm, BIO_CYCLE  (29). Differential rhythmicity analysis was carried out using LimoRhyde  (30). Starting rhythmicity detection was JTK_CYCLE; P < 0. 01. Transcripts were considered differentially rhythmic at pDR < 0. 01. GO biological process (DIRECT) enrichment analysis was conducted using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) version 6. 8  (87). TFBSs were analyzed in the promoter regions [-10,000 to +2000 base pairs (bp) of transcription starting site (TSS)] of circadian genes. Binding sites were determined from experimental ChIP-seq data and MotifMap  (32)  results for the mouse genome build mm10 [BBLS (Bayesian branch length score) > 1, FDR < 0.
0. 1 tool  (88). Differential expression analysis was assessed with edgeR v3. 30. 0 R Bioconductor package  (89) , and only genes with a cutoff expression of >1 CPM (counts per million reads mapped) in at least two samples were included in the analysis. TMM method was applied to normalize gene counts, and glmQLFTest and decideTestsDGE functions were used to perform differential analysis, with a minimum cutoff of an absolute fold change of ≥1. 2 and an FDR of ≤0. 05. Complete list of differentially expressed genes and statistical information (P value and FDR) for each condition are available at Gene Expression Omnibus (GEO: GSE158600, GSE171432, and GSE171184).
Exon-intron analysis For intron-exon analysis, reads were mapped to the mouse genome (mm9) using STAR v2. 6. 0c (90)  with the option "--quantMode TranscriptomeSAM," and then gene expression level in fragments per kilobase million (FPKM) was quantified using RSEM v1. 3. 1  (91). The gene annotation file was downloaded from GENCODE (www. gencodegenes. org/mouse/release_M1. html), and the coordinates of exons and introns were generated from it. To avoid assigning the exon-junction reads to introns  (92) , introns were shrank by 10 bp at both 5′ and 3′ ends. The "coverageBed" module in bedtools v2. 25. 0 (93) was used to generate read count in each intron/exon. The option of "-F 0. 5" was used to make sure a particular read was not counted in both exon and intron. The read count per kilobase per million reads (RPKM) value was calculated as the expression level of each intron/exon normalized by both intron/ exon length and the total intronic/exonic read count of each sample.
Cross-tissue gene correlation All datasets, analysis, and walk-through are provided at https://github. com/marcus-seldin/endocrine_communication-nonrestored-clocks. Briefly, genes identified as network-dependent (this study) were taken from liver gene expression in the Hybrid Mouse Diversity Panel  (94, 95). All peripheral genes (adipose, muscle, hypothalamus, and intestine) from the same mice were then correlated across the network-dependent genes in the liver, where the global levels of cross-tissue correlation were quantified as a strength of cross-tissue predictions (Ssec) score  (49). The Ssec score for each peripheral gene was normalized to the average correlation for each given tissue and scaled according to fold change relative to the same Ssec for noncircadian genes. Collectively, this enabled us to pinpoint peripheral genes that correlated specifically with network-dependent liver genes.
ChIP sequencing Library construction was performed from two biological replicates using the NEBNext Ultra DNA Library Prep for Illumina kit (E7370) according to the manufacturer's protocol. Briefly, input and ChIPenriched DNA were subjected to end repair and addition of "A" bases to 3′ ends, ligation of adapters, and USER excision. All purification steps were performed using AgenCourt AMPure XP beads (Beckman Coulter, A63882   (97)  with the following settings (bowtie2 -q -local). Mapped reads were then sorted using SAMtools 1. 9 (98) and filtered with Sambamba v0. 7. 1  (99)  with the following settings (sambamba view -h -t 2 -f bam -F "[XS] == null and not unmapped and not duplicate"). ChIP-seq quality assessment was performed using ChIPQC v1. 26. 0 R Bioconductor package  (100). CEBPB-enriched regions relative to input DNA were assessed using the ENCODE recommended Irreproducibility Discovery Rate (IDR) framework.
Peaks in both replicates were detected with MACS2 v2. 2. 7. 1  (101)  with the following settings (macs2 callpeak -f BAM -g 1. 87e9 -B -p 1e-3). Peak consistency between true replicates was detected with IDR v2. 0. 4. 2 (https://projecteuclid. org/journals/annals-ofapplied-statistics/volume-5/issue-3/Measuring-reproducibilityof-high-throughput/10. 1214/11-AOAS466. full) with the following settings (--input-file-type narrowPeak --rank p. value). Complete list of peak coordinates and statistical information (P value and FDR) in each sample and between replicates are available at Gene Expression Omnibus (GEO: awaiting reference number). To compute the overlap between CEBPB-enriched regions and previously published BMAL1-enriched regions (13), BMAL1 raw reads were retrieved from GEO:GSE132659 and were mapped, filtered, and peaks detected using the IDR framework.
We then intersect CEBPB and BMAL1 peak coordinates with bedtools v2. 30. 0 with the following settings (bedtools intersect -b -a -u). Peak annotation was assessed with ChIPseeker v1. 26. 0 R Bioconductor package (102) using the prebuild annotation database TxDb. Mmusculus. UCSC. mm9. knownGene [tssRegion=c  (-2000, 2000) ]. Coverage tracks for each sample were computed with bedtools bamCoverage option, with the following settings (bamCoverage --binSize 20 --normalizeUsing BPM --smoothLength 60--extendReads 150 -centerReads). To obtain a global evaluation of enrichment around the TSS, we used both the computeMatrix and plotHeatmap options of bedtools package, with the following settings (computeMatrix reference-point --referencePoint TSS --beforeRegion-StartLength 2000--afterRegionStartLength 2000 -skipZeros).
Differential enrichment analysis was assessed with DiffBind v3. 0. 3 R Bioconductor package (103) using default system settings, providing prebuild DBA_BLACKLIST_MM9 black list coordinates and exporting reads with the following settings (bUsePval = TRUE,th = 0. 05). Statistical analysis and visualization of functional profiles for genes and gene clusters associated with TSS region were computed with clusterProfiler R Bioconductor package v3. 16. 1 (104), using org. Mm. eg. db R Bioconductor annotation database, with a minimum threshold of an FDR of ≤0. 05.