dataesr/scientific-paragraphs-categorization huggingface dataset:
Upregulation of NR0B2 in Cholangiocytes From Abcb4À/À Livers Adenosine triphosphate-binding cassette subfamily B member 4 (Abcb4)À/À (ie, Mdr2: multiple drug resistance 2) mice are used as a model for sclerosing cholangitis because they reflect the progressive fibrosing cholangitis aspects of human PSC and also develop intrahepatic cholangiocarcinoma. 19, 20 Abcb4 is an adenosine triphosphatebinding cassette transporter that is required for the biliary excretion of phosphatidylcholine. 21 Single-cell transcriptomes from wild-type and Abcb4À/À mouse models are provided by the GSE168758 data set. 22 With 4 replicates in each group, a single-cell WT and Abcb4À/À normalized object was built with 46,087 cells which individually expressed a minimum of 300 genes. After dimension reduction and clustering, 15 cell communities were identified and classified in 5 cell types: hepatocytes, cholangiocytes, endothelial cells, macrophages, and lymphocytes (Figure  5A  and Figure  A2 ).
These results suggest that the NR0B2 NR was upregulated in the cholangiocytes of a mouse model of sclerosing cholangitis. Metabolic Regulation During the Cell Fate Decision of Abcb4À/À Cholangiocytes NR0B2 (ie, small heterodimer partner) is a transcription regulator involved in BA metabolism, lipid metabolism, and glucose and energy homeostasis. 25 Starting from the singlecell transcriptome GSE168758 data set, a random subset of Abcb4À/À cholangiocytes was selected to build a single-cell trajectory based on the alternative expression of NR0B2 and SOX9. Within the cholangiocyte compartment of 1316 cells, 238 cells were SOX9-and NR0B2-double negative, 51 cells were SOX9-and NR0B2þ, 406 cells were SOX9þ and NR0B2-, and 621 cells were SOX9þ and NR0B2þ double positive (Figure  6A  and Figure  A4, A  and B ).
Pseudotime transformation of a single-cell transcriptome in this selected compartment (Figure  6  and Figure  A4C ) reflected a cell trajectory involving progressive modification of the expression of SOX9 (Figure  A4D ) and NR0B2 (Figure  A4E ). Identification of the most significant regulated genes on this cell trajectory (Table  A4 ) revealed 3 regulated gene clusters (Figure  6C ). The expressions of SOX9 and NR0B2 were closely regulated on the trajectory of the pseudotime green gene cluster (Figure  6C ), where the expression of Alb, GSTA3, inhibitor of DNA binding 2 (ID2), and TMEM45A were found to be regulated alongside NR0B2 and SOX9 (Figure  6C  and Figure  A4F ). A protein-protein network performed with genes belonging to the green pseudotime gene cluster with functional inference of the Gene Ontology Biological Process database was able to identify functional enrichment of the positive regulation of metabolism (FDR P value ¼ 5.
Discussion The integration of omic-type biological data-either published or available from clinical, animal, and cellular models-enabled a synthesis of knowledge and the determination of genes or pathways that had not previously been highlighted. This is classic for genomic-type data and is becoming widespread in oncology through the use of the The Cancer Genome Atlas database. It is much less common in the field of physiopathology, particularly with regard to rare diseases for which omic data are rare. For example, through the text mining of lipid-related genes, we were able to link inflammation to hepatocarcinogenesis during nonalcoholic hepatitis. 26 We used the "Génie" text mining program based on physiopathological keywords which delivers and ranks a set of genes cited in the PubMed, MED-LINE, and National Center for Biotechnology Information (NCBI) database  12  and then cross-validated this data set with the relevant omic data.
Based on 3 keywords-biliary inflammation, biliary fibrosis, and biliary stasis  1  -525 genes were found to be related to PSC. 27  This set of genes was challenged on liver RNA omics data from patients with diverse causes of liver disease (GSE61256). The paper from which this data set was derived was not found using the keywords because its subject was NAFLD, and PSC patients were only included in the control group. 7 These genes showed a clear clustering of PSC samples. The top-ranked gene was NR1H4 (FXR) for which role/therapeutic target is clear in both cholestatic and fatty liver diseases. 28 By analyzing these 525 genes in FXR KO mice and the liver single-cell transcriptomes of PSC Abcb4À/À mice, we were able to reveal the important involvement of NR0B2 which is both FXR-dependent at transcriptional level and a NR associated with FXR NRs and thus involved in numerous pathways such as metabolism, cell proliferation, and inflammation in BA metabolism.
Biomedical PubMed Text Mining "Génie" data-mining tools were able to perform gene prioritization  9  by linking scientific literature from the MEDLINE database with gene information from the NCBI Gene database and orthologous gene information on different species from the HomoloGene database. 12, 45 The Génie algorithm works by connecting the gene identifier and gene ortholog NCBI databases with genes related to the keywords in article abstracts from the PubMed literature resource. PSC-related symptoms Medical Subject Heading terms were individually submitted to the "Génie" algorithm on July 2, 2021, as keywords. All PubMed abstracts were taken as the background for the analysis of genes corresponding to Homo sapiens coding proteins. Information on all orthologs was collected during the process.
Public Data Sets Transcriptome Studies. GSE61256 is a data set of human liver biopsies (n ¼ 134) that was developed to study nonalcoholic steatohepatitis. Among the controls, we found 14 patients with a diagnosis of PSC with very few clinical annotations regarding severity. 7 The transcriptomes were downloaded and annotated with the corresponding platform (Gene Expression Omnibus Platform identifier for annotation: GPL11532) used for the Affymetrix GeneChip Human Gene 1. 1 ST Array (alias HuGene-1_1-st) available at the following address: https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi? acc¼GPL11532 (accessed on July 2, 2021). GSE54557 is a data set on livers from Mus musculus, either KO for FXR (Cre recombinase on C57BL/6J genetic background) or receiving the FXR agonist. 16 FXR-null mice harbor inhibition of CYP7A1 transcription and of BA synthesis.
The expression of biomarkers was also stratified using Ishak staging: mild (0-2), moderate (3-4), and cirrhosis (5-6). GSE168758 Mus musculus Abcb4À/À single-cell transcriptome liver samples  22  came from mice lacking the phospholipid floppase Mdr2 (Abcb4) with a PSC phenotype. The bile ducts of Mdr2(À/À) mice displayed disrupted tight junctions and basement membranes, BA leakage into portal tracts, the induction of a portal inflammatory (CD11b, CD4-positive) infiltrate, and the activation of proinflammatory (tumor necrosis factor-alpha, interleukin-1b) and profibrogenic cytokines (transforming growth factor-1b). This resulted in the activation of periductal myofibroblasts leading to periductal fibrosis, separating the peribiliary plexus from bile duct epithelial cells and ultimately causing atrophy and death of the bile duct epithelium.
19 Single-cell transcriptome libraries of liver cells were prepared using the Chromium Single Cell 3 0 NextGEM Reagent Kit v3. 1 (10Â Genomics) in order to perform sequencing on NextSeq 550 (Illumina) and demultiplexing the pipeline with CellRanger v5. 0. 0 (10x Genomics). MTX matrix file formats were downloaded at https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc¼GSM5165876 (accessed on July 2, 2021). Transcriptome Analyses. An unsupervised analysis was performed using principal component analysis with FactoMineR R-package version 2. 4. 46 Gene functional enrichment analyses were performed with the ToppGene web application. 47 Differentially expressed genes were identified using the Linear Model from Microarray algorithm implemented under the R Bioconductor package version 3. 44. 3.
Bioinformatics Analysis. Single-Cell Transcriptome Analysis. Single-cell transcriptome analyses at the whole liver level were performed using the Seurat4 R-package version 4. 0. 3. 49 After canonical correlation, filtration (cells expressing a minimum of 300 genes), and scaling between replicated samples (n ¼ 4) of the WT and Abcb4À/À groups, a normalized single-cell object comprising 46,087 transcriptomes was built. Dimension reduction was achieved using principal component analysis on the most variable features (50 PCA components during dimension reduction). Then t-distributed stochastic neighbor embedding and Uniform Manifold Approximation and Projection dimensionality reductions were performed on the 30 best components of the PCA. Cell cluster communities were detected through graph-based clustering approaches.
50 Abcb4À/À cholangiocytes identified in clusters C0-C5-C11 were selected and randomly downsampled to 10% of their initial quantity in the data set. The resulting matrix containing 1316 cholangiocytes was processed to perform downstream analyses. Single-cell trajectories were then determined on Abcb4À/À cholangiocytes using the monocle2 R Bioconductor package version 2. 20. 0. 51 Cell hierarchy was constructed on the alternative expression of 2 distinct cluster markers in Abcb4À/À cholangiocytes. Pseudotime transformation on cell trajectories was performed with the DDRtree algorithm, and pseudotime cluster identification was achieved through a pseudotime expression heatmap drawn using the best genes found to be significant. Network Analyses. Gene and function relationships identified during the representative functional analysis were collected, and a network was built using Cytoscape standalone software version 3.
Methods The locations and presence of emperor penguins was assessed using two satellite imagery platforms. The primary source was ESAs Sentinel2 platform, part of the Copernicus programme. These data were accessed through the Copernicus Sentinel Hub, viewed in Sentinel Playground [apps. sentinel-hub. com/sentinelplayground/] and downloaded using the Sentinel Hub EO browser [https://apps. sentinel-hub. com/eo-browser/]. Sentinel2 imagery has been taken continuously in Antarctica since 2018 and collection can vary between several times per week to several times per month. All imagery taken between August and the end of December 2022 was assessed manually for the presence of emperor penguins at each of their five colony locations in the Bellingshausen Sea. Sentinel2 imagery has a spatial resolution of 10 m in the visible bands and groups of emperor penguins can easily be distinguished on the sea ice as they leave distinctive brown guano stains on the ice  11, 21  , which can be viewed manually on a pixel-bypixel basis.
Here we assume that the presence of brown pixels is indicative of penguin guano staining and hence the location of their colony. This staining may exist for several days after abandonment until the next snowfall, so it possible that there is some lag in the dates reported here. To ensure the best contrast when using the Sentinel Playground browser, the custom parameters [B08*0. 8,B04*0. 8,B03*0. 8] were used. These data were supplemented using Very High-Resolution WorldView3 data from MAXAR, with a spatial resolution of 0. 31 m at two sites; Pfrogner Point and Rothschild Island. At Pfrogner Point (image ID 10300100DE30A800 26th November 2022) this image was used to confirm that no penguins were present. At Rothschild Island, the WorldView3 image could confirm the presence of emperors and enable a rough count of individuals.
Acknowledgements We would like to thank the staff and crew of the  Commandant Charcot  for the helicopter counts of emperor penguin chicks for Rothschild Island. We would also like to thank  Connor Bamford  for population estimates of Verdi Inlet. WWF provided funding for the project ( WWF project Understanding emperor penguin populations and distribution in the Weddell Sea and Antarctic Peninsula ). We would also like to thank  Thomas Bracegirdle  for advice on sea ice conditions and climate model predictions.
Data availability ESA Copernicus Sentinel2 satellite images used in this study are available through https:// scihub. copernicus. eu/ or https://apps. sentinel-hub. com/eo-browser. Location data for the five emperor penguin colonies mentioned in the text is available as a shapefile from: NERC EDS UK Polar Data Centre, https://doi. org/10. 5285/a777e89c-ffcc-4ff4-981c-8f37e5ca84c2.
Specific activities of 90 Sr were similar in both contaminated plots, being approximately 1500 Bq × kg -1 (Table  1  and Table  S1 ). Additionally, we received data on the annual absorbed dose rates from thermoluminescent dosimeters in three plots out of four. The The experimental plots had significantly different levels of radionuclide contamination (Tables  1  and S1 ). The contaminated plots of Kulazhin and Masany were characterised by high ambient dose rates of 6. 84 and 3. 2 μSv × h -1 , respectively. At the same time, the highest specific activity of 137 Cs (36,990 Bq × kg -1 ) was observed in the Kulazhin plot. Spe- The soil properties in the experimental plots were slightly different (Table  2 ): the soil samples in all plots were slightly acidic, with low humus content (1-5%) and low variation in the content of important minerals, being typical for soils of this region.
In order to describe a possible functional interconnection between the identified proteins, we performed an analysis with the STRING tool (Figure  5 ), using homologous proteins, suggesting profound changes in energy metabolism and translation. These sults overlap with the transcriptomic data, further supporting the notion that a strat for coping with chronic ionising radiation exposure involves the modulation of the p tosynthesis rate, possibly for adjusting toxic ROS levels which are produced during en getic processes involving electron transport chains (ETCs), but also due to water radiol  [2, 25, 26, 28]. [29]  for proteins with significantly different abundance tween contaminated and reference plots (homologous proteins of Medicago truncatula L. ). Line th ness indicates the strength of data support (confidence).
Non-Targeted Metabolomic Analysis of V. cracca Populations To specifically describe plant molecular phenotypes and to reveal the results of in actions of transcriptomes and proteomes, we performed a non-targeted GC-MS anal of metabolites in V. cracca leaves. The obtained metabolic profiles included around compounds, of which 70% were identified. We filtered the metabolites with close QC ues for all replicates and selected 139 for further statistical analysis. Their concentrati are presented in Table  S4. The normalised data were used for the PCA analysis. The sco plot (Figure  S1 ) shows five principal components (PC1-PC5) explaining 74. 2% of va tions from the sixteen samples of V. cracca. According to PC1, which explains 24. 1% of variance, the metabolite profiles of samples from control plots were grouped separa from samples of contaminated plots, which overlapped.
Non-Targeted Metabolomic Analysis of V. cracca Populations To specifically describe plant molecular phenotypes and to reveal the results of interactions of transcriptomes and proteomes, we performed a non-targeted GC-MS analysis of metabolites in V. cracca leaves. The obtained metabolic profiles included around 750 compounds, of which 70% were identified. We filtered the metabolites with close QC values for all replicates and selected 139 for further statistical analysis. Their concentrations are presented in Table  S4. The normalised data were used for the PCA analysis. The scores plot (Figure  S1 ) shows five principal components (PC1-PC5) explaining 74. 2% of variations from the sixteen samples of V. cracca. According to PC1, which explains 24. 1% of the variance, the metabolite profiles of samples from control plots were grouped separately from samples of contaminated plots, which overlapped.
0 against the Arabidopsis thaliana library as a reference closest dicotyledonous plant in the database. Metabolic pathways altered considerably via chronic radiation exposure were revealed (Figure  7C ), which included galactose metabolism (impact value: 0. 27, FDR = 0. 002), tyrosine metabolism (impact value: 0. 22, FDR = 0. 007), and citrate cycle (TCA cycle) (impact value: 0. 15, FDR = 0. 01) (Figure  7C ). Kyoto Encyclopedia of Genes and Genomes (KEGG) identification numbers were used for pathway analyses of significantly changed metabolites (Table  5 ) in MetaboAnalyst 5. 0 against the Arabidopsis thaliana library as a reference closest dicotyledonous plant in the database. Metabolic pathways altered considerably via chronic radiation exposure were revealed (Figure  7C ), which included galactose metabolism (impact value: 0.
Multi-Omics Data Integration The PaintOmics4 tool was used to identify pathways enriched or under-represented in contaminated plots on different functional levels for a holistic data view. Among them were general pathways of carbon (Figure  S2 ) and fatty acid catabolism (Figure  S3 ), as well as N-glycan biosynthesis (Figure  S4 ). The multi-omics analysis confirmed changes in the citrate cycle, where fumarate, 2-oxoglutarate, and malonate accumulation was observed and citrate concentrations decreased (Figure  8 ), and in pyruvate metabolism (Figure  9 ), where the accumulation of pyruvate was accompanied by the downregulation of glycolysis in general (Figures  9  and S2 ). Profound changes in photosynthesis were revealed (Figure  10 ). Among more specific pathways, we observed the upregulation of the glyoxylate cycle (Figure  S5 ), the downregulation of biotin metabolism (Figure  S6 ), and the upregulation of certain branches of propanoate metabolism, which led to β-alanine synthesis Blue-downregulated metabolites, grey-non-significant, red-upregulated metabolites.
Multi-Omics Data Integration The PaintOmics4 tool was used to identify pathways enriched or under-represented in contaminated plots on different functional levels for a holistic data view. Among them were general pathways of carbon (Figure  S2 ) and fatty acid catabolism (Figure  S3 ), as well as N-glycan biosynthesis (Figure  S4 ). The multi-omics analysis confirmed changes in the citrate cycle, where fumarate, 2-oxoglutarate, and malonate accumulation was observed and citrate concentrations decreased (Figure  8 ), and in pyruvate metabolism (Figure  9 ), where the accumulation of pyruvate was accompanied by the downregulation of glycolysis in general (Figure  9  and Figure  S2 ). Profound changes in photosynthesis were revealed (Figure  10 ). Among more specific pathways, we observed the upregulation of the glyoxylate cycle (Figure  S5 ), the downregulation of biotin metabolism (Figure  S6 ), and the upregulation of certain branches of propanoate metabolism, which led to β-alanine synthesis (Figure  S7 ).
Structural modifications of ribosomes were also evident (Figure  S8 ), accompanied by significant changes in RNA polymerase subunits (Figure  S9 ). Altogether, the results suggest that the modulation of energy production is a central aspect of plants growing under chronic radiation exposure. (Figure  S7 ). Structural modifications of ribosomes were also evident (Figure  S8 ), panied by significant changes in RNA polymerase subunits (Figure  S9 ). Altogeth results suggest that the modulation of energy production is a central aspect of growing under chronic radiation exposure. The blue colour of a square reflects downregulation, and red-upregulation. If a biological feature is significant for any of the omics data layers, it is highlighted by a thicker border and a red mark at the top right corner.
After quality control, the pool of cDNA libraries was sequenced using Illumina NovaSeq 6000 (150 bp, pair-ended). FASTQ files were obtained using bcl2fastq v. 2. 20 Conversion Software (Illumina, USA). The format for recording the quality data string was Phred 33. As a result, 1,090,239,068 raw reads were obtained. The transcriptome data analysis was performed following suggestions previously described in  [84]. Quality assessment was performed using FastQC v. 0. 11. 9 and MultiQC v. 1. 10 software. Trimmomatic v. 0. 40 software was used for trimming using the following parameters: ILLUMINACLIP:TruSeq3-PE. fa:2:30:10; HEADCROP:11; LEADING:3; TRAIL-ING:3; MAXINFO:130:1; MINLEN:50. The trimmed reads were deposited in Sequence Read Archive under accession number PRJNA958217 and used for de novo transcriptome assembly using Trinity v.
2. 9. 1 in the Galaxy public server (https://usegalaxy. eu, accessed from 15 September 2022 to 15 March 2023)  [85]. The in silico normalisation of paired and unpaired reads was performed, and unpaired readings were attached to the "left" paired read. Contigs smaller than 200 bp were filtered from further analysis. Assembly completeness was tested in BUSCO v. 2  [86]. The fabales_odb10 data set corresponding to representatives of the Fabaceae family was used for evaluation. The results showed 89. 4% similarity of the assembly with the group of orthologues and a high percentage of duplications (84. 0%), which may indicate the possible tetraploidy of the samples since V. cracca is known to possess either diploid or autotetraploid cytotypes  [87, 88]. The alignment of paired reads to the assembled transcriptome showed a satisfactory result (above 60%).
Open reading frames were predicted for the assembled contigs in TransDecoder v. 5. 5. 0. The search for protein (blastp) and translation (blastx) motifs were performed on sequences obtained from the Swiss-Prot database. Protein domains were identified in HMMER v. 2. 2  [89]  and Pfam  [90]. Predictions of signal peptides, rRNA transcripts, and transmembrane regions were performed in SignalP v. 4. 1  [91] , RNAMMER v. 1. 2 [92] , and TmHMM v. 2. 0, respectively. Functional annotation was carried out in Trinotate v. 3. 2. 2  [93]  and InterProScan 5. 59_91. 0 [94]. As a result, gene annotations of contigs and terms of gene ontology (GO) were obtained. The quantification algorithm kallisto v. 0. 46  [95]  and DESeq2 statistics package  [96]  were used to quantify gene expression. Comparisons were made between contaminated and control plots and two control plots (Babchin-Lomysh) to assess heterogeneity (Figure  S10 ).
For tandem scanning, only ions from z = 2+ to z = 4+ were in charging conditions. The maximum accumulation time for precursor ions was 50 ms, and for fragment ions, it was 150 ms. All measured precursors were dynamically excluded from the tandem MS/MS analysis for 10 s. Proteins were identified using the MaxQuant v. 1. 6. 17. 0 software and the Andromeda search algorithm. Proteins were identified using the UniProt database (UniProt release 2022_02) with species indication. The following search parameters were set: the cleaving enzyme trypsin, the accuracy of mass determination of m/z peptides in MS ± 5 ppm, the accuracy of mass determination in MS/MS spectra ± 0. 01 Da, and the possibility of skipping two trypsin cleavage sites. Proteins were considered reliably identified if at least two peptides were found.
The oxidation of methionine, the acetylation of the N-terminus of the protein, and the modification of cysteine with chloroacetamide were considered possible and mandatory modifications of the peptides. The False Discovery Rate value of no more than 1. 0% was used to validate comparisons of Peptide-Spectrum Matches in the spectra and peptides. Label-free protein quantification was based on iBAQ. The data were filtered to remove possible contaminants and unreliably identified proteins, and only proteins that occurred in two technical repetitions of the MS analysis were retained. We only used proteins with log2FC ≥ |1| for further functional evaluation compared to the control samples.