dataesr/scientific-paragraphs-categorization huggingface dataset:
emarginatus, respectively) sampled in Georgia, Caucasus  [21]. The ftsZ sequence similarity of Bartonella sp. "Iv76" (GenBank: MH544204) to bat-associated bartonellae available on GenBank from Georgia  [20]  was below 85. 5% (data not shown). In Europe, molecular evidence on the occurrence of bat haemoplasmas has hitherto been reported from western countries, i. e. Spain  [23]  and the Netherlands  [11]. Based on blood and fecal samples, respectively, these studies suggested infections of bats with the relevant agents. Haemoplasmas are regarded as predominantly vector-borne  [24]. However, bat-associated haemoplasmas have not hitherto been identified in blood-sucking arthropods. Here, three haemotropic Mycoplasma genotypes have been detected in a tick specimen (I. simplex), collected in Hungary (Table  2 ).
Maximum-likelihood tree of spotted fever group (SFG: encircled with dashed line), Rickettsia felis group (RFG: encircled with dashed line) and other rickettsiae based on the gltA gene. Sequences from this study are highlighted with red color and bold accession numbers. Branch lengths represent the number of substitutions per site inferred according to the scale shown phylogenetically close to "Ca. M. haemohominis", are reported here for the first time from central Europe and from any bat tick.
1 Maximum-likelihood tree of spotted fever group (SFG: encircled with dashed line), Rickettsia felis group (RFG: encircled with dashed line) and other rickettsiae based on the gltA gene. Sequences from this study are highlighted with red color and bold accession numbers. Branch lengths represent the number of substitutions per site inferred according to the scale shown phylogenetically close to "Ca. M. haemohominis", are reported here for the first time from central Europe and from any bat tick.
1c ). Cluster 4 (1393 genes) represents a sharp upregulation of transcripts in pachytene that can be rationalized by their transcription. Cluster 2 (2381 genes) corresponds to genes that are abruptly downregulated as cells enter pachytene; this requires a large component of RNA degradation to rationalize the decreased dosage of these transcripts given that leptotene-zygotene cells are transcriptionally inert. This pattern was also identified using an independent higher resolution single-cell dataset  49  (Fig. S1 ). Given the prominent role for the terminal uridylyltransferases TUT4 and TUT7 in sculpting the maternal transcriptome,  44  we hypothesized that 3′ mRNA uridylation may also contribute to posttranscriptional regulation during spermatogenesis. Both TUT4 and TUT7 are expressed throughout spermatogenesis, despite that the expression of TUT7 is upregulated in round spermatids whereas TUT4 expression is downregulated during meiosis (Fig.
S7 ) identified from the analysis of the spermatogenic single-cell dataset  49  (Fig. S1 ). In essence, TUT4/7 and their uridylation activities are required to degrade transcripts in pachytene spermatocytes, and the failure to do so derails spermatogenesis. Long 3′ UTRs and AU elements are features of TUT4/7-regulated transcripts in the male and female germline Next, we sought to understand which features of TUT4/7regulated transcripts could identify them for degradation in pachytene spermatocytes. Analysis of transcripts revealed that 5′ UTR, CDS and 3′UTR were increased in length in transcripts that are upregulated in Tut4/7 cKO pachytene spermatocytes (Fig. 6a ). 3′ UTRs contain many types of regulatory signals ranging from consensus sequences for RNA binding proteins and miRNA binding sites to the actual length of the UTR itself.
i GAM fit as in h where the frequency of the motif is normalized to the length of the 3′UTR Bioinformatics Microarray data were analyzed using the limma package. 81 ntensities were normalized using the rma function; a linear model was then fitted with lmFit and, differential expression was assessed using the eBayes function. For GV analyses, Tut4/7 cKO and control samples were obtained from ref. 44. For clustering analyses, microarray data was generated in this study for SSCs, leptotene/ zygotene spermatocyte, and round spermatids populations and a published dataset was used for pachytene/diplotene population. 24 o cluster genes according to changes in gene expression across different stages of spermatogenesis, we used the Markov clustering algorithm (MCL) implemented in the BioLayout Express application.
To evaluate the enrichment of upregulated transcripts in Tut4/ 7 cKO vs Tut4/7 CTL pachytene cells in different spermatogenesis expression clusters, we used the hypergeometric test. Ternary plots were generated with the ggtern package. 84 Normalized intensities from Tut4/7 CTL , Tut4/7 cKO and Tut4/7 cAAD Affymetrix experiments were used as input. For gene ontology analysis, the topGO R package was used. 85 Fisher's exact test with the classic algorithm was used to determine enrichment of the Biological Process (BP) ontology. For the piRNA analysis, reads were trimmed from adapter sequences using cutadapt  86  and sequentially mapped to miRNAs, ncRNAs, transposable elements and the genome using Bowtie. 87 o evaluate piRNA levels between different conditions, the frequency of reads from all detected 24 to 36 K-mers was compared.
For miRNA, the frequency of terminal modifications and expression levels was determined as previously described. 44 riefly, adapter trimmed small RNAs were mapped to miRNA precursors  88  allowing for two mismatches with Chimira. 89 The first optimal alignment output from BLASTn was used for multimapped reads. To assess terminal modifications, only nontemplated 3′ nucleotides were regarded as post-transcriptional additions. For expression analysis, both modified and nonmodified species of the same miRNA were grouped. The DESeq2 package was used to normalize read counts and determine differential expression between Tut4/7 CTL and Tut4/7 cKO miRNAs. To identify small RNA signatures in the UTRs of differentially expressed genes between Tut4/7 CTL and Tut4/7 cKO pachytene cells we used Sylamer.
Mass spectrometry (MS) For liquid chromatography-MS/MS analysis, samples were prepared as described. 93 Briefly, ~2 μg of peptides corresponding to 50,000 cells were separated on an EasySpray 50 cm column (Thermo Fisher) coupled to an Orbitrap Fusion Lumos (Thermo Fisher) mass spectrometer operated in DIA mode. Data was processed using Spectronaut 12 software  94  (Biognosys) with project-specific spectral libraries using default settings as described. 95 Condition analysis was performed using t-test on individual log2 ratios of peptides of a protein, resulted P values were corrected for multiple testing using Q value to control FDR level.
ACKNOWLEDGEMENTS We acknowledge the  EMBL GeneCore facility in Heidelberg, Germany  for preparing the microarrays and next-generation sequencing libraries. We also thank  R. Pillai  for sharing his anti-TUT7 antibody. This work was funded by the  Wellcome Trust  [ 106144 ] and the  European Union   H2020  program grant GermAge. Work in the  Wellcome Trust Centre for Cell Biology  is supported by  Wellcome Trust  core funding [092076]. Work in the Porse Lab was supported through a center grant from the  Novo Nordisk Foundation  ( Novo Nordisk Foundation Center for Stem Cell Biology, DanStem ; Grant Number  NNF17CC0027852 ).
Sample-specific barcodes were added by double-indexing  (Kircher et al. , 2012) , using KAPA HiFi Uracil+ (Kapa Biosystems). Samples were then pooled in equimolar ratios and sequenced from both ends in a single flow cell (Illumina HiSeq 4000). As well as shotgun sequencing, samples were also sequenced after enrichment by in-solution hybridization capture (MYcroarray) according to the manufacturer's instructions for degraded samples. RNA baits were transcribed from 80-mer oligonucleotides complementary to the M. leprae TN genome to give an array with 2 tiling density. Sequence data are curated at the European Nucleotide Archive under study accession number PRJEB31393.
Data analysis Raw sequencing data were pre-processed with AdapterRemoval 2. 1  (Schubert et al. , 2016)  to remove adapter sequence remnants, trim low quality bases and merge paired-end reads. Reads of at least 25 bp which formed pairs with at least 11 bp overlap, and non-overlapping pair mates of >25 bp, were retained in separate files. The paired-end reads were then mapped to the M. leprae TN genome with BWA 0. 7. 12  (Li and Durbin, 2009). The alignments were cleaned by soft clipping, sorted based on coordinate with Picard Tools (http://broadinstitute. github. io/picard), and mapped reads with a quality score of at least 20 extracted using SAMtools 0. 1. 19 (Li et al. , 2009). Read duplicates were removed using the MarkDuplicates option in Picard Tools. The mapped reads with duplicates removed were converted to Fasta files and tested by BLAST  (Altschul et al.
Results and discussion Patients with AH undergo deep transcriptional reprogramming. In order to uncover the mechanisms involved in progression to AH in patients with ALD, we first performed a comprehensive analysis of liver RNA sequencing (RNA-seq) data from a large series of patients (N = 92) with different disease stages including normal liver (Normal, N = 10), early alcoholic steatohepatitis (ASH, N = 12), AH with liver failure (AH, N = 18) and a unique set of explants from patients with AH that underwent urgent liver transplantation (exAH, N = 10)  13  (Fig. 1a ). As diseased controls, we included patients with non-alcoholic fatty liver disease (NAFLD, N = 9), chronic hepatitis C (HCV, N = 9) and compensated HCV cirrhosis (CIRR, N = 9). The principal component analysis (PCA) showed patient clustering according to the progressive clinical phenotypes (Fig.
1e ). Top upstream regulators and target genes belonging to these 4 patterns are depicted in Fig. 1f. A detailed gene set enrichment analysis revealed down-regulation of genes related to basic hepatocyte functions (i. e. metabolism of amino acids and lipids, biological oxidations, mitochondrial function and bile acid metabolism), while cell proliferation, extracellular matrix regulation and inflammation related pathways were enriched among up-regulated genes (Supplementary Fig. 3 ). Overall, these changes could explain key features in AH including massive fibrosis, proliferation of immature ductular cells and bilirubinostasis  14, 15. In order to gain insight into the main drivers of gene expression that could result in the development of hepatocellular failure in AH, we analyzed the predicted activity of transcription factors using a complementary approach, by combining the search of transcription factor binding motifs in the promoter of differentially expressed genes (DEG) and by the use of Ingenuity Pathway Analysis (IPA) software to uncover predicted upstream transcription factor activity (see Material and Methods section).
qPCR of l HNF4α-P2 and m HNF4α-P1. n Western blot of HNF4α-P1 and HNF4α-P2 in nuclear extracts. qPCR of HNF4α-P1 targets related to o metabolic functions (PCK1, ALB and F7) and p bile acid synthesis and transport (BSEP, CYP7A1 and CYP27A1). q, r Primary human hepatocytes were silenced with siRNA-HNF4A-P2. q Supernatant was collected 48 h after transfection (n = 3 for each group) and total bile acids were quantified. r Glucose production in P2-silenced primary human hepatocytes. Significance was determined by unpaired, two-tailed Student's t-test in a and c, by Fisher exact probability test in d, e and by twotailed Mann-Whitney U test in g, i, l, m, o, p, q, r: *P < 0. 05. For box-and-whisker plots: perimeters, 25th-75th percentile; midline, median; individual data points are represented treatment, RNA Pol II was found to be bound to a proximal region of intron 1 that contains one FOS::JUN site.
7d ). HNF4α and miR122 dependent genes are only partially overlapping (Fig. 7e ), suggesting that miR122 dysfunction in the   progression from early ASH to AH could involve HNF4α-independent pathways. Finally, we used miRTarBase  41  , a curated database of miRNA targets, to select the top 10 most validated miR122 targets in humans. In patients with AH, 9 of those top targets were upregulated while none of them was found increased in early ASH (Fig. 7f ). Although these new results support a potential role for miR122 in AH, further functional experiments are needed to confirm this hypothesis. N T T G F β 1 T G F β 1 + T G F β R i N T T G F β 1 T G F β 1 + T G F β R i N T T G F β 1 T G F β + T G F β R i N T T G F β 1 T G F β 1 + T G F β R i HNF4α-P1 mRNA (FC) N T T G F β 1 T G F β 1 + T A K 1 i 0.
e LETFs) in patients with AH could be part of a global epigenetic remodeling. In an unbiased fashion, we studied the overall expression analysis of genes encoding epigenetic modulators in patients with AH. For this purpose, we used the EpiFactors database  46. The top 5 hits of each family based on the differential expression comparing normal and AH patients are shown in Fig. 9a. Main genes found markedly deregulated included HDAC 7, HDAC 11, PIWIL4 (MIWI2), NCOR2, ZBTB33, PRDM6, PCGF2, and PHC2. The DNA methyl transferases 1 and 3 A were notably increased in patients with AH (Fig. 9b, c ). We then analyzed the methylation status of over 850,000 loci in normal livers (N = 5) and livers from AH patients (N = 6) and found around 3000 differentially methylated (DM) CpG-containing loci with an absolute change in beta value >0.
9h ). The analysis of soluble upstream regulators revealed TNFα and TGFβ1 involvement in the expression of genes containing hypomethylated CpG (Fig. 9i ). The presence of SNPs in differentially methylated regulatory regions could be involved in dysregulation of the HNF4A locus or the HNF4α-dependent transcriptome. Two annotated CpG islands near the HNF4A locus were identified using the UCSC human genome browser, which contained SNPs from the AH GWAS dataset (rs148377517 and rs13038786), although none of them were associated with the risk of developing severe AH (OR of 0. 62 and 1. 89; P value of 0. 3809 and 0. 24, respectively). We then analyzed the SNP located in Differentially Methylated Regions (DMR) located around the HNF4A locus. Five DMRs around HNF4A locus were identified and 20 SNPs fell within these DMR (Supplementary Data 5a, b).
Next, we analyzed the SNPs within or near HNF4α binding motifs globally found within CpG islands and DMR. A total of 3214 DM CpG loci containing HNF4α binding motifs were found. The SNPs lying±75bp from the locus of a DM CpG locus were extracted from the AH GWAS dataset. In total 505 SNPs fulfilled these criteria. Of these, 18 demonstrated a potential association with the risk of developing AH (P < 0. 05) (Supplementary Data 5c). Of note, the SNP rs942043 lies near E2F3 gene which encodes a transcription factor involved in cell cycle regulation. The variant rs846897 lies near the gene IGSF23, a member of the immunoglobulin superfamily. Finally, we analyzed the DMR around HNF4α binding motif-containing CpG loci. In total, 328 DMRs were extracted and 36 SNPs were found in these regions. Four variants, three of which were in perfect linkage disequilibrium, demonstrated a potential association with disease (P < 0.
To quantify expression from transcriptome mappings we employed RSEM  52. Principal component analysis (PCA) was done using made4 library  53. Analysis of differential expression was performed using the Limma package  54. Cyclic loess normalization was applied, followed by log transformation of the counts per million and mean-variance adjustment using the voom function. The Jonckheere-Terpstratest and Kendall correlation was used to check ordered differences gene among progressive disease stages. To agglomerate gene patterns along disease stages, Short Time-course Expression Miner (STEM) algorithm was used through on-line platform  55. The output of STEM analysis is shown in Fig. 1e , f, Supplementary Fig. 2  and Supplementary Data 1. To uncover biological functions related to gene expression changes, Gene Ontology (GO) enrichment through gene set overlapping computation was done by means of rMATS, using the Canonical Pathways (CP) collection, which includes 1329 gene sets  56.
An overlap p-value to determine statistically significant overlap between the transcription factor target gene dataset and the DEG for each comparison was also calculated using Fisher's Exact Test. For this study, the selected transcription factors (Figs. 2c, d , 5l and 9f) showed an overlap p-value <0. 01 for the comparison between Normal livers and Early ASH and an overlap p-value <0. 005 for the comparison between Early ASH and AH. Opossum calculates two complementary scoring methods to measure the over-representation of transcription factor binding sites: (1) Z-scores measures the change in the relative number of TFBS motifs in the DEG gene set compared with the background set, and (2) Fisher scores based on a one-tailed Fisher exact probability assessing the number of genes with the TFBS motifs in the foreground set vs.
the background set. Since Opossum does not take into account the direction of the expression changes, the top upregulated and the top downregulated genes were scanned separately. Only those TF motifs with positive Z Score were considered. JASPAR database was used as the source of DNA binding profiles (Supplementary Fig. 12 ). HNF4α gene splicing analysis. RNA-seq reads were trimmed to a uniform length of 75 bp using the FastxToolkit (http://hannonlab. cshl. edu/fastx_toolkit/). After read trimming, alignment of RNA-seq reads was performed with the STAR aligner (v2. 5. 2a) against the hg19 human genome. Resulting bam files were indexed with samtools for rMATS  58. Differential expression of splice isoforms was completed using STAR alignment-StringTie-BallGown pipeline as described elsewhere  59.
To identify exon-specific expression, an alternate pipeline was used. First, reads were put through adapter trimming using TrimGalore (https://www. bioinformatics. babraham. ac. uk/ projects/trim_galore/). After the trimming, reads were aligned with the STAR aligner (v2. 5. 2a) against the hg19 genome. The resulting bam files were then put through the DEXSeq R Bioconductor package (v1. 26. 0 for DEXSeq and 3. 3. 1 for R) pipeline. To obtain raw read counts for each exon, we used a standard DEXSeq script for exon counting (dexseq_count. py), with minor modifications. The exons were categorized in the GenCode v19 release. After exon counting, individual R scripts were used to obtain the exon-specific expression profiles. All custom scripts are available upon request. Genomic DNA methylome analysis.
Genomic DNA (gDNA) was extracted from flash-frozen liver tissue with PureLink Genomic DNA Mini Kit (Thermo) and quantified using Nanodrop (Thermo). In total 1 μg of isolated gDNA was bisulfite converted, denatured, fragmented and hybridized to Infinium Methylation Bead Chip, following the manufacturer protocol (Infinium MethylationEPIC kit, Illumina). BeadChips were imaged using an Illumina Scan System and intensity was determined by iScan Control Software (Illumina). Sample intensities were normalized using functional normalization from the minfi package (v1. 24. 0)  60. Probes failing a detection p-value threshold (0. 01) in at least 50% of samples were removed, as were probes identified as containing a SNP with a MAF >0. 05. Differentially methylated probes were identified by applying limma (v3.
For the next-generation sequencing, ChIPseq libraries were prepared from 10 ng of ChIP and input DNAs with the Ovation Ultralow DR Multiplex system (NuGEN). The ChIP-seq libraries were sequenced to 51 base pairs from both ends using the Illumina HiSeq 2000 in the Mayo Clinic Medical Genomics Core. Data were analyzed by the HiChIP pipeline  64. Briefly, reads were aligned to the hg19 genome assembly using BWA and visualized using the Integrative Genomics Viewer (IGV). Mapped reads were post-processed to remove duplicates and pairs of reads mapping to multiple locations. The MACS2 and Sicer algorithm was used for peak-calling in relation to the input DNA. IGV was then used to visualize H3K27ac peak changes on individual genes in this study. Human primary hepatocytes and cell lines. Primary human hepatocytes were purchased from Lonza.
90, associations between principal components and casecontrol status were tested in R. The resultant dataset was phased using ShapeIt v2. r790 and imputed against the 1000 genomes project reference dataset using IMPUTE v2. 3. 2  76. The imputed genotypes were hard-called using a probability threshold of 0. 9 and quality control filters were appliedmissingness <5%, minor allele frequency >1% and deviation from Hardy-Weinberg equilibrium <1 × 10 -6. Associations with case-control status were tested in PLINK v1. 90 specifying the principal components associated with case-control status as covariates. Only autosomal data was analyzed. For the single marker analyses, key transcription factors and related genes were identified through the primary analysis of RNAseq data. Genomic coordinates for the coding regions of these genes, including 3′ and 5′ upstream regions, were obtained from Ensembl Biomart.
Single nucleotide polymorphisms (SNPs) falling within these genetic loci were extracted from the AH study data. Analyses in the AH study data were limited to SNPs with a minor allele frequency >1%. In order to control the false discovery rate a Bonferroni correction was applied based upon 105 independent tests in the directly genotyped dataset. Thus, the study-specific threshold for significance for tests of single SNPs was 0. 0005. For all other tests p < 0. 05 was considered significant. For significantly associated SNPs predicted effects on protein structure were predicted using SIFT  77  and Polyphen  78  , expression quantitative train locus (eQTL) tests were conducted using GTeX  79. For gene-and pathway-based association tests were performed using study summary statistics in MAGMA v1.
Columns were packed with Luna C18 resin (Phenomenex). The mass spectrometer was operated in data-dependent mode, with the Orbitrap operating at 60,000 FWHM and 17,500 FWHM for MS and MS/MS respectively. The fifteen most abundant ions were selected for MS/MS. Data were searched using a local copy of Mascot with the following parameters: Enzyme: Trypsin/P; Database: SwissProt Human. Fixed modification: Carbamidomethyl (C); Variable modifications: Oxidation (M), Acetyl (N-term), Pyro-Glu (N-term Q), Deamidation (N/Q); Mass values: Monoisotopic; Peptide Mass Tolerance: 10ppm; Fragment Mass Tolerance: 0. 02 Da; Max Missed Cleavages: 2. Mascot DAT files were parsed into Scaffold (Proteome Software) for validation, filtering and to create a non-redundant list per sample. Data were filtered using at 1% protein and peptide FDR and requiring at least two unique peptides per protein.
Acknowledgements CLC Genomics Workbench Software licensed through the  Molecular Biology Information Service of the Health Sciences Library System, University of Pittsburgh  was used for data analysis. This work was mainly supported by  NIH/NIAAA  funded Consortia "Integrated approaches for identifying molecular targets in alcoholic hepatitis" InTEAM ( U01AA021908 ) (R. B. , P. M. , P. S-B. ,I. R. , J. Cbl ). This work was supported in part by:  NIH /NIAAA  ( R01AA023781 ),  USA  (C. W. );  Hepacare Project ,  Fundación La Caixa , Spain (M. A. A. , C. B. and  M. U. L) ;  Fond national de la recherche scientifique  ( FNRS J. 0146. 17 ) and  Fond de la recherche scientifique médicale  ( FRSM T. 0217. 18 ), Belgium  (P. S. );  NIH/ NCATS  ( UH3TR000503 ) and  EPA  ( STAR 83573601 ),  USA  (D.
Data availability The RNA-sequencing and Methylomic raw data (Figs. 1, 2, 3a, 4, 7, 9 and Supplementary Figs 2,3 and 5) have been deposited in the Database of Genotypes and Phenotypes (dbGAP) of the National Center for Biotechnology Information (United States National Library of Medicine, Bethesda, MD) under accession number phs001807. v1. p1. The GWAS Summary used to generate Fig. 8  and Supplementary Data 3 and 5 and the ChIPseq peak calling data in Fig. 10  are publicly available (https://doi. org/10. 5281/ zenodo. 3233952).