dataesr/scientific-paragraphs-categorization huggingface dataset:
The up-regulated long non-coding RNAs included a previously described interferon-inducible transcript, NRIR  (Kambara et al, 2014; Fig 3C). Gene ontology analyses showed a strong enrichment for genes involved in immune system processes, in particular response to virus and type I interferon signaling pathway (Dataset EV3). Interestingly, the GO categories found to be enriched among the genes up-regulated in CHC low-ISG patients were generally also over-represented in this second comparison, although the enrichment was much weaker than the one observed for the interferon pathway (Dataset EV3). In agreement with these observations, we found that interferon-stimulated response element (ISRE) motifs and IFN-regulatory factor (IRF) motifs were strongly overrepresented in the promoters of the genes up-regulated in CHC high-ISG samples  (Appendix Fig S3).
The remaining genes that were only differentially expressed in low ISG compared to non-CHC patients generally displayed consistent fold changes in both comparisons, but did not pass the FDR threshold when comparing CHC high-ISG and non-CHC samples (Fig 4B  -D ). In contrast, most of the genes that were uniquely up-regulated or down-regulated in CHC high-ISG patients compared to controls had only weak expression changes in the comparison between CHC low-ISG and control samples (Fig 4B  , E  and F ). These observations are consistent with the presence of a unique expression signature associated with the group of high-ISG patients  (Fig 1A). To further define this expression signature, we directly compared the two groups of CHC samples  (Appendix Fig S4, Dataset EV2). We found numerous genes with significant expression changes, including 176 proteincoding genes and 21 lncRNA candidates  (Appendix Fig S4).
Gene expression patterns associated with HCV infection in vivo and in vitro We next sought to compare the transcriptional responses following HCV infection in vivo and in vitro. A previous genome-wide analysis of differential gene expression in HCV-infected Huh-7. 5 cells revealed that numerous genes involved in cell death, cell cycle, and cell growth/proliferation are mis-regulated following viral infection  (Walters et al, 2009). Although our unsupervised gene ontology analyses did not reveal enrichments for cell cycle-associated categories among the genes differentially expressed in CHC samples (Dataset EV3), we found significant intersections between the sets of genes that are differentially expressed following HCV infection in vivo and in vitro. Specifically, out of 698 protein-coding genes differentially regulated in HCV-infected Huh-7.
5 cells (Materials and Methods), we found that 25 (3. 6%) were differentially expressed between control and CHC low-ISG samples and 48 (6. 9%) were differentially expressed between control and CHC high-ISG samples (FDR < 0. 1, Dataset EV5). The extent of the overlap was significantly higher than expected by chance given the total number of differentially expressed genes, in both cases (chi-square test, P-value < 1e-9). The common differentially regulated genes included several genes associated with cell death and cell cycle, such as UBD, TNFRSF9, BIRC3, JUN, and FOS (Dataset EV5). Differentially expressed genes shared between HCV-infected Huh-7. 5 cells and high-ISG liver biopsies include classical ISGs, such as MX1, ISG15,  and IFIT1 (Dataset EV5  genes defined based on the miRNAs that are predicted to target them.
At all time points, we found strong enrichments in GO categories related to the type I interferon signaling and response to virus pathways among the genes that were up-regulated following pegIFN-a treatment (Dataset EV7), as expected. Consistently, we observed that interferon-stimulated response element (ISRE) motifs and IFNregulatory factor (IRF) motifs were strongly enriched in the promoters of up-regulated protein-coding genes, at all time points (Appendix Fig  S5A-E ). For down-regulated genes, we found significant (FDR < 0. 1) enrichments in functional categories related to small-molecule biosynthetic process and lipid metabolism, but only for the 16-h time point (Dataset EV7).
EMBO Molecular Medicine We next compared the sets of differentially expressed genes observed for the different time points. We observed that almost 60% of the up-regulated protein-coding genes passed our differential expression thresholds at a single time point  (Fig 5B). For up-regulated lncRNAs and other non-coding genes, the time point-specificity was even stronger (Fig  5B ). However, we were able to identify a core set of 59 genes that are up-regulated at four or more time points, including 30 genes up-regulated at all analyzed time points (Dataset EV8). Interestingly, three lncRNAs were found to be significantly up-regulated at all time points (Dataset EV8, Appendix Fig S5F  -H ). However, two of these lncRNAs [including NRIR, a lncRNA previously proposed to act as a negative regulator of interferon response  (Kambara et al, 2014) ] are found downstream of interferon-stimulated protein-coding genes (CMPK2 and BST2), and our RNA-seq data suggest that their induction may be at least in part due to leaky transcription from the upstream gene  (Appendix Fig S5F and G).
Comparison between pegIFN-a treatment and endogenous IFN system activation The endogenous induction of hundreds of ISGs in patients with CHC has little impact on viral replication, whereas treatment of patients with recombinant pegIFN-a achieves high cure rates specifically in patients without an activation of the endogenous IFN system in the liver  (Heim, 2013; Heim & Thimme, 2014). To investigate the molecular differences between these two modes of IFN system activation, we compared the transcriptional response to pegIFN-a treatment with the one elicited by the endogenous IFN system activation. We first extracted the genes that are significantly up-or down-regulated following pegIFN-a treatment, at each time point, and analyzed their expression differences between CHC low-ISG and CHC high-ISG patients (Fig 6, Dataset EV9).
We found that the vast majority of genes that are up-regulated upon pegIFN-a treatment are also induced in high-ISG patients (Fig 6, Dataset EV9). In numerous cases, these differences were also statistically significant (FDR < 10%) in the comparison between low-ISG and high-ISG patients  (Fig 6). However, the level of gene induction was significantly stronger in the pegIFN-a treatment analysis (Fig 6A ), as were the absolute levels of gene expression in the corresponding samples  (Fig 6C). In other words, the ISG expression levels reached after pegIFN-a/ribavirin treatment are higher than the ones observed in patients with high endogenous ISG levels. In contrast, genes that were down-regulated upon pegIFN-a treatment were only rarely down-regulated in high ISG compared to low-ISG patients (Fig 6B  and D ).
However, numerous quantitative differences in the two transcriptional responses can be observed, with stronger ISG induction levels following pegIFN-a/ ribavirin treatment. To further investigate the inability of the endogenous IFN system activation to cure HCV infections, we analyzed the expression patterns of a set of genes proposed to act as antiviral effectors, selected based on their capacity to inhibit HCV replication in human cell lines  (Schoggins et al, 2011; Metz et al, 2012 Metz et al, , 2013;; Materials and Methods). We analyzed the expression of these genes in our samples  (Fig 7, Dataset EV10). We found that their expression levels in pegIFN-a/ribavirin-treated samples and in high-ISG samples were strongly positively correlated (Fig  7A ). However, a number of these candidate ISGs were indeed significantly more stimulated by pegIFN-a than by endogenous IFNs in high-ISG patients (Fig 7A, Dataset EV10).
1). We found 100 such proteincoding genes, including 85 up-regulated and 15 down-regulated genes  (Appendix Fig S8). Note that no genes displayed statistically significant opposite patterns (e. g. , up-regulated by pegIFN-a treatment, but down-regulated in high ISG compared to low-ISG samples). This dataset included some genes with known antiviral functions, such as APOBEC3A and OASL, which is potentially relevant for the inability of patients with high endogenous ISG levels to spontaneously clear the viral infection. Overall, a tendency for up-regulation or down-regulation for these genes was also observed in high-ISG samples compared to low-ISG samples, but at lower levels and with more variability among individuals (Appendix Fig  S8 ).
The estimated decrease in expression levels is likely not due to differential processing of the mature miRNA out of the primary transcript, as consistent differences between baseline and post-treatment biopsies were observed along the entire gene length  (Fig 8B, Appendix Fig S9). Among the other miRNAs whose precursor genes are down-regulated upon pegIFN-a treatment, miR-146a is a striking example, with significant down-regulation (at 10% FDR) for three out of the five analyzed time points  (Fig 8A). Our RNA-seq dataset does not allow us to determine whether the observed down-regulation of miRNA primary genes affects the pool of mature miRNAs in the cells. We thus assessed the expression levels of mature miRNAs using qPCR, for three miRNAs whose primary transcripts were down-regulated upon pegIFN-a treatment: miR-122-5p, miR-146a-5p, and miR-331-3p (Materials and Methods).
For all three miRNAs, this data indicates that the mature miRNAs are indeed down-regulated after pegIFN-a treatment at the 16-h time point (also at the 96-h time point for miR-122-5p and miR-146a-5p; Appendix Fig S10, Dataset EV12). To further test this hypothesis (albeit indirectly), we reasoned that a decrease in mature miRNA expression levels should positively affect the expression of their target genes. We thus analyzed the behavior of predicted miRNA target genes in response to pegIFN-a/ribavirin treatment, at the 16-h   -1 0 1 2 3 4 5   16h   -1 0 1 2 3 4 5   96  274  45   -1 0 1 2 3 4 5   48h   -1 0 1 2 3 4 5   50  34  12   -1 0 1 2 3 4 5   96h   -1 0 1 2 3 4 5   54  57  24   -1 0 1 2 3 4 5   144h   -1 0 1 2 3 4   A, B Differential expression patterns of the genes that are significantly up-regulated (A) or down-regulated (B) (FDR < 0.
We show 79 miRNA families with at least 100 conserved target genes. As a control, we show the median fold expression change for all genes predicted to be targets of any liver-expressed miRNAs or of genes not predicted to be targeted by these miRNAs (blue dots). The horizontal bars represent 95% confidence intervals of the median, constructed with a bootstrap resampling approach (Materials and Methods). Only miRNAs with high expression in normal or HCV-infected liver were analyzed  (Hou et al, 2011). Evolutionarily conserved miRNA target predictions were extracted from TargetScan v7. 1  (Agarwal et al, 2015; Materials and Methods). miRNAs highlighted in red correspond to primary miRNA transcripts significantly down-regulated at the 16-h time point; miRNAs highlighted in orange correspond to primary miRNA transcripts significantly down-regulated at other time points.
Discussion Hepatitis C virus (HCV) is one of the most widely used model systems to investigate host-virus interactions. The adaptive changes to HCV infections have been studied extensively in cell culture systems. For example, a comprehensive analysis of gene expression in HCV-infected Huh-7. 5 cells reported over 800 genes with > 2-fold changes in expression  (Walters et al, 2009). Adaptive changes were described in lipid biosynthetic pathways, endoplasmatic reticulum stress response, autophagy, and cell cycle regulation. However, due to the difficulties inherent to human liver tissue sampling, few of these findings have been validated in vivo. To investigate whether the observations obtained in cell culture systems can be confirmed in the human liver, we have collected and analyzed transcriptome data from liver biopsies derived from control and chronic hepatitis C patients, in the absence of and during treatment with pegIFN-a/ribavirin.
5 cells and in liver samples from patients without IFN system activation. This common gene set included several cell cycle-associated genes, such as UBD, ITIH1, and BIRC3 (Dataset EV5;  Walters et al, 2009). Thus, we were able to identify a significant number of genes that respond to HCV infection both in vivo and in vitro. How might we explain the differences in HCV-induced expression patterns in vivo and in vitro? It has been estimated that the number of HCV virions per infected cell is between 1 and 8 in the human liver, but can be as high as 500-1,000 in the Huh-7. 5 cell culture model  (Stiffler et al, 2009). It is thus conceivable that some of changes described in cell culture are due to the high viral concentration and do not occur in vivo. However, it is also possible that our analysis was underpowered to detect HCV-induced cell-intrinsic changes in gene expression.
However, most of these "additional" genes were up-regulated in high-ISG samples as well, albeit to a lesser degree and therefore not passing the significance threshold. Such quantitative differences were also observed for a number of candidate anti-HCV effector ISGs identified in large screens  (Schoggins et al, 2011; Metz et al, 2013). Most of them were significantly stronger induced by pegIFN-a compared to endogenous IFNs, but their expression levels were highly positively correlated between samples treated with pegIFN-a/ ribavirin and samples with high endogenous ISG levels  (Fig 7, Dataset EV10). We present six candidate antiviral effectors [IRF1, IRF2, IRF7, IRF9, OASL, IFITM3, reported in both publications cited above  (Schoggins et al, 2011; Metz et al, 2013) ] that do not differ significantly between low-ISG and high-ISG patients.
RNA-seq data generation Total RNA was extracted from fresh-frozen bulk liver biopsy tissue using TRIzol reagent (Invitrogen) and subsequently subjected to DNase treatment using DNA-free TM DNA Removal Kit (Ambion) according to manufacturer's instructions. RNA concentration was determined using NanoDrop 2000 spectrophotometer (Thermo Scientific), and RNA quality/integrity was assessed with an Agilent 2100 BioAnalyzer using RNA 6000 Nano Kit (Agilent Technologies). All 46 RNA samples included in this study had RNA integrity number (RIN) values of > 7 (7. 1-9. 4; median 8. 9), with 42 out of 46 samples having RIN values ≥ 8. We generated RNA sequencing (RNA-seq) data using the Illumina TruSeq Stranded mRNA protocol,.
RNA-seq data processing The RNA-seq reads were trimmed to remove 3 0 end adapter sequences, keeping a maximum read length of 81 bp. The reads were aligned on the hg38 primary assembly (excluding patches and haplotypic sequences) of the human genome, downloaded from the Ensembl  (Cunningham et al, 2015)  database release 76. The alignments were done using TopHat  (Kim et al, 2013)  release 2. 0. 10 and Bowtie  (Langmead & Salzberg, 2012)  release 2. 1. 0. We allowed intron sizes between 40 bp and 1 million bp for spliced read alignments, with a minimum anchor size of 8 bp and a maximum of one mismatch for each aligned read segment.
Gene model reconstruction with RNA-seq We used Cufflinks  (Trapnell et al, 2010)  release 2. 2. 1 to reconstruct de novo gene models from TopHat unique read alignments. Reads with multiple reported alignments were excluded from the dataset prior to de novo reconstruction. We kept isoforms with a minimum frequency above 0. 05. Intra-intronic transcripts (corresponding to retained introns or unspliced pre-mRNAs) were kept if their frequency was above 0. 25. We allowed intron sizes between 40 bp and 500,000 bp. Neighboring transcribed regions were collapsed if they were closer than 40 bp. We performed the gene model reconstruction separately for each RNA-seq sample and then merged them into a single set of gene models using the cuffmerge tool in Cufflinks. We note that de novo reconstructed gene models may be fragmented, meaning that a single locus can be split into several predicted gene models, in particular for low expression levels.
Long non-coding RNA dataset We used genomic annotations from the Ensembl  (Cunningham et al, 2015)  database release 82 as a basis for our analyses, to which we added de novo gene models reconstructed with Cufflinks. We determined the protein-coding potential of de novo gene models based on the codon substitution frequency (CSF) score approach  (Lin et al, 2007 (Lin et al, , 2011) )  and on sequence similarity with known protein databases  [SwissProt (UniProt, 2015) ] and protein domains [Pfam-A  (Finn et al, 2014) ], as previously described  (Necsulea et al, 2014). For the CSF approach, to determine the codon substitution frequencies expected for coding and non-coding regions, we aligned Ensembl-annotated protein-coding sequences and intronic regions, for 9,000 1-1 orthologous gene families for 42 vertebrate species extracted from the Ensembl Compara database  (Vilella et al, 2009).
In addition, we searched for sequence similarity between exonic sequences and known proteins from and protein domains from the SwissProt (UniProt, 2015) and Pfam-A  (Finn et al, 2014)  databases, using blastx  (Altschul et al, 1990). We kept SwissProt proteins with high confidence annotations (protein existence score 1, 2, or 3). We retained blastx hits with e-values below 0. 001. Gene models were classified as potentially proteincoding if they had significant blastx hits with SwissProt or Pfam-A protein sequences over at least 90 bp or 5% of their exonic length. De novo reconstructed gene models classified as non-coding with both approaches were kept for further long non-coding RNA analyses. To avoid annotating alternative untranslated regions or introns of protein-coding genes as independent long non-coding RNAs, we further filtered the set of de novo-predicted lncRNAs based on their distance to Ensembl-annotated protein-coding genes.
We retained lncRNA candidates that had no sense overlap with Ensembl-annotated protein-coding genes and that were at least 10 kilobases (kb) away from protein-coding gene coordinates. Sense intronic overlaps with other non-coding transcripts were accepted. We discarded loci with an exonic length below 200 bp (for multi-exonic loci) or 500 bp (for mono-exonic loci). We further excluded loci overlapping with RNA repeats or with UCSC-annotated retro-transposed gene copies over more than 10% of their length. In addition to de novo-predicted lncRNA candidates, we analyzed Ensembl-annotated transcripts corresponding to gene biotypes "lincRNA", "processed_transcript", or "antisense". For both Ensembl-annotated and de novo-predicted lncRNAs, we further required support from at least 50 uniquely mapped RNA-seq reads, across all RNA-seq samples pooled together.
Differential gene expression We assessed differential gene expression using methods in the DESeq2  (Love et al, 2014)   For the comparison between normal liver, CHC high-ISG and CHC low-ISG patients, we further selected the samples based on their METAVIR score for inflammation and fibrosis (A1/ F1, A1/F2, and A2/F2 samples were kept for further analyses). We also excluded one sample (identifier A707), which had high expression levels for inflammatory markers despite its A2/F2 METAVIR classification. The sets of patients analyzed for each test are provided in Dataset EV1. P-values were corrected for multiple testing using the Benjamini-Hochberg method, as implemented in DESeq2. Note that for some genes, the resulting false discovery rate (FDR) is set to "NA", if the expression levels are too low or too variable to ensure reliable differential expression estimates  (Love et al, 2014).
Resampling to control for outlier effects in differential expression analyses The level of endogenous IFN system activation can vary among patients classified as high ISG (Fig  1 ). In addition, our dataset included different number of patients in the control (6), low-ISG (7), and high-ISG (7) categories, which may affect the statistical power of the analysis. To avoid outlier effects in our differential expression analyses for these comparisons (contrasting non-CHC, low-ISG, and high-ISG patients), we resampled six out seven patients for the low-ISG and high-ISG categories and we performed differential expression analyses with the reduced number of samples. All possible patient combinations were tested separately. We then selected gene expression differences based on their average false discovery rates (FDR) and average log2-fold expression change level across all resampling replicates.
The analyses presented in the manuscript correspond to this resampling correction. Similar conclusions were reached when differential expression analyses were performed with all samples (results provided in Dataset EV2). For these comparisons, we considered that genes are differentially expressed if the FDR was below 0. 1 and the absolute fold change above 1. 5. We voluntarily chose slightly less restrictive FDR and fold change thresholds for these analyses than for the pegIFN-a treatment analysis, to avoid a loss of sensitivity associated with this resampling procedure.
Gene ontology enrichment We performed gene ontology enrichment analyses using the GOrilla webserver  (Eden et al, 2009). We contrasted a focus set of genes (e. g. genes that were up-regulated in a set of patients or at one time point during treatment) with a background set of genes expressed at in the same samples. We set the false discovery rate for the GO enrichment analysis at 0. 1. Only protein-coding genes were used for this analysis. We also downloaded GO associations for each Ensembl-annotated gene from the Ensembl 82 database, using BioMart. For the principal component analysis (PCA) presented in Appendix Fig S1 , we selected genes associated with the categories: "type I interferon production", "response to type I interferon", "response to interferon-gamma".
microRNA expression To evaluate the involvement of miRNA regulation in the gene expression differences observed between patient categories or following treatment, we first obtained a dataset of miRNAs expressed in the relevant samples. We downloaded miRNA expression values evaluated with RNA sequencing and measured as TPM (tags per million mapped reads) from a previous publication  (Hou et al, 2011). This dataset included expression levels for normal liver, hepatitis B virus (HBV)-infected and HCV-infected liver, and hepatocellular carcinoma from HBV and HCV-infected livers. For comparability with our samples, we retained only expression levels from normal liver and HCV-infected, non-carcinoma samples. We summed the TPM values across all relevant samples and ranked miRNAs based on their combined expression values.
microRNA target prediction We used two sources for miRNA target predictions. First, we extracted computational target predictions from the TargetScan v7. 1 human dataset  (Agarwal et al, 2015). To enrich in reliable target predictions, we kept only gene-miRNA associations that had a cumulative context++ score  (Agarwal et al, 2015)  below 0. We analyzed separately miRNA-target gene relationships for which at least one conserved binding site was predicted. Second, we used target predictions determined experimentally with HITS-CLIP in Huh7 cells  (Luna et al, 2015). This dataset included binding sites for 50 miRNAs expressed in Huh7 cells. We filtered this dataset to keep only binding sites found in the 3 0 UTR region, and we excluded 6mer binding sites. For the TargetScan dataset, we analyzed only miRNAs that were expressed in normal and HCVinfected liver, as defined above.
All data are provided in Dataset EV4. For the analysis of miRNA targets and their expression patterns, we always exclusively analyzed genes that are expressed at an RPKM level of at least 1 in at least one of the relevant samples. For the analysis presented in Fig  8 , we computed confidence intervals for the median expression fold change (following pegIFN-a/ribavirin treatment at the 16-h time point) with a bootstrap approach, by resampling the same number of genes 100 times with replacement. We then extracted the 2. 5 and 97. 5% quantiles of the resulting distribution, which are displayed as confidence intervals in Fig 8.