Gene Expression Signatures Associated With Survival Times of Pediatric Patients With Biliary Atresia Identify Potential Therapeutic Agents

Gastroenterology 2019;-:1–15 Gene Expression Signatures Associated With Survival Times of Pediatric Patients With Biliary Atresia Identify Potential ...

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Gastroenterology 2019;-:1–15

Gene Expression Signatures Associated With Survival Times of Pediatric Patients With Biliary Atresia Identify Potential Therapeutic Agents Q13

Zhenhua Luo,1,* Pranavkumar Shivakumar,1,* Reena Mourya,1 Sridevi Gutta,1 and Jorge A. Bezerra1 1

Division of Gastroenterology, Hepatology and Nutrition of Cincinnati Children’s Hospital Medical Center and the Department of Pediatrics of the University of Cincinnati College of Medicine, Cincinnati, Ohio

BACKGROUND & AIMS: Little is known about the factors that affect outcomes of patients with biliary atresia and there are no medical therapies that increase biliary drainage. METHODS: Liver biopsies and clinical data were obtained from infants with cholestasis and from children without liver disease (controls); messenger RNA (mRNA) was isolated, randomly assigned to discovery (n ¼ 121) and validation sets (n ¼ 50), and analyzed by RNA sequencing. Using the Superpc R package followed by Cox regression analysis, we sought to identify gene expression proﬁles that correlated with survival without liver transplantation at 24 months of age. We also searched for combinations of gene expression patterns, clinical factors, and laboratory results obtained at diagnosis and at 1 and 3 months after surgery that associated with transplantfree survival for 24 months of age. We induced biliary atresia in BALB/c mice by intraperitoneal administration of Rhesus rotavirus type A. Mice were given injections of the antioxidants N-acetyl-cysteine (NAC) or manganese (III) tetrakis-(4benzoic acid)porphyrin. Blood and liver tissues were collected and analyzed by histology and immunohistochemistry. RESULTS: We identiﬁed a gene expression pattern of 14 mRNAs associated with shorter vs longer survival times in the discovery and validation sets (P < .001). This gene expression signature, combined with level of bilirubin 3 months after hepatoportoenterostomy, identiﬁed children who survived for

24 months with an area under the curve value of 0.948 in the discovery set and 0.813 in the validation set (P < .001). Computer models correlated a cirrhosis-associated transcriptome with decreased times of transplant-free survival; this transcriptome included activation of genes that regulate the extracellular matrix and numbers of activated stellate cells and portal ﬁbroblasts. Many mRNAs expressed at high levels in liver tissues from patients with 2-year transplant-free survival had enriched scores for glutathione metabolism. Among mice with biliary atresia given injections of antioxidants, only NAC reduced histologic features of liver damage and serum levels of aminotransferase, gamma-glutamyl transferase, and bilirubin. NAC also reduced bile duct obstruction and liver ﬁbrosis and increased survival times. CONCLUSIONS: In studies of liver tissues from infants with cholestasis, we identiﬁed a 14-gene expression pattern that associated with transplant-free survival for 2 years. mRNAs encoding proteins that regulate ﬁbrosis genes were increased in liver tissues from infants who did not survive for 2 years, whereas mRNAs that encoded proteins that regulate glutathione metabolism were increased in infants who survived for 2 years. NAC reduced liver injury and ﬁbrosis in mice with biliary atresia, and increased survival times. Agents such as NAC that promote glutathione metabolism might be developed for treatment of biliary atresia.

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Keywords: Chronic Liver Disease; Cholestasis; Biomarker; Risk Factor.

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WHAT YOU NEED TO KNOW BACKGROUND AND CONTEXT

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iliary atresia (BA), the most common neonatal cholangiopathy, results from a ﬁbro-inﬂammatory obstruction of the extrahepatic bile duct and induces a rapidly progressive hepatic ﬁbrosis.1 Despite advances in understanding key factors relevant to etiology and pathogenesis of disease,2 the only treatment is the hepatoportoenterostomy (HPE), which may restore bile drainage in some patients, but does not stop the progressive ﬁbrosis that is typical of BA.3 In studies designed to identify triggers of biliary injury using mouse and zebraﬁsh models, viruses and environmental toxins have been shown to injure cholangiocytes and activate the innate and adaptive immune systems.1,4 For example, dendritic cells and natural killer (NK) cells populate the livers of infants at the time of diagnosis and are directly implicated in the epithelial breach and the promotion of an adaptive inﬂammatory response that ampliﬁes the cholangiocyte injury and obstructs the duct lumen.5–8 However, the use of corticosteroids to suppress the inﬂammatory response after HPE did not improve biliary drainage in a double-blind, placebo-controlled study,9 but appeared to reduce the serum total bilirubin if given to younger infants suggesting that stages of disease may be an important factor in treatment response.10 Despite the association of young age with treatment response, age alone does not predict clinical outcome nor correlates reliably with liver histological scoring or transcriptional proﬁles.11–13 With substantial variability in clinical course, the discovery of predictive biomarkers at the time of diagnosis would be invaluable to the ﬁeld by enabling the customization of treatment protocols and the stratiﬁcation of patients into clinical trials. In search of predictive biomarkers, we analyzed a comprehensive platform containing clinical and laboratory data and the hepatic transcriptional proﬁles at diagnosis. Our analyses identiﬁed a 14-gene signature before surgical intervention that best predicted low survival with the native liver at 2 years of age in a discovery cohort, which was reproduced in a validation cohort. This signature was linked to an abundance of activated hepatic stellate cells and portal ﬁbroblasts, and to a transcriptional evidence of oxidative stress. Testing the relevance of this molecular signature in neonatal mouse models of BA and ﬁbrosis, we found that the use of the antioxidant N-acetyl-cysteine (NAC) improved the clinical outcome and suppressed biliary injury and liver ﬁbrosis.

Biliary atresia is a severe obstructive cholangiopathy of neonates, with variable response to surgical treatment. Without effective medical treatment, the liver disease rapidly progresses to cirrhosis. NEW FINDINGS The authors identiﬁed a hepatic expression signature of 14 genes at diagnosis with a high predictive performance for survival with the native liver at 2 years of age. LIMITATIONS The performance of the 14-gene signature in predicting the 2-year survival requires a prospective validation in new cohorts. IMPACT The 14-gene signature at diagnosis may be a valuable tool to guide enrollment of subjects into clinical trials and forms a rationale for an interventional study to assess the efﬁcacy of n-acetyl-cysteine in improving clinical outcome. approved by the human research review boards of all participating institutions. The diagnosis of BA was deﬁned by an abnormal intraoperative cholangiogram and histological demonstration of obstruction of extrahepatic bile ducts. Liver biopsies were obtained at the time of HPE/diagnosis. Follow-up visits for clinical data collection occurred at 1 and 3 months after HPE and at the age of 24 months. Healthy controls consisted of liver biopsy samples obtained from 7 deceased-donor children aged 22 to 42 months as described previously.14

RNA Sequencing and Supervised Principal Components for Cox Regression Details of RNA sequencing (RNAseq) analyses are described in the Supplementary Methods. All RNAseq data have been deposited to the Gene Expression Omnibus database under accession number GSE122340. The correlation between patient short-term survival data (survival with native liver at 24 months of age) and the normalized gene expression data were subjected to supervised principal component analysis using the supervised principal components for Cox regression (Superpc) R package, followed by Cox regression analysis (Superpc Cox) as reported previously.15 Steps for Superpc Cox are described in the Supplementary Methods. We ﬁrst trained the Superpc Cox * Authors share co-ﬁrst authorship.

Materials and Methods Patients Liver biopsies and clinical data were obtained from infants with cholestasis enrolled into a prospective study (ClinicalTrials.gov Identiﬁer: NCT00061828) of the National Institute of Diabetes and Digestive and Kidney Diseases–funded Childhood Liver Disease Research Network (www. childrennetwork.org) and from infants evaluated at Cincinnati Children’s Hospital Medical Center. The study protocols were

Abbreviations used in this paper: aHSCs, activated hepatic stellate cells; ALT, alanine aminotransferase; aPFs, activated portal ﬁbroblasts; a-SMA, a-smooth muscle actin; BA, biliary atresia; ffu, ﬂuorescence forming units; GGT, gamma-glutamyl transferase; HPE, hepatoportoenterostomy; MnTBAP, manganese (III) tetrakis-(4-benzoic acid)porphyrin; NAC, Nacetyl-cysteine; NK cells, natural killer cells; PBS, phosphate-buffered saline; RNAseq, RNA sequencing; RRV, Rhesus rotavirus; Superpc, supervised principal components for Cox regression; ssGSEA, single sample gene set enrichment analysis; TB, total bilirubin. © 2019 by the AGA Institute 0016-5085/$36.00 https://doi.org/10.1053/j.gastro.2019.06.017

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regression model on the discovery cohort (n ¼ 121) and tested the reproducibility of the predictor of survival in the validation cohort (n ¼ 50). By ranking the patients into low survival and high survival groups based on the predicted score in a descending manner, study subjects were dichotomized into 2 groups, with those with relatively higher predicted scores being assigned to the low survival group. To examine if the transition to nonsurvival delineates a transcriptional trajectory, we performed principal component analysis to cluster all 171 patients with BA and 7 healthy controls as described previously.16

Prognostic Index We developed a prognostic index by the linear combination of 2 parameters (the 14-gene prognostic signature and total bilirubin at 3 months after HPE) weighted with regression coefﬁcients from the multivariate Cox regression model in the discovery cohort using the following formula to generate an index: Index ¼ 0:6985 14 gene signature predicted score þ 0:1864 level of total bilirubin at 3 months Applying this formula to the data in the discovery cohort, subjects were ranked based on the index in a descending manner and dichotomized into high index (low survival) and low index (high survival) groups, which again dichotomized study subjects into 2 survival groups. This prognostic index was applied to the validation cohort following the same approach.

Transcriptional Overlap With Hepatopathies, Pathway Enrichment Analysis, and Estimation of Cell Abundance Analytical methods are described in the Supplementary Methods. The detailed information of studies for liver diseases and their animal models are summarized in Supplementary Table 1. Details are described in the Supplementary Methods.

Standard and Modiﬁed Neonatal Mouse Model of BA and Antioxidant Treatment To directly test the functional relationship among liver ﬁbrosis, antioxidant-related genes, and survival, we performed experiments in standard and modiﬁed mouse models of BA. For the standard mouse model, BA is induced by the intraperitoneal administration of 1.5 106 ﬂuorescence forming units (ffu) Rhesus rotavirus type A (RRV) to BALB/c mice in day 1 of life. The modiﬁcation consisted of a delay in intraperitoneal inoculation of 1.875 106 ffu of RRV until day 3 of life to produce an ongoing hepatic injury, hyperbilirubinemia, and ﬁbrosis. Detailed testing the effect of antioxidants in this model and phenotyping are described in the Supplementary Methods.

Histopathology, Immunostaining, Colorimetric Assays, and Real-Time Polymerase Chain Reaction Details are described in the Supplementary Methods. Primer sequences are listed in Supplementary Table 2.

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Statistical Analyses Detailed statistical analyses Supplementary Methods.

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Results The Hepatic Transcriptome Contains a 14-Gene Signature Identifying Patients With High and Low Survival To explore the relationship between gene expression proﬁling and patient survival with the native liver at 2 years of age, we developed an analytic pipeline that started with the random assignment of 171 human liver biopsies collected at the time of diagnosis into discovery (n ¼ 121) and validation (n ¼ 50) cohorts (Figure 1A). Analyses of clinical variables of both cohorts showed no differences in sex, age, biochemical indicators of liver injury or cholestasis (serum levels of total bilirubin [TB], aspartate aminotransferase, alanine aminotransferase [ALT], gamma-glutamyl transferase [GGT]), platelet count, presence of syndromic BA, histological inﬂammation, ﬁbrosis, or survival (Supplementary Table 3 and Supplementary Figure 1). To identify prognostic gene signatures, we performed Superpc and survival analyses using liver RNAseq data from the discovery cohort. We identiﬁed 14 genes associated with survival in the discovery cohort, which was reproducible when we applied the same analytical approach to RNAseq from the validation cohort (Figure 1B and C and Supplementary Table 4). Using the 14-gene signature, the patients were dichotomized into 2 groups based on the predicted scores, with signiﬁcant survival differences between 2 groups both in the discovery (Log-rank P < 0.0001; Figure 1D) and validation cohorts (Log-rank P ¼ .0082; Figure 1E). The median length of follow-up is 9 and 8 months for low survival groups in discovery and validation cohorts, respectively, and 23 months for high survival groups in both cohorts. In the low survival groups, only 33% (discovery cohort) and 24% (validation cohort) were alive with the native liver, in contrast with 70% (discovery cohort) and 60% (validation cohort) in the high survival groups (Figure 1D and E). Applying principal component analysis clustering to these groups and a group of healthy controls, we observed that healthy subjects were positioned closer to those with high survival, with a similar separation pattern reproduced in the validation cohorts (Figure 1F and G). Analyses of the clinical parameters between these groups showed differences in age and histological ﬁbrosis, with younger subjects in the high survival group in the discovery (median [25%–75%] days: 52 [40–73] vs 68 [55–78], P ¼ .0001) and validation cohorts (50 [37–71] vs 59 [55–82], P ¼ .034), and relatively higher degree of ﬁbrosis in the low survival group in the discovery cohort (P ¼ .0039) and validation cohorts (P ¼ .016; Supplementary Table 5). These data uncovered a 14-gene signature at the time of diagnosis that successfully predicted the 2-year survival of patients with BA, with an initial association among survival, age, and ﬁbrosis at diagnosis.

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Figure 1. A 14-gene signature that predicts patient’s survival at 2 years of age. (A) The analytic pipeline that started with the random assignment of 171 human liver biopsies collected at the time of diagnosis into discovery (n ¼ 121) and validation (n ¼ 50) cohorts, then supervised principal components for Cox regression was applied to dichotomize patients into low and high survival groups. Heatmaps in (B) and (C) show the expression of 14 genes in discovery and validation cohorts, respectively (levels of gene expression are shown as color variation from red [high] to blue [low]). Euclidean distance was used in hierarchical clustering of genes. Graphs in (D) and (E) show Kaplan-Meier plots dichotomizing individual cohorts into 2 groups based on high or low survival based on predicted scores generated by Superpc Cox regression. Log-rank test was used to compare the survival distributions of 2 groups. Graphs in (F) and (G) show principal component analysis clustering of all 171 patients with BA and 7 healthy controls.

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The 14-Gene Signature and TB After HPE Form an Index With High Predictive Characteristics To directly examine how the 14-gene signature performs as a predictor of outcome, we applied uni- and multivariate Cox regression analyses to a platform of data elements containing the signature, age at diagnosis, and other clinical parameters obtained at diagnosis and at 1 and 3 months after HPE. In a univariate analysis, the 14gene signature at diagnosis, high serum TB, aspartate aminotransferase, and ALT at 1 and/or 3 months after HPE were associated with decreased survival (Supplementary Tables 6 and 7). However, applying multivariate analyses to the discovery cohort, only the 14gene signature (hazard ratio 2.2; 95% conﬁdence interval 1.4–3.6; P ¼ .002) and the persistent elevation of TB at 3 months after HPE (hazard ratio 1.2; 95% conﬁdence interval 1.12–1.29; P < .0001) were relative risk factors for low survival (Table 1), with similar ﬁndings in the validation cohort (Supplementary Table 7).

We next developed a prognostic index by linearly combining the 14-gene signature and TB at 3 months weighed with regression coefﬁcients in the discovery cohort (Figure 2A). In this approach, a higher index indicates lower survival. When generated for the discovery cohort, the prognostic index dichotomized the patients into 2 groups, with a low survival of 20% in the group with a high index and 90% with a low index (Log-rank P < .0001, Figure 2B), which was reproduced in the validation cohort (survival of 16% and 70%, respectively; Log-rank P ¼ .0004; Figure 2C). Testing the performance of the prognostic index to predict a 2-year survival generated an area under the curve of 0.948 in the discovery and 0.813 in the validation cohorts (Figure 2D and E), with corresponding model c-statistic of 0.862 and 0.763, sensitivity of 0.886 and 0.825, speciﬁcity of 0.867 and 0.821, positive predictive value of 0.822 and 0.801, and negative predictive value of 0.907 and 0.811, respectively. A comparative analysis of against the 14-gene signature and TB showed that TB 3 months after HPE

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performs better than the 14-gene signature and that the prognostic index outperforms the 14-gene signature and TB independently in all parameters analyzed (Supplementary Table 8). These data suggested that a prognostic index that combines the signature at diagnosis and the TB as a surrogate of surgical response has a better accuracy in predicting survival.

The Hepatic Expression Signature Overlaps With Human and Experimental Fibrosis Searching for prominent biological processes linked to the 2-year outcome, we ﬁrst compared the expression levels of individual genes in the high survival group with those in the low survival group and a group of healthy controls. There was a gradation of expression levels, with the expression for all 14 genes peaking in the low survival group, followed by lower levels in the high survival group, and the lowest level in the control group (Figure 2F). This suggested that the induction of gene expression in the liver occurs in all patients with BA, but it has the highest degree of expression in the low survival group that more directly relates to the poor outcome. Next, to obtain insight into predominant biological processes related to outcome prediction, we mined the entire transcriptome platform to identify genes that were differentially expressed between the low and high survival groups (Figure 3A). Setting a threshold of 1.5-fold change and a Benjamini-Hochberg adjusted P < .05, we identiﬁed 820 upregulated and 123 downregulated genes in the discovery cohort and 1218 upregulated and 46 downregulated genes in the validation cohort (Figure 3B and C). Investigating whether this comprehensive gene panel may be speciﬁc for BA or contain expression signatures of pathogenic mechanisms shared by other diseases, we performed pairwise overlapping analyses between differentially expressed genes and a variety of gene signatures published for diseased human livers and livers and extrahepatic bile ducts for animal models of liver disease (Supplementary Table 1). These gene signatures were derived by comparing disease livers and liver/extrahepatic bile ducts with healthy controls. The most consistent ﬁnding was a signiﬁcant overlap of upregulated genes in the low survival group with those in human cirrhotic livers for the discovery and validation cohorts, followed by hepatocellular carcinoma, nonalcoholic steatohepatitis, a ﬁbrosis signature previously published for livers of infants with BA, primary biliary cholangitis, and primary sclerosing cholangitis (Figure 3D).11 Among the experimental models, the most signiﬁcant overlap was with gene signatures of chronic liver injury induced by bile duct ligation, diethyl-nitrosamine, and carbon tetrachloride (Figure 3E). In the analysis of extrahepatic bile ducts from the murine model of rotavirusinduced experimental BA,17 there was an inverse relationship with the time of inﬂammatory obstruction (7 days after virus infection). Based on these ﬁndings, we applied the same strategy to cells known to be the main producers of extracellular matrix and found signiﬁcant transcriptional overlaps with activated hepatic stellate cells and portal

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ﬁbroblasts (Figure 3F).18,19 Combined, these data pointed to a close relationship between upregulated genes in the low survival group and hepatic ﬁbrosis. They also provided an initial link to the activation of cells known to produce extracellular matrix.

Transcriptional Modeling Identiﬁes the Expression and Cellular Source of Extracellular Matrix To investigate the molecular basis for the relationship between the low-survival signature and ﬁbrosis in other diseases and experimental models, we applied pathway enrichment analyses to the upregulated genes in the low survival group (Figure 4A). Nine of the top 10 most statistically signiﬁcant pathways related to the expression of extracellular matrix, including glycoproteins, collagen, and proteoglycans (Figure 4B, Supplementary Figure 2A). Based on the ﬁndings of a transcriptional overlap with activated hepatic stellate cells (aHSCs) and activated portal ﬁbroblasts (aPFs) discussed previously and on the major roles of these cells as effectors of liver ﬁbrosis,20,21 we estimated their relative abundance by single sample gene set enrichment analysis (ssGSEA) using gene expression enriched for individual cell types as described previously.22 Transcriptional estimation of aHSCs, aPFs, and cholangiocytes showed a gradation of cellular abundance, with a peak in the low survival group, a decrease in the high survival group, and the lowest in the healthy group (Figure 4C–E). These transcriptional estimates were validated in a set of liver biopsy samples immunostained to detect a-smooth muscle actin (aSMA) and cytokeratin 7 (Supplementary Figure 3A–G) and by the expression of the gene markers ACTA2 (for a-SMA) and KRT7 and KRT19 (for cytokeratins 7 and 19; Supplementary Figure 3H and I). These data suggested that these cells may contribute to the prominent ﬁbrosis and lower survival in BA.23 Based on the increasing recognition that hepatic immune cells play a role as inducers of ﬁbrogenesis and in the pathogenesis of BA,1,24,25 we applied ssGSEA to quantify the abundance of innate and adaptive immune cell types using gene signatures from 2 independent studies (Bindea et al26 and Charoentong et al22). The abundance of liver CD8-T and NK cells was greater in the low survival group in the discovery and validation cohorts (Figure 4F and G using gene signatures reported by Bindea et al26; data using signatures from Charoentong et al22 are shown in Supplementary Figure 4A and B). The same strategy identiﬁed differences in dendritic cells, eosinophils, neutrophils, and subtypes of T cells, but they could not be reproduced in the validation cohort or when we compared the signatures using the 2 published methods (Supplementary Figure 5). Using Pearson correlation to explore the potential functional relationship between CD8-T and NK cells with aHSCs, aPFs, and cholangiocytes, we found the best correlation with NK cells (Figure 4H–J and Supplementary Figure 4C–E), whereas the correlation with CD8-T cells was low or not reproducible (Supplementary Figure 4F–K). Altogether, these data provide evidence that the low survival signature is linked to an

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Figure 2. Prognostic index of 2-year survival with the native liver. (A) A strategy that combines the 14-gene signature and serum TB at 3 months after hepatoportoenterostomy to generate a prognostic index. Kaplan-Meier plots in (B) and (C) dichotomize the cohort into groups of high or low survival based on prognostic index for individual patients. Log-rank test was used to compare the survival distributions of 2 groups. In (D) and (E), receiver operating characteristic curves generated by the prognostic index at 2 years of age for (D) discovery and (E) validation cohorts. Area under the curve (AUC) of index is shown. In (F), boxplots of expression levels of 14 genes in the low and high survival groups in discovery cohort and healthy controls group. Expression levels are represented as mean ± standard deviation of normalized expression (reads per kilobase of exon per million mapped reads [RPKM] value). P value was calculated by Wilcoxon rank sum test. *P < .05, **P < .01, ***P < .001.

increased production of extracellular matrix in the liver, with a relative abundance of aHSCs and aPFs, and a potential functional relationship between these cells and NK cells.

Gene Signature in the High Survival Group Is Enriched for Glutathione Metabolism Having demonstrated that biological pathways and cells converge to a predominant ﬁbrosis signature in subjects with

poor outcome, we reasoned that analysis of the transcriptional signature of those with improved survival may give insight into mechanisms of hepatocellular protection and identify therapeutic targets. To this end, we applied pathway enrichment analyses to genes upregulated in the high survival group (Figure 5A). Among the pathways with the highest statistical signiﬁcance, 6 related to oxidation and at least 4 were enriched for glutathione metabolism, implying a transcriptional response to oxidative radicals (Figure 5B and Supplementary

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Figure 3. Low survival group exhibits a prominent gene signatures of cirrhosis. (A) The use of differentially expressed genes between high and low survival groups compared with signatures of other liver diseases and animal models. The expression heatmaps of differentially expressed genes are shown for the discovery (B) and validation (C) cohorts (levels of gene expression are shown as color variation from red [high] to blue [low], with Euclidean distance used in hierarchical clustering); 14 genes are identiﬁed in the heatmaps. Pairwise overlapping comparisons were performed between upregulated or downregulated genes with gene signatures of human liver diseases (D), animal models of human liver diseases (E), and hepatic stellate cells and portal ﬁbroblasts (F). Overlaps were examined by Fisher’s exact test and shown as dots whose size is proportional to log10(P value) and gradation of red is proportional to log2(odds ratio). Overlaps with log10(P value) >1.3 and log2(odds ratio) >0 were deﬁned as signiﬁcant overlaps and are shown in red; the color black depicts overlaps with odds ratio 1 or P value > .05. HCC, hepatocellular carcinoma; NASH, nonalcoholic steatohepatitis; PBC, primary biliary cholangitis; PSC, primary sclerosing cholangitis.

Figure 2B). Some secondary pathways related to hepatocytes (eg, bile acid metabolism) are also featured in Figure 5B, but the lower strength of signiﬁcance implies a similar functional representation of these cells among the low and high survival

groups. Further analyzing the glutathione gene signature, we found the glutathione metabolism–related genes increased in the high survival group (Figure 5C). When expressed as a ratio to ssGSEA scores for aHSCs, aPFs, and cholangiocytes,

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glutathione metabolism–related genes increased in all 3 cell types in the high survival group in the discovery and validation cohorts (Figure 5D–F). These ﬁndings raised the possibility that an effective antioxidative response may suppress hepatic ﬁbrosis and promote improved survival in BA.

Treatment With a Glutathione-related Antioxidant Suppresses Injury and Fibrosis in Both Standard and Modiﬁed RRV Models To examine the potential impact of antioxidants on injury and hepatic ﬁbrosis of neonates, we used the standard RRV mouse model and a modiﬁed model by delaying its injection to 3 days of life. The development of this modiﬁed model was necessary because the standard model with RRV infection in the ﬁrst 24 hours of birth allows for studies of biliary injury (early phases of pathogenesis of BA) but not of ﬁbrosis due to early death of neonatal mice between 10 and 14 days.27 Although some studies show liver ﬁbrosis at day 14 after RRV infection in the standard model, the degree of ﬁbrosis is mild.28,29 In initial experiments to validate the modiﬁed model of delayed RRV infection, we examined extrahepatic bile ducts after RRV administration throughout the suckling period and found focal epithelial injury and inﬂammatory obstruction 7 days later, followed by improvement of the epithelial injury, decreased inﬂammation, and restoration of the duct lumen by day 14 (Supplementary Figure 6A). In the liver, portal tracts had severe inﬂammation and increased number of duct proﬁles, with Sirius red staining showing ﬁbrosis that intensiﬁed from days 7 to 19 (Supplementary Figure 6B and C). Next, directly testing the hypothesis that antioxidants can suppress injury, liver ﬁbrosis and improve survival, we injected NAC (150 mg/kg per day) or manganese (III) tetrakis-(4-benzoic acid)porphyrin (MnTBAP) (5 mg/kg per day) intraperitoneally beginning 12 hours after RRV inoculation in a standard model (Figure 6A) and in a modiﬁed model (Figure 7A). In control noninfected mice, the administration of similar doses of NAC or MnTBAP had no effect on growth or survival through 15 days of life, and mice displayed no unusual behavior suggestive of adverse effects of the drugs (data not shown). In both the standard and modiﬁed models, mice treated with NAC had better weight gain and survival (80% survival in at day 14 in the standard model, 100% survival beyond 19 days after infection or 22 days of age in the modiﬁed model) with lower levels of serum TB, ALT, and GGT when compared with RRVþphosphate-buffered saline (PBS), whereas the administration of MnTBAP was similar to RRVþPBS with lower weight and survival (Figure 6B–F and Figure 7B–F).

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Treatment with NAC substantially decreased epithelial injury and inﬂammation and prevented extrahepatic bile duct obstruction (Figure 6G and Figure 7G). In liver, NAC signiﬁcantly reduced portal inﬂammation, the extent of Sirius red staining, and the presence of a-SMAþ cells (Figure 6H–J and Figure 7I–J). In contrast, hematoxylineosin, Sirius red, and a-SMA staining following MnTBAP was similar to RRVþPBS (not shown). Using real-time polymerase chain reaction, we did not observe signiﬁcant changes of ﬁbrosis-associated genes in the standard model, probably due to mild degree of ﬁbrosis (Supplementary Figure 7A). Strikingly, treatment with NAC decreased the expression of the ﬁbrosis-associated genes Acta2, Col1a1, Timp1, and Tgfb1 in the modiﬁed model (Supplementary Figure 7B). These data pointed to a beneﬁcial effect of the glutathione-related antioxidant NAC in the suppression of neonatal injury and ﬁbrosis.

Discussion We found that the liver of infants with BA have a 14gene signature at diagnosis that predicts survival with the native liver at 2 years of age. This predictive feature is not shared by other clinical or biochemical parameters at diagnosis; however, the combination of the molecular signature with the serum TB level 3 months after HPE further improved the predictive properties as an index of survival. The superiority of the prognostic index over the 14-gene signature and TB 3 months after HPE as individual parameters does not minimize the unique value of the 14gene signature owing to its relative predictive properties for long-term outcome at the time of diagnosis (before surgical intervention). Functional analysis of the gene signature in the group with low survival revealed a prominent footprint for extracellular matrix production, which was further supported by digital modeling pointing to a high abundance of activated stellate cells and portal ﬁbroblasts. In the high survival group, pathway enrichment analyses identiﬁed an increased expression of genes related to glutathione metabolism. Consistent with this signature, the use of NAC in both standard RRV model and a neonatal mouse model of chronic injury decreased serum ALT, GGT, and bilirubin, suppressed ﬁbrosis, and fostered long-term survival. The ability of a 14-gene signature to identify groups of infants with high survival supports the existence of key biological processes that may play a role in the likelihood of response to surgical intervention and in the activation of cellular processes to promote tissue repair. Looking at the

= Figure 4. Extracellular matrix (ECM) formation related gene signature and cellular source, CD8T, and NK cells are enriched in the low survival group. (A) Analytic pipeline identifying enriched pathways and cell types in the low survival group and the quantiﬁcation of the relative abundance of individual cell types. Dotplot in (B) depicts pathways that are enriched in upregulated genes of the low survival group in the discovery cohort. Pathway terms were ranked according to their log10(false discovery rate) values and categorized into ECM, cell-cell interaction, inﬂammation, and other. The dot sizes are proportional to number of genes. (C–G) Boxplots show enrichment of aHSCs (C), aPFs (D), cholangiocytes (E), CD8 T cells (F), and NK cells (G). Enrichments are represented as mean ± standard deviation of ssGSEA scores. P value was calculated by Wilcoxon rank sum test. (H–J) Scatterplots represent linear correlation between NK cells and aHSCs, aPFs, and cholangiocytes. Pearson correlation coefﬁcient r is shown, with the gray area representing 95% conﬁdence limits. *P < .05, **P < .01, ***P < .001.

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Figure 5. Gene signature in the high survival group is enriched by glutathione metabolism. (A) An analytic pipeline identifying enriched pathways and cell types in the high survival group and the quantiﬁcation of the oxidation signatures relative to the abundance of individual cell types. The dotplot shown in (B) depicts pathways that are enriched in upregulated genes of the high survival group in the discovery cohort. Pathway terms were ranked according to their log10(false discovery rate) values and categorized into oxidation, metabolism, and other. The dot sizes are proportional to number of genes. (C–F) Boxplots show ssGSEA scores for glutathione metabolism (C) and ratios of ssGSEA scores for glutathione metabolism and aHSCs (D), aPFs (E), and cholangiocytes (F). *P < .05, **P < .01, ***P < .001.

potential relationship between the 14-gene signature and a young age at HPE being linked to better outcome, the ages differed between the 2 groups, but a multivariate analysis did not identify age as an important outcome factor. A similar relationship was reported previously, but the substantial overlap of ages in the high and low survival groups in that report limited the use of age as a predictor of outcome.11 The 14-gene expression signature does not contain the inﬂammation proﬁle associated with an improved outcome reported in this previous study, thus raising the possibility that the strength of the association in that study was falsely high because of a small sample size, as supported by the lack of association when we included the larger sizes for the discovery and validation cohorts reported here. The 14-gene signature does reproduce the previous report that a ﬁbrosis expression signature is

associated with low survival. A discrepancy to note is the lack of signiﬁcant correlation between histological ﬁbrosis and outcome,11 which was reported previously and reproduced here. A potential explanation being the sampling artifact derived from a heterogeneous tissue injury in which the examination of a 5- to 6-m thin section by histology may not include the neighboring ﬁbrosis. In contrast, the RNA is isolated from a relatively larger tissue fragment likely to contain a greater representation of tissue. Another possibility is the inﬂuence of other biological factors inﬂuencing outcome, such as the oxidative stress that we explore mechanistically in animal models. These factors notwithstanding, our study and the previous report have similar ﬁndings that a prominent expression of matrix genes adversely affects clinical outcome.11 The combination of these data with the prominent signature of activated stellate

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Figure 6. NAC suppresses liver injury in the standard mouse model of BA. (A) The experimental design of an RRV-induced BA model, with 1.5 106 ffu of RRV injected intraperitoneally into newborn BALB/C mice, followed by the daily intraperitoneal injection of PBS, NAC, or MnTBAP until day 14. Tissues and serum samples were harvested at days 7 and 14 after RRV injection. Daily body weights (mean ± standard deviation [SD]; n ¼ 18–26 mice per group from 3 independent experiments; shown in [B]), percent survival (C), and serum TB, ALT, and GGT (D–F; mean ± SD; n ¼ 6 mice per group). **P < 0.01, ***P < 0.001. Representative extrahepatic bile duct (G) and liver (H) sections with hematoxylin-eosin staining at day 7 or day 14 after RRV infection or wild-type saline controls without RRV infection. In (G), arrows denote areas of periductal inﬂammation; asterisk denotes duct lumen. Representative liver sections with Sirius red (I) and anti-a-SMA (J) staining at day 7 or day 14 after RRV infection or wild-type saline controls without RRV infection.

cells and portal ﬁbroblasts underscores the existence of a hepatic environment that is functionally primed to induce ongoing ﬁbrosis. Predictive algorithms for gene function point to the activation of glutathione pathways in infants with improved survival. Conversely, those with poor survival had low levels of expression of genes related to glutathione metabolism when analyzed as a group as well as when normalized by the levels in stellate cells, portal ﬁbroblasts, and cholangiocytes, implying a substantial level of oxidative stress.

These ﬁndings are particularly relevant in view of the increasing experimental evidence that oxidative stress is an important mechanism of biliary injury, obstructive phenotype, and expression of ﬁbrosis genes in a toxin model of BA in zebraﬁsh.30–32 In such a model, biliatresone, a naturally occurring biliary toxin, injures cholangiocytes by transiently depleting glutathione, which leads to a loss of cholangiocyte polarity, disruption of epithelial integrity, and activation of ﬁbrosis genes.31 Using pharmacologic and genetic means to manipulate the glutathione-redox homeostasis, investigators

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Figure 7. NAC suppresses liver injury and ﬁbrosis in a modiﬁed mouse model of BA. (A) Experimental design of an RRVinduced injury and liver ﬁbrosis model, with 1.875 106 ffu of RRV injected intraperitoneally into newborn BALB/C mice at day 3 of life, followed by the daily intraperitoneal injection of PBS, NAC, or MnTBAP until day 22 of life (19 doses), at which time tissues and serum samples were harvested. Daily body weights (mean ± standard deviation [SD]; n ¼ 18–26 mice per group from 3 independent experiments; shown in [B]), percent survival (C), and serum TB, ALT, and GGT (D–F; mean ± SD; n ¼ 6–12 mice per group). **P < .01, ***P < .001. Representative extrahepatic bile duct (G) and liver (H) sections with hematoxylin-eosin staining at day 7 or day 14 after RRV infection or wild-type saline controls without RRV infection. (G) Arrows denote areas of periductal inﬂammation; asterisk denotes duct lumen. Representative liver sections with Sirius red (I) and anti-a-SMA (J) staining at day 7 or day 14 after RRV infection or wild-type saline controls without RRV infection. (J) Arrows denote a-SMA– positive cells.

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demonstrated the impact of NAC in suppressing cholangiocyte injury.30 Here, we tested the effect of NAC on injury and ﬁbrosis in both a standard RRV mouse model and a modiﬁed mouse model of transient injury and obstruction of extrahepatic bile ducts that allows for prolonged survival and the development of hepatic ﬁbrosis. In both models, the prophylactic administration of NAC protected against cell injury (shown by lower ALT and GGT) and improved biliary ﬂow (shown by lower serum bilirubin); it also suppressed ﬁbrosis in the chronic injury model. The speciﬁcity of this metabolic pathway is supported by the lack of improvement in mice treated with the antioxidant MnTBAP. These data suggest that the availability of glutathione may be an important determinant of improved survival in infants with BA and identify glutathione metabolism as a potential therapeutic target in BA. The hepatic population of inﬂammatory cells in infants with BA has been linked to pathogenesis of disease, with their abundance at diagnosis suggesting an earlier stage of tissue injury.33 Their relationship to ﬁbrogenesis, however, is less clear. In mouse models of ﬁbrosis, CD8 T cells may promote ﬁbrosis by activation of stellate cells, whereas NK cells attenuate liver ﬁbrosis by killing these cells.34,35 In patients with hepatitis C virus infection, the level of intrahepatic NK cells was inversely correlated with ﬁbrosis stage. Without access to fresh liver tissue from infants with BA to directly quantify cells and correlate with ﬁbrosis, we used validated gene expression–based computational models to quantify the relative abundance of immune cells between the high and low survival groups. Among all cell types, only NK cells reproducibly correlated with stellate cells and portal ﬁbroblasts (as well as with the genes encoding the activation marker ACTA2/a-SMA) in a positive fashion regardless of the predictive model. We recognize the intrinsic limitation of analyses of whole liver RNA expression, as they do not allow for the detection of important biological changes that may be present in less abundant nonhepatocyte cells. Our approach estimated these cells in relative values across the cellular ecosystem, but the expression of RNAs as an average for each cell type cannot make visible the functional changes in subgroups of cells that may be important to mechanisms of disease (such as trans-differentiation) and tissue pathology. For such studies, the use of single-cell RNAseq will give a more detailed and accurate map of the cellular landscape of BA. In closing, the relationship between a 14-gene signature at diagnosis and the 2-year survival with the native liver provides insight into staging of liver disease and the development of new therapies. In relation to staging of disease, the validation of a prominent ﬁbrosis-related signature in a separate cohort and its relationship to poor outcome provide evidence that ﬁbrosis at diagnosis is a hallmark of poor survival with the native liver, a concept that has been suggested by previous reports.11–13 In these patients, ﬁbrosis may represent a later stage of liver disease that may not be uniformly correlated with the age at diagnosis, or it may represent a rapid ﬁbrosis program that is poorly understood and limited to a subgroup of patients. A potential application of the 14-gene

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signature is to guide clinical trials or the development of new care protocols that take into account the most prominent biological processes at the time of diagnosis. For example, infants with low expression of the 14 genes may be assigned to new trials evaluating the effect of new adjuvant therapies to enhance bile drainage after HPE; conversely, those with a uniformly high gene expression may be prioritized for antiﬁbrotic therapies, or perhaps not undergo HPE and be considered for close clinical follow-up with timely evaluation for liver transplantation (especially if the expression is present in infants >75 days). Before these uses, the predictive power of the gene signature requires a prospective validation in which the assignment of outcome in individual patients is made at diagnosis, with long-term follow-up of clinical outcome to validate or negate the prediction. A particularly appealing possibility is the design of a clinical trial designed for infants with high expression of glutathione aimed at testing the ability of NAC to suppress. In relation to new therapies, the enrichment of glutathione-related genes points to their potential role in promoting survival as well as in the suppression of ﬁbrosis, both supported by our data in neonatal mice treated with NAC, a compound that has been shown to stimulate bile acid–independent bile ﬂow.36 The known safety proﬁle of NAC in acute liver failure, its use in other pediatric diseases,37 and the data presented here form the foundation for a potential trial aimed at decreasing cellular injury and suppressing ﬁbrosis, perhaps targeting infants who have lower ﬁbrosis by the 14-gene signature or those with good response after HPE, in whom ﬁbrosis progresses despite ongoing bile ﬂow.

Supplementary Material Note: To access the supplementary material accompanying this article, visit the online version of Gastroenterology at www.gastrojournal.org, and at https://doi.org/10.1053/ j.gastro.2019.06.017.

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Author names in bold designate shared co-ﬁrst authorship. Received December 21, 2018. Accepted June 8, 2019. Reprint requests Address requests for reprints to Jorge A. Bezerra, MD, Division of Gastroenterology, Hepatology and Nutrition, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH 45229-3030, e-mail: Q1 [email protected]; fax: 513-636-5581.

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Acknowledgments Author contributions: Study concept and design: ZL, PS, and JAB; acquisition of data: ZL, RM, PS, and SG; analysis and interpretation of bioinformatic data: ZL and JAB; analysis and interpretation of mouse data: ZL, PS, and JAB; drafting of the manuscript: ZL and JAB. Conﬂicts of interest The authors disclose no conﬂicts. Funding Supported by National Institutes of Health grants DK-64008 and DK-83781 to Jorge A. Bezerra and by the Gene Analysis Cores of the Digestive Health Center (DK-78392). The work was also supported by U01 and UL1 grants from the National Institute of Diabetes and Digestive and Kidney Diseases:

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DK 62497 and UL1TR000077 (Cincinnati Children’s Hospital Medical Center), DK 62470 and UL1TR002378 (Children’s Healthcare of Atlanta), DK 62481 and UL1TR001878 (The Children’s Hospital of Philadelphia), DK 62456 (The University of Michigan), DK 84536 and UL1TR001108 (Riley Hospital for Children), DK 84575 and UL1TR002319 (Seattle Children’s Hospital), DK 62500 and UL1TR000004 (UCSF Children’s Hospital), DK 62503 (Johns Hopkins School of Medicine), DK 62445 (Mount Sinai School of Medicine), DK 62466 and UL1TR001857 (Children’s Hospital of Pittsburgh of UPMC), DK 62453 and UL1TR002535 (Children’s Hospital Colorado), DK 62452 (Washington University School of Medicine), DK 84538 and UL1TR000130 (Children’s Hospital Los Angeles), DK 62436 and UL1TR000150 (Ann & Robert H Lurie Children’s Hospital of Chicago), DK103149 (Texas Children’s Hospital), DK103135 (The Hospital for Sick Children), and DK103140 (University of Utah).

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Supplementary Methods RNAseq Analyses Total RNA was isolated from liver biopsies using the miRNeasy Kit (Qiagen, Valencia, CA), veriﬁed for integrity by the Agilent (Santa Clara, CA) 2100 Bioanalyzer, and converted to complementary DNA for sequencing library preparation by TruSeq Stranded mRNA Library Prep Kit from Illumina (San Diego, CA). DNA fragments were endrepaired to generate blunt ends with 50 phosphatase and 30 hydroxyls and adapters were ligated for single-end or paired-end sequencing on Illumina HiSeq 2500. Ten million reads with 50 base pairs (bp) in length for single-end RNAseq and 20 million mate-paired reads with 125 bp in length for paired-end RNAseq were generated. Single-end RNAseq was used in the discovery cohort and healthy controls, and paired-end RNAseq was used in the validation cohort. RNAseq reads were aligned to the human genome (GRCh37/hg19) using TopHat (version 2.1.0),1 with transcript quantiﬁcations performed by Cufﬂinks v2.2.1.2 Annotated transcripts were obtained from the UCSC genome browser (http://genome.ucsc.edu) and the Ensembl database. Transcript abundances were normalized in reads per kilobase of exon per million mapped reads (RPKM) for single-end RNAseq and fragments per kilobase of exon per million mapped reads (FPKM) for paired-end RNAseq. Genes with RPKM or FPKM <0.5 in more than half of samples were considered not expressed and ﬁltered out. Differentially expressed genes were identiﬁed by Cuffdiff 2.3 The selection of genes with a fold-change cutoff at 1.5 or higher included the Benjamini-Hochberg false discovery rate adjusted P < .05.

Supervised Principal Components for Cox Regression Steps for Superpc Cox are summarized as follows: (1) compute univariate Cox proportional hazard regression coefﬁcients for each gene, (2) estimate coefﬁcient threshold theta by 10-fold cross-validation, (3) generate a reduced gene expression matrix consisting of genes whose coefﬁcients exceed the threshold theta, (4) compute the ﬁrst principal component of the reduced matrix, (5) use the ﬁrst principal component to predict the survival outcome, and (6) assign predicted scores to patients using the ﬁrst principal component predictor and importance scores to genes. For the analysis, patients with higher predicted score have lower survival. The values of genes with positive importance scores means decrease in patient survival.

Gene Signatures for Liver Diseases and Animal Models of Hepatopathies Gene signatures for liver diseases and their animal models are deﬁned as differentially expressed genes between disease samples and healthy control samples identiﬁed by GEO2R (https://www.ncbi.nlm.nih.gov/geo/ geo2r/). We also curated signatures associated with

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inﬂammation or ﬁbrosis in BA, and prognosis signatures in hepatocellular carcinoma. Genes with the fold-change cutoff at 2 or higher and Benjamini-Hochberg false discovery rate adjusted P < .05 were considered differentially expressed. These signatures underwent pairwise overlapping comparisons between upregulated or downregulated genes in the low survival group and gene signatures of human liver diseases using Fisher’s exact test.

Pathway Enrichment Analysis and Estimation of Relative Cell Abundance For estimation of the relative abundance of individual cell types, unique marker gene signatures were obtained for immune cell types from 2 different studies (Bindea et al4 and Charoentong et al5), and for activated hepatic stellate cells, activated portal ﬁbroblasts and cholangiocytes from Zhang et al,6 Iwaisako et al,7 and Dianat et al,8 respectively. Gene signature for glutathione metabolism was obtained from the KEGG database (KEGG pathway: hsa00480). Levels of cell types or glutathione metabolism were quantiﬁed based on the normalized RNAseq expression values (RPKM or FPKM) of marker genes using the ssGSEA implemented in the GSVAR package. ssGSEA is rank-based method that produces an enrichment score for each sample. The enrichment score was calculated by a sum of the difference between weighted Empirical Cumulative Distribution Functions of the marker genes and the other genes in an expression proﬁle. The enrichment score represents the abundance of cells or enrichment of pathway. Differences between the low and high survival groups were tested by Wilcoxon rank sum test. To calculate the ratio of ssGSEA score for glutathione metabolism to ssGSEA score for aHSCs, aPFs, or cholangiocytes, we ﬁrst scaled the ssGSEA score to 0.1 to 1 range, then divided the ssGSEA score for glutathione metabolism by the ssGSEA score for aHSCs, aPFs, or cholangiocytes.

Standard and Modiﬁed Neonatal Mouse Model of BA and Antioxidant Treatment Testing the effect of antioxidants in this model, NAC (A9165; Sigma,) or MnTBAP (EMD Millipore, Burlington, Q5 MA) were reconstituted in 1x phosphate-buffered saline, and administered in daily doses of 150 mg/kg (for NAC) or 5 mg/kg (for MnTBAP) beginning 12 hours after RRV inoculation; equal volumes of 1x phosphate-buffered saline were injected into RRV-infected mice serving as controls. All groups of experimental mice were phenotyped daily as described previously.9 In brief, Mice were killed at 7 and 14 days for the standard model, and at 7, 14, and 19 days for the modiﬁed model after RRV injections, and extrahepatic bile duct, liver, and plasma samples were obtained for analyses. Mice treated with 0.9% saline were used as healthy controls. All BALB/c mice were maintained with normal diet in pathogen-free vivarium rooms equipped with a 12hour dark-light cycle. Animal studies and experimental protocols were performed in strict accordance with the guidelines recommended by the Institutional Animal Care and Use Committee (Protocol IACUC2017-0007) of Cincinnati Children’s Hospital Medical Center.

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Histopathology and Immunoﬂuorescence Staining Mouse livers and extrahepatic bile ducts were microdissected using a stereomicroscope, and tissues were parafﬁn-embedded, sectioned, and stained with hematoxylin-eosin for both tissues and with Sirius red, aSMA staining for livers only. Human liver biopsies were obtained at the time of HPE and snap-frozen in liquid nitrogen, formalin-ﬁxed/parafﬁn-embedded, and sectioned. Liver sections were blocked from nonspeciﬁc binding by using normal goat serum and incubated with the primary antibodies rabbit anti-a-SMA (ab5694 from Abcam, Cambridge, MA, for mouse tissues, and 1A4 from Roche, Indianapolis, IN, for human tissues) or rabbit anti-cytokeratin 7 antibody (KRT7; ab119697 from Abcam for mouse and human livers). Secondary anti-rabbit biotinylated antibodies were from Vectastain ABC-HRP Kit (PK-4001; Vector Lab, Burlingame, CA). Images were captured using Olympus BX51 microscope (Olympus America Inc., Center Valley, PA) and cellSens Dimension digital imaging software (Olympus Corporation, version 1.8.1); the stained areas were quantiﬁed by ImageJ software (National Institutes of Health, Bethesda, MD), as reported previously.10 Human liver biopsies were obtained at the time of HPE. Seven and 8 human BA liver sections from low and high survival groups, respectively, and 3 normal liver sections were used for KRT7 and a-SMA staining. a-SMA or KRT7-positive areas in human liver sections were quantiﬁed by ImageJ software. Quantiﬁcation of aHSCs and aPFs are represented as a-SMA– positive areas in lobular and portal regions, respectively. Quantiﬁcation of cholangiocytes are represented as KRT7positive areas. a-SMA–positive arteries and veins were manually excluded before quantiﬁcation.

Colorimetric Assay for TB, ALT, and GGT Plasma TB, ALT, and GGT concentrations were measured with Total Bilirubin Reagent Set (Pointe Scientiﬁc Inc, Canton, MI), Liquid GGT Reagent Set (Pointe Scientiﬁc Inc), and DiscretPak ALT Reagent Kit (Catachem, Bridgeport, CT) according to the manufacturers’ instructions. Photometric absorbance for assays was read on a Synergy H1 Hybrid Reader (BioTek, Winooski, VT) at 555 nm for TB and ALT, and at 405 nm for GGT.

Real-Time Polymerase Chain Reaction RNA was isolated from mouse liver tissues using the RNeasy Mini Kit (Qiagen) and subjected to real-time polymerase chain reaction with the Brilliant III SYBR Green QPCR Master Mix Gene Expression Assay Kit and the Mx3005p system (Stratagene, La Jolla, CA), normalized with glyceraldehyde 3-phosphate dehydrogenase (Gapdh).

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Statistical Analyses Kaplan-Meier curves were generated using GraphPad Prism 6 (La Jolla, CA). Receiver operating characteristic curves were generated using survivalROC package (version 1.0.3). Gene expression heatmaps were generated using GENE-E software. Boxplots and barplots were generated using ggplot2 package (version 2.2.1), and correlation plots were generated using Corrplot package (version 0.84). Cox proportional hazards regression, log-rank test, Wilcoxon rank sum test, Fisher exact test, and Harrell’s C-statistic were performed in R (version 3.3.0).

References 1.

Trapnell C, Pachter L, Salzberg SL. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 2009; 25:1105–1111. 2. Trapnell C, Roberts A, Goff L, et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufﬂinks. Nat Protoc 2012;7:562–578. 3. Trapnell C, Hendrickson DG, Sauvageau M, et al. Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat Biotechnol 2013;31:46–53. 4. Bindea G, Mlecnik B, Tosolini M, et al. Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity 2013; 39:782–795. 5. Charoentong P, Finotello F, Angelova M, et al. Pancancer immunogenomic analyses reveal genotypeimmunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep 2017; 18:248–262. 6. Zhang DY, Goossens N, Guo J, et al. A hepatic stellate cell gene expression signature associated with outcomes in hepatitis C cirrhosis and hepatocellular carcinoma after curative resection. Gut 2016;65:1754–1764. 7. Iwaisako K, Jiang C, Zhang M, et al. Origin of myoﬁbroblasts in the ﬁbrotic liver in mice. Proc Natl Acad Sci U S A 2014;111:E3297–E3305. 8. Dianat N, Dubois-Pot-Schneider H, Steichen C, et al. Generation of functional cholangiocyte-like cells from human pluripotent stem cells and HepaRG cells. Hepatology 2014;60:700–714. 9. Yang L, Mizuochi T, Shivakumar P, et al. Regulation of epithelial injury and bile duct obstruction by NLRP3, IL1R1 in experimental biliary atresia. J Hepatol 2018; 69:1136–1144. 10. Koyama Y, Wang P, Liang S, et al. Mesothelin/mucin 16 signaling in activated portal ﬁbroblasts regulates cholestatic liver ﬁbrosis. J Clin Invest 2017;127:1254–1270.

Author names in bold designate shared co-ﬁrst authorship

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Supplementary Figure 1. Kaplan-Meier survival curves for the discovery and validation cohorts. Log-rank test was used to compare the survival distributions of the 2 groups.

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Supplementary Figure 2. Pathway enrichment analysis of upregulated genes in the low and high survival groups in the validation cohort. Dotplots depict pathways that are enriched in upregulated genes of the low (A) and high (B) survival groups in the validation cohort. Pathway terms were ranked according to their log10(false discovery rate) values and categorized into extracellular matrix (ECM), cell-cell interaction, inﬂammation, and other for (A), and oxidation, metabolism, and other for (B).

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Supplementary Figure 3. Correlation between ssGSEA method and section staining, and levels of aHSCs, aPFs, and cholangiocytes. Scatterplots represent linear correlation between quantiﬁcation by ssGSEA method and quantiﬁcation by section staining for aHSCs (A), aPFs (B), and cholangiocytes (C). Pearson correlation coefﬁcient r is shown, with the gray area representing 95% conﬁdence limits. Representative human liver sections with anti-a-SMA (upper panel in [D]) and anti-KRT7 staining (bottom panel in [D]). Boxplots show percentage of positive area for aHSCs (a-SMA–positive area in lobular region [E]), aPFs (a-SMA–positive area in portal region [F]), and cholangiocytes (KRT7-positive area [G]). *P < .05, **P < .01, calculated by t test. Boxplots show the expression of ACTA2 (encoding a-SMA, marker of aHSCs and aPFs [H]) and KRT7 and KRT19 (markers for cholangiocytes [I]). **P < .01, ***P < .001, calculated by Wilcoxon rank sum test.

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Supplementary Figure 4. Enrichment of CD8 T and NK cells in the low survival group and their correlation with aHSCs, aPFs, and cholangiocytes. Boxplots in (A) and (B) show enrichment of CD8 T and NK cells, respectively, using signature from Charoentong et al.5 Enrichments are shown as mean ± standard deviation of ssGSEA scores, with P values calculated by Wilcoxon rank sum test. (C–E) Scatterplots of the correlations between NK cells and aHSCs (C), aPFs (D), and cholangiocytes (E) also using the method from Charoentong et al.5 The remaining Scatterplots (F–K) show the correlation between CD8 T cells and aHSCs, aPFs, and cholangiocytes using signatures from Bindea et al4 as well as Charoentong et al.5 Enrichments are represented as ssGSEA scores. Pearson correlation coefﬁcient r is shown, with the gray area representing 95% conﬁdence limits. *P < .05, **P < .01, ***P < .001.

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Supplementary Figure 6. Neonatal mouse model of liver ﬁbrosis. (A) Representative sections stained with hematoxylin-eosin at different time points after administration of 1.875 106 ffu of RRV in 25 mL volume at day 3 of life; an equal volume of 0.9% normal saline was injected into control mice. Extrahepatic bile ducts (EHBDs) and livers were harvested from both groups at days 7, 14, and 19 after injections. Hematoxylin-eosin staining of EHBDs show normal bile duct epithelium in control mice (top) whereas RRV challenge (bottom) induces inﬂammatory obstruction of the duct lumen at 7 days followed by restoration of duct epithelium and lumen at days 14 and 19. Arrows denote areas of periductal inﬂammation; asterisk denotes duct lumen. In (B), liver sections show normal parenchyma and portal triads. Infection with RRV (bottom) induces signiﬁcant portal inﬂammation, cholangitis, and expansion of portal spaces. Arrows indicate inﬂammatory cell inﬁltrations. (C) Sirius red stainings of normal livers (top) and after RRV infection (bottom) resulting in progressive accumulation of the red staining localized to portal areas that is mild at day 7 and extensive by day 19 after RRV challenge. Arrrowheads denote Sirius red staining; n ¼ 8–28 mice per group/time-point from 3–6 independent experiments.

=

Supplementary Figure 5. Immune cell differences between the low and the high survival groups. Boxplots in (A) show enrichments of immune cell types using the method published in Bindea et al.4 Enrichments are represented as ssGSEA scores; *P < .05, **P < .01, ***P < .001. In (B), boxplots show enrichments of immune cell types based on the method of Charoentong et al.5 Enrichments are represented as mean ± standard deviation of ssGSEA score; *P < .05, **P < .01, ***P < .001. aB cells, activated B cells; aDC, activated dendritic cell; aT cells, activated T cells; DC, dendritic cell; iB cells, immature B cells; iDC, immature dendritic cell; mB cells, memory B cells; MDSC, myeloid-derived suppressor cells; NKT, natural killer T cells; pDC, plasmacytoid dendritic cell; Tcm cells, central memory T cells; Tem, effector memory T cells; Tfh cells, T follicular helper cells; Tgd cells, gamma delta T cells; Th1 cells, type 1 T helper cells; Th2 cells, type 2 T helper; Th17 cells, T helper 17 cells; Treg cells, regulatory T cells.

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Supplementary Figure 7. NAC suppresses expression of ﬁbrotic genes in modiﬁed mouse model of BA. Graphs show the expression levels of ﬁbrosis-related genes Acta2, Col1a1, Timp1, and Tgfb1 in Standard RRV model (A) and modiﬁed model (B). Data are shown as mean ± standard deviation. n ¼ 6–12 mice per group from 3 independent experiments are shown. *P < .05, **P < .01, ***P < .001.

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Supplementary Table 1.Information of Studies for Liver Diseases and Their Animal Models Name in Figure 3 Up in cirrhosis Down in cirrhosis Poor prognosis in HCC Good prognosis in HCC Up in NASH Down in NASH Inﬂammation signature in BA Fibrosis signature in BA Up in PBC Down in PBC Up in PSC Down in PSC Up in BDL rat Down in BDL rat Up in DEN rat Down in DEN rat Up in CCl4 rat Down in CCl4 rat Up in CCl4 mouse Down in CCl4 mouse Up in EHBD: BA RRV D3 Down in EHBD: BA RRV D3 Up in EHBD: BA RRV D7 Down in EHBD: BA RRV D7 Up in EHBD: BA RRV D14 Down in EHBD: BA RRV D14 Hepatic stellate cell signature Portal ﬁbroblast signature

Disease or model Cirrhotic liver Cirrhotic liver Hepatocellular carcinoma Hepatocellular carcinoma Nonalcoholic steatohepatitis Nonalcoholic steatohepatitis Biliary atresia Biliary atresia Primary biliary cholangitis Primary biliary cholangitis Primary sclerosing cholangitis Primary sclerosing cholangitis Bile duct ligation for two weeks Bile duct ligation for two weeks 100 mg/kg diethylnitrosamine for 18 weeks 100 mg/kg diethylnitrosamine for 18 weeks Carbon tetrachloride Carbon tetrachloride 0.1cc of a 40% CCL4 for 18 weeks 0.1cc of a 40% CCL4 for 18 weeks EHBD and gallbladder with RRV infection at EHBD and gallbladder with RRV infection at EHBD and gallbladder with RRV infection at EHBD and gallbladder with RRV infection at EHBD and gallbladder with RRV infection at EHBD and gallbladder with RRV infection at NA NA

Control

day day day day day day

3 3 7 7 14 14

Normal Normal NA NA Normal Normal NA NA Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal NA NA

liver liver

liver liver

liver liver liver liver liver liver liver liver liver liver liver liver EHBD EHBD EHBD EHBD EHBD EHBD

Species

GEO accession

Reference PMID

Human Human Human Human Human Human Human Human Human Human Human Human Rat Rat Rat Rat Rat Rat Mouse Mouse Mouse Mouse Mouse Mouse Mouse Mouse Mouse Mouse

GSE6764 GSE6764 GSE10140 GSE10140 NA NA GSE15235 GSE15235 NA NA NA NA GSE13747 GSE13747 GSE27641 GSE27641 GSE73499 GSE73499 GSE27641 GSE27641 GSE46995 GSE46995 GSE46995 GSE46995 GSE46995 GSE46995 NA NA

17393520 17393520 18923165 18923165 25581263 25581263 20465800 20465800 11559656 11559656 11559656 11559656 20077562 20077562 24677197 24677197 27659347 27659347 24677197 24677197 24493287 24493287 24493287 24493287 24493287 24493287 26045137 25074909

EHBD, extrahepatic bile duct; HCC, hepatocellular carcinoma; NASH, nonalcoholic steatohepatitis; PBC, primary biliary cholangitis; PSC, primary sclerosing cholangitis.

Supplementary Table 2.Forward and Reverse Oligonucleotide Primer Sequences to Quantify the Expression of Fibrotic Markers Name

Sequence 5’ to 3’

Col1a1

Forward: TGGTGCTAAGGGTGAAGCTG Reverse: TCCATCAGCACCAGGGTTTC Forward: GGCATCCACGAAACCACCTA Reverse: AATGCCTGGGTACATGGTGG Forward: TTCTTGGTTCCCTGGCGTAC Reverse: ACTCTCCAGTTTGCAAGGGA Forward: CCGCAACAACGCCATCTATG Reverse: TGCCGTACAACTCCAGTGAC Forward: TGGTTTGACAATGAATACGGCTAC Reverse: GGTGGGTGGTCCAAGGTTTC

Acta2 Timp1 Tgfb1 Gapdh

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Q8

2941 2942 2943 2944 2945 2946 2947 2948 2949 2950 2951 2952 2953 2954 2955 2956 2957 2958 2959 2960 2961 2962 2963 2964 2965 2966 2967 2968 2969 2970 2971 2972 2973 2974 2975 2976 2977 2978 2979 2980 2981 2982 2983 2984 2985 2986 2987 2988 2989 2990 2991 2992 2993 2994 2995 2996 2997 2998 2999 3000

15.e11

3001 3002 3003 3004 3005 3006 3007 3008 3009 3010 3011 3012 3013 3014 3015 3016 3017 3018 3019 3020 3021 3022 3023 3024 3025 3026 3027 3028 3029 3030 3031 3032 3033 3034 3035 3036 3037 3038 3039 3040 3041 3042 3043 3044 3045 3046 3047 3048 3049 3050 3051 3052 3053 3054 3055 3056 3057 3058 3059 3060

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Supplementary Table 3.Characteristics of Patients at the Time of Diagnosis Discovery cohort (n ¼ 121)

Patient characteristic Sex, n (%) Female Male Age (d), median (25%–75%) TB (Mean ± SD, mg/dL) AST (mean ± SD, IU) ALT (mean ± SD, IU) GGT (mean ± SD, IU) Platelets BASM, n (%) Histological inﬂammation, n (%) 0 1 2 3 Histological ﬁbrosis, n (%) 0 1 2 3 4

65 (54) 56 (46) 63 (46–76) 7.4 ± 2.8 172 ± 137.1 128 ± 124.4 605 ± 530.6 490.0 ± 215.4 12 (10)

Validation cohort (n ¼ 50) 27 (54) 23 (46) 63 (46–77) 7.9 ± 3.2 206.8 ± 160.3 133.0 ± 103.3 718.6 ± 514.8 481.8 ± 175.3 7 (14)

P valuea 1 .7859 .8894 .4621 .3169 .8264 .4275 .4336

18 31 64 8

(15) (26) (53) (7)

2 18 23 7

(4) (36) (46) (14)

.0544

3 33 62 18 5

(2) (27) (51) (14) (4)

0 6 31 11 2

(0) (12) (62) (22) (4)

.1471

AST, aspartate aminotransferase; SD, standard deviation. a P value for Sex, BASM (biliary atresia splenic malformation syndrome), histological inﬂammation, and ﬁbrosis were calculated by Fisher exact test, P values for others were calculated by Wilcoxon rank sum test.

Supplementary Table 4.Information of 14 Prognostic Genes Gene symbol LOXL1 C1QTNF5 3H4P HSDL1 SFRP4 SLC44A3 GLMN DPY30 ABHD4 KIF7 EDIL3 PLOD1 LGALS4 HSF2

Gene name

Entrez ID

Importance score

Lysyl oxidase like 1 C1q and TNF related 5 Prolyl 3-hydroxylase family member 4 (non-enzymatic) Hydroxysteroid dehydrogenase like 1 Secreted frizzled related protein 4 Solute carrier family 44 member 3 Glomulin, FKBP associated protein Dpy-30, histone methyltransferase complex regulatory subunit Abhydrolase domain containing 4 Kinesin family member 7 EGF like repeats and discoidin domains 3 Procollagen-lysine,2-oxoglutarate 5-dioxygenase 1 Galectin 4 Heat shock transcription factor 2

4016 114902 10609 83693 6424 126969 11146 84661 63874 374654 10085 5351 3960 3298

68.95 67.246 66.785 58.281 57.239 55.351 53.919 47.122 46.62 45.807 44.443 41.664 35.479 30.262

FLA 5.6.0 DTD YGAST62717_proof 27 August 2019 12:28 am ce

3061 3062 3063 3064 3065 3066 3067 3068 3069 3070 3071 3072 3073 3074 3075 3076 3077 3078 3079 3080 3081 3082 3083 3084 3085 3086 3087 3088 3089 3090 3091 3092 3093 3094 3095 3096 3097 3098 3099 3100 3101 3102 3103 3104 3105 3106 3107 3108 3109 3110 3111 3112 3113 3114 3115 3116 3117 3118 3119 3120

-

3121 3122 3123 3124 3125 3126 3127 3128 3129 3130 3131 3132 3133 3134 3135 3136 3137 3138 3139 3140 3141 3142 3143 3144 3145 3146 3147 3148 3149 3150 3151 3152 3153 3154 3155 3156 3157 3158 3159 3160 3161 3162 3163 3164Q9 3165 3166 3167 3168 3169 3170 3171 3172 3173 3174 3175 3176 3177 3178 3179 3180

2019

Molecular Prediction of Outcome in Biliary Atresia

15.e12

Supplementary Table 5.Characteristics of Patients at the Time of Diagnosis Discovery cohort (n ¼ 121) Patient characteristic Sex, n (%) Female Male Age (d), median (25%–75%) TB (mean ± SD, mg/dL) AST (mean ± SD, IU) ALT (mean ± SD, IU) GGT (mean ± SD,IU) Platelets BASM, n (%) Histological inﬂammation, n (%) 0 1 2 3 Histological ﬁbrosis, n (%) 0 1 2 3 4

Validation cohort (n ¼ 50)

Low survival (n ¼ 60)

High survival (n ¼ 61)

P valuea

Low survival (n ¼ 25)

High survival (n ¼ 25)

P valuea

34 (57) 26 (43) 68 (55–78) 8.2 ± 2.9 210.7 ± 123.8 156.3 ± 124.3 825.3 ± 593.1 477.3 ± 203.0 4 (7)

31 (51) 30 (49) 52 (40–73) 7.2 ± 2.7 208.8 ± 149.9 150.6 ± 125.4 629.4 ± 448.3 467.5 ± 198.6 8 (13)

.5859

15 (60) 10 (40) 59 (55–82) 8.1 ± 2.7 252.9 ± 201.9 167.4 ± 123.6 881.2 ± 592.7 445.8 ± 127.8 3 (25)

12 (48) 13 (52) 50 (37–71) 7.7 ± 3.6 163.0 ± 90.8 101.4 ± 68.8 548.9 ± 357.7 515.0 ± 206.9 4 (25)

.571

.0001 .0849 .5053 .6822 .0535 .6793 .3626

.034 .187 .274 .068 .077 .187 1.000

10 12 34 3

(17) (20) (56) (5)

8 19 30 5

(13) (31) (49) (8)

.4857

0 11 10 2

(0) (44) (40) (8)

2 7 13 5

(8) (28) (52) (20)

.237

1 13 26 14 5

(2) (22) (43) (23) (8)

2 20 36 4 0

(3) (33) (59) (7) (0)

.0039

0 1 13 9 2

(0) (4) (52) (36) (8)

0 5 18 2 0

(0) (20) (72) (8) (0)

.016

AST, aspartate aminotransferase; SD, standard deviation. a P values for sex, BASM (biliary atresia splenic malformation syndrome), histological inﬂammation, and ﬁbrosis were calculated by Fisher exact test, P values for others were calculated by Wilcoxon rank sum test.

Supplementary Table 6.Association of 14-Gene Signature and Clinical Variables at Diagnosis and at 1 and 3 Months After HPE With Survival in the Discovery Cohort Univariable analysis Variable 14-gene signature Age at diagnosis (days) Sex Platelets at diagnosis APRI at diagnosis Histological inﬂammation Histological ﬁbrosis stage TB at diagnosis AST at diagnosis ALT at diagnosis GGT at diagnosis TB at 1 month AST at 1 month ALT at 1 month GGT at 1 month TB at 3 months AST at 3 months ALT at 3 months GGT at 3 months

Hazard Ratio (95% CI) 2.463 1.009 1.608 1.000 1.272 0.959 0.909 1.000 1.000 1.001 1.000 1.271 1.007 1.001 1.000 1.229 1.004 1.003 1.000

(1.754–3.459) (0.998–1.019) (0.922–2.804) (0.999–1.002) (0.8432–1.92) (0.699–1.316) (0.5943–1.391) (0.909–1.100) (0.998–1.002) (0.999–1.003) (0.999–1.001) (1.175–1.376) (1.003–1.010) (0.998–1.003) (0.999–1.000) (1.173–1.289) (1.003–1.005) (1.001–1.005) (0.999–1.000)

Multivariable analysis P value

Hazard Ratio (95% CI)

P value

<.0001 .109 .094 .602 .251 .797 .660 .999 .930 .522 .435 <.0001 .0002 .717 .473 <.0001 <.0001 .0009 .717

2.200 (1.3500–3.587)

.002

0.953 (0.8143–1.114) 1.000 (0.993–1.008)

.543 .940

1.205 (1.130–1.284) 1.003 (0.997–1.009) 0.999 (0.993–1.005)

<.0001 .309 .633

AST, aspartate aminotransferase; CI, conﬁdence interval.

FLA 5.6.0 DTD YGAST62717_proof 27 August 2019 12:28 am ce

3181 3182 3183 3184 3185 3186 3187 3188 3189 3190 3191 3192 3193 3194 3195 3196 3197 3198 3199 3200 3201 3202 3203 3204 3205 3206 3207 3208 3209 3210 3211 3212 3213 3214 3215 3216 3217 3218 3219 3220 3221 3222 3223 3224 3225 3226 3227 3228 3229 3230 3231 3232 3233 3234 3235 3236 3237 3238 3239 3240

15.e13

3241 3242 3243 3244 3245 3246 3247 3248 3249 3250Q10 3251 3252 3253 3254 3255 3256 3257 3258 3259 3260 3261 3262 3263 3264 3265 3266 3267 3268 3269 3270 3271 3272 3273 3274 3275 3276 3277 3278 3279 3280 3281 3282 3283 3284 3285 3286 3287 3288 3289 3290 3291 3292 3293 3294 3295 3296 3297 3298 3299 3300

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Supplementary Table 7.Association of 14-Gene Signature and Clinical Variables at Diagnosis and at 1 and 3 Months after HPE With Survival in the Validation Cohort Univariable analysis Variable

Hazard Ratio (95% CI)

14-gene signature Age at diagnosis (d) Sex Platelets at diagnosis APRI at diagnosis Histological inﬂammation Histological ﬁbrosis TB at diagnosis AST at diagnosis ALT at diagnosis GGT at diagnosis TB at 1 month AST at 1 month ALT at 1 month GGT at 1 month TB at 3 months AST at 3 months ALT at 3 months GGT at 3 months

1.873 1.006 1.397 1.000 0.912 1.153 1.078 1.068 1.002 1.002 1.000 1.255 1.005 1.005 1.000 1.148 1.002 1.002 1.000

(1.079- 3.25) (0.992–1.020) (0.617–3.165) (0.998–1.003) (0.573–1.45) (0.688–1.934) (1.053–1.781) (0.954–1.195) (0.999–1.005) (0.998–1.006) (0.999–1.001) (1.093–1.441) (1.002–1.009) (0.998–1.011) (0.999–1.000) (1.067–1.234) (1.000–1.004) (0.999–1.005) (0.999–1.000)

Multivariable analysis P value

Hazard Ratio (95% CI)

P value

.003 .431 .423 .798 .697 .589 .390 .253 .205 .407 .582 .001 .002 .162 .883 .0002 .010 .033 .896

1.795 (1.329–1.921)

.006

0.953 (0.801–1.375) 1.005 (0.999–1.011)

.727 .135

1.301 (1.039–1.631) 0.993 (0.984–1.003) 1.006 (0.995–1.018)

.002 .164 .292

AST, aspartate aminotransferase; CI, conﬁdence interval.

Supplementary Table 8.Predictive Accuracy of Prognostic Models Discovery Parameter

Validation

AUC C-statistic Sensi-tivity Speci-ﬁcity PPV NPV AUC C-statistic Sensi-tivity Speci-ﬁcity PPV NPV

14-gene 0.734 signature TB at 3 0.920 months (3M TB) Index (14-gene 0.948 signature þ 3M TB)

0.711

0.748

0.792

0.791 0.758 0.732

0.704

0.733

0.755

0.741 0.821

0.844

0.771

0.844

0.771 0.814 0.780

0.746

0.801

0.762

0.746 0.813

0.862

0.886

0.867

0.822 0.907 0.813

0.763

0.825

0.821

0.801 0.811

AUC, area under the curve; NPV, negative predictive value; PPV, positive predictive value.

FLA 5.6.0 DTD YGAST62717_proof 27 August 2019 12:28 am ce

3301 3302 3303 3304 3305 3306 3307 3308 3309 3310 3311 3312 3313 3314 3315 3316 3317 3318 3319 3320 3321 3322 3323 3324 3325 3326 3327 3328 3329 3330 3331 3332 3333 3334 3335 3336 3337 3338 3339 3340 3341 3342 3343 3344 3345 3346 3347 3348 3349 3350 3351 3352 3353 3354 3355 3356 3357 3358 3359 3360

Gene Expression Signatures Associated With Survival Times of Pediatric Patients With Biliary Atresia Identify Potential Therapeutic Agents

Gene Expression Signatures Associated With Survival Times of Pediatric Patients With Biliary Atresia Identify Potential Therapeutic Agents

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