Interferon signalling in the liver : implications for the natural course and therapy of hepatitis C

Hepatitis C virus is a global health concern, estimated to infect 2-3% of the world's population. Inter-individual differences in the course of infection and response to therapy, highlighted by recent genomewide association studies, point to the crucial role of the host immune system in the efficient control of infection. Ongoing progress in the studies of the role of innate immunity during hepatitis C virus infection has improved our understanding of the intricacies of the host-virus interactions. In this work I present and discuss results of three studies aimed to dissect interferon signalling in the liver in the context of natural course or therapy of hepatitis C virus infection. Interferon-based therapies are in clinical use for treatment of diseases such as hepatitis C virus infection or multiple sclerosis. Interferon-induced regulators of the Jak-STAT signalling are known to involve in negative feedback loops and affect the response to exogenously administered interferon alpha. In this context it is important to understand which interferon subtypes are potent inducers of the negative regulators and whether all interferons are equally sensitive to the inhibitory mechanisms. To tackle this question we attempted to characterize and compare response patterns to interferons alpha, beta and lambda in a setting of continuous and repeated stimulation (see Section 3.1). The acute phase of hepatitis C virus infection in humans (first 6 months after transmission) is characterized by high rates of spontaneous clearance and excellent treatment response (>90% cure rate). As the infection at that stage is mostly asymptomatic, it is rarely diagnosed and, in comparison to the chronic phase of hepatitis C virus infection, little is known about the human liver response to acute hepatitis C virus infection and the host-virus interactions during this time. In the second part of this PhD project we made use of the acute hepatitis C liver biopsies collected over the course of several years at the University Hospital of Basel to describe human hepatic response to acute hepatitis C virus infection and gain an insight into the mechanism of improved cure rate compared to chronic hepatitis C (see Section 3.2). Chronic hepatitis C is currently treated with combination therapies based on pegylated interferon- alpha. A significant proportion of patients fails to respond to the current treatment options, probably due to the refractory state of the preactivated endogenous interferon system in the liver. Several compounds are currently in clinical development with the aim to improve the treatment outcome of pegylated interferon�alpha nonresponders. In the last part of this work we investigated in vivo the mode of action of a novel synthetic TLR9 agonist which is a clinical candidate for anti-hepatitis C virus therapy and characterized the hepatic response to this compound (see Section 3.3)

Protein phosphatase 2A promotes hepatocellular carcinogenesis in the diethylnitrosamine mouse model through inhibition of p53

Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide. Most HCCs develop in cirrhotic livers. Alcoholic liver disease, chronic hepatitis B and chronic hepatitis C are the most common underlying liver diseases. Hepatitis C virus (HCV)-specific mechanisms that contribute to HCC are presently unknown. Transgenic expression of HCV proteins in the mouse liver induces an overexpression of the protein phosphatase 2A catalytic subunit (PP2Ac). We have previously reported that HCV-induced PP2Ac overexpression modulates histone methylation and acetylation and inhibits DNA damage repair. In this study, we analyze tumor formation and gene expression using HCV transgenic mice that overexpress PP2Ac and liver tissues from patients with HCC. We demonstrate that PP2Ac overexpression interferes with p53-induced apoptosis. Injection of the carcinogen, diethylnitrosamine, induced significantly more and larger liver tumors in HCV transgenic mice that overexpress PP2Ac compared with control mice. In human liver biopsies from patients with HCC, PP2Ac expression was significantly higher in HCC tissue compared with non-tumorous liver tissue from the same patients. Our findings demonstrate an important role of PP2Ac overexpression in liver carcinogenesis and provide insights into the molecular pathogenesis of HCV-induced HC

Machine Learning Successfully Detects Patients with COVID-19 Prior to PCR Results and Predicts Their Survival Based on Standard Laboratory Parameters in an Observational Study

Introduction: In the current COVID-19 pandemic, clinicians require a manageable set of decisive parameters that can be used to (i) rapidly identify SARS-CoV-2 positive patients, (ii) identify patients with a high risk of a fatal outcome on hospital admission, and (iii) recognize longitudinal warning signs of a possible fatal outcome. Methods: This comparative study was performed in 515 patients in the Maria Skłodowska-Curie Specialty Voivodeship Hospital in Zgierz, Poland. The study groups comprised 314 patients with COVID-like symptoms who tested negative and 201 patients who tested positive for SARS-CoV-2 infection; of the latter, 72 patients with COVID-19 died and 129 were released from hospital. Data on which we trained several machine learning (ML) models included clinical findings on admission and during hospitalization, symptoms, epidemiological risk, and reported comorbidities and medications. Results: We identified a set of eight on-admission parameters: white blood cells, antibody-synthesizing lymphocytes, ratios of basophils/lymphocytes, platelets/neutrophils, and monocytes/lymphocytes, procalcitonin, creatinine, and C-reactive protein. The medical decision tree built using these parameters differentiated between SARS-CoV-2 positive and negative patients with up to 90–100% accuracy. Patients with COVID-19 who on hospital admission were older, had higher procalcitonin, C-reactive protein, and troponin I levels together with lower hemoglobin and platelets/neutrophils ratio were found to be at highest risk of death from COVID-19. Furthermore, we identified longitudinal patterns in C-reactive protein, white blood cells, and D dimer that predicted the disease outcome. Conclusions: Our study provides sets of easily obtainable parameters that allow one to assess the status of a patient with SARS-CoV-2 infection, and the risk of a fatal disease outcome on hospital admission and during the course of the disease

Genome-wide whole blood transcriptome profiling in a large European cohort of systemic sclerosis patients

Objectives The analysis of annotated transcripts from genome-wide expression studies may help to understand the pathogenesis of complex diseases, such as systemic sclerosis (SSc). We performed a whole blood (WB) transcriptome analysis on RNA collected in the context of the European PRECISESADS project, aiming at characterising the pathways that differentiate SSc from controls and that are reproducible in geographically diverse populations. Methods Samples from 162 patients and 252 controls were collected in RNA stabilisers. Cases and controls were divided into a discovery (n=79+163; Southern Europe) and validation cohort (n=83+89; Central-Western Europe). RNA sequencing was performed by an Illumina assay. Functional annotations of Reactome pathways were performed with the Functional Analysis of Individual Microarray Expression (FAIME) algorithm. In parallel, immunophenotyping of 28 circulating cell populations was performed. We tested the presence of differentially expressed genes/pathways and the correlation between absolute cell counts and RNA transcripts/FAIME scores in regression models. Results significant in both populations were considered as replicated. Results Overall, 15 224 genes and 1277 functional pathways were available; of these, 99 and 225 were significant in both sets. Among replicated pathways, we found a deregulation in type-I interferon, Toll-like receptor cascade, tumour suppressor p53 protein function, platelet degranulation and activation. RNA transcripts or FAIME scores were jointly correlated with cell subtypes with strong geographical differences; neutrophils were the major determinant of gene expression in SSc-WB samples. Conclusions We discovered a set of differentially expressed genes/pathways validated in two independent sets of patients with SSc, highlighting a number of deregulated processes that have relevance for the pathogenesis of autoimmunity and SSc.EU/EFPIA/Innovative Medicines Initiative Joint Undertaking PRECISESADS 115 56

Integrative Analysis Reveals a Molecular Stratification of Systemic Autoimmune Diseases

Clinical heterogeneity, a hallmark of systemic autoimmune diseases (SADs) impedes early diagnosis and effective treatment, issues that may be addressed if patients could be grouped into a molecular defined stratification.With the aim of reclassifying SADs independently of the clinical diagnoses, unsupervised clustering of integrated whole blood transcriptome and methylome cross-sectional data of 955 patients with 7 SADs and 267 healthy controls was undertaken. In addition, an inception cohort was prospectively followed for 6 and 14 months to validate the results and analyze if cluster assignment changed or not with time.Four clusters were identified and validated. Three were pathological representing 'inflammatory', 'lymphoid', and 'interferon' patterns each including all diagnoses and defined by genetic, clinical, serological, and cellular features. A fourth cluster with no specific molecular pattern associated with low activity, and accumulated also healthy controls. A longitudinal and independent inception cohort showed a relapse-remission pattern, where patients remained in their pathological cluster, moving only to the healthy one, thus showing that with time, the molecular clusters remain stable and that single pathogenic molecular signatures characterize each individual patient.Patients with SADs can be jointly stratified into three stable disease clusters with specific molecular patterns differentiating different molecular disease mechanisms. These results have important implications for future clinical trials and the study of therapy non-responsiveness marking a paradigm shift in our view of SADs

A new molecular classification to drive precision treatment strategies in primary Sjögren’s syndrome

There is currently no approved treatment for primary Sjögren's syndrome, a disease that primarily affects adult women. The difficulty in developing effective therapies is -in part- because of the heterogeneity in the clinical manifestation and pathophysiology of the disease. Finding common molecular signatures among patient subgroups could improve our understanding of disease etiology, and facilitate the development of targeted therapeutics. Here, we report, in a cross-sectional cohort, a molecular classification scheme for Sjögren's syndrome patients based on the multi-omic profiling of whole blood samples from a European cohort of over 300 patients, and a similar number of age and gender-matched healthy volunteers. Using transcriptomic, genomic, epigenetic, cytokine expression and flow cytometry data, combined with clinical parameters, we identify four groups of patients with distinct patterns of immune dysregulation. The biomarkers we identify can be used by machine learning classifiers to sort future patients into subgroups, allowing the re-evaluation of response to treatments in clinical trials

Integrative Analysis Reveals a Molecular Stratification of Systemic Autoimmune Diseases

Objective Clinical heterogeneity, a hallmark of systemic autoimmune diseases, impedes early diagnosis and effective treatment, issues that may be addressed if patients could be classified into groups defined by molecular pattern. This study was undertaken to identify molecular clusters for reclassifying systemic autoimmune diseases independently of clinical diagnosis. Methods Unsupervised clustering of integrated whole blood transcriptome and methylome cross-sectional data on 955 patients with 7 systemic autoimmune diseases and 267 healthy controls was undertaken. In addition, an inception cohort was prospectively followed up for 6 or 14 months to validate the results and analyze whether or not cluster assignment changed over time. Results Four clusters were identified and validated. Three were pathologic, representing “inflammatory,” “lymphoid,” and “interferon” patterns. Each included all diagnoses and was defined by genetic, clinical, serologic, and cellular features. A fourth cluster with no specific molecular pattern was associated with low disease activity and included healthy controls. A longitudinal and independent inception cohort showed a relapse–remission pattern, where patients remained in their pathologic cluster, moving only to the healthy one, thus showing that the molecular clusters remained stable over time and that single pathogenic molecular signatures characterized each individual patient. Conclusion Patients with systemic autoimmune diseases can be jointly stratified into 3 stable disease clusters with specific molecular patterns differentiating different molecular disease mechanisms. These results have important implications for future clinical trials and the study of nonresponse to therapy, marking a paradigm shift in our view of systemic autoimmune diseases

Integrative epigenomics in Sjögren´s syndrome reveals novel pathways and a strong interaction between the HLA, autoantibodies and the interferon signature

Primary Sjögren's syndrome (SS) is a systemic autoimmune disease characterized by lymphocytic infiltration and damage of exocrine salivary and lacrimal glands. The etiology of SS is complex with environmental triggers and genetic factors involved. By conducting an integrated multi-omics study, we confirmed a vast coordinated hypomethylation and overexpression effects in IFN-related genes, what is known as the IFN signature. Stratified and conditional analyses suggest a strong interaction between SS-associated HLA genetic variation and the presence of Anti-Ro/SSA autoantibodies in driving the IFN epigenetic signature and determining SS. We report a novel epigenetic signature characterized by increased DNA methylation levels in a large number of genes enriched in pathways such as collagen metabolism and extracellular matrix organization. We identified potential new genetic variants associated with SS that might mediate their risk by altering DNA methylation or gene expression patterns, as well as disease-interacting genetic variants that exhibit regulatory function only in the SS population. Our study sheds new light on the interaction between genetics, autoantibody profiles, DNA methylation and gene expression in SS, and contributes to elucidate the genetic architecture of gene regulation in an autoimmune population

O31 Integrative analysis reveals a molecular stratification of systemic autoimmune diseases

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