The present profile of chronic hepatitis B virus infection highlights future challenges: An analysis of the Multicenter Italian MASTER-B cohort.

BACKGROUND: Chronic hepatitis B virus (HBV) infection remains a primary cause of morbidity and mortality worldwide. AIM: The study is aimed at updating the clinical and epidemiological profile of chronic HBV infection in Italy. METHODS: A cross-sectional multicenter prospective study enrolled consecutive HBsAg positive patients seen in 73 Italian centers in the period 2012-2015. Individual patient data were collected using an electronic platform and analyzed using standard statistical methods. RESULTS: Among 2877 HBsAg positive individuals (median age 49.8 years, 68% males), 27% were non-Italian natives (NINs); 20% had chronic infection, 58.5% chronic hepatitis and 21.5% cirrhosis. Among NINs, age was younger, male gender was less prevalent and liver disease less advanced than in Italians (all p < 0.0001). HBeAg positive cases were 23.6% among NINs vs 8.2% in Italians (p < 0.0001); HDV coinfections 11.1% vs 7.3% (p = 0.006) and HCV coinfections 2.3% vs 4.2% (p = 0.017), respectively. Anti-HDV or anti-HCV antibodies were detected more frequently in patients with cirrhosis. Fifty percent of NINs with cirrhosis were aged below 45 years. CONCLUSION: The study offers an insight into the evolving burden of chronic hepatitis B virus infection in the near future and highlights new territories for public health interventions.

Pretreatment prediction of response to ursodeoxycholic acid in primary biliary cholangitis: development and validation of the UDCA Response Score.

BACKGROUND: Treatment guidelines recommend a stepwise approach to primary biliary cholangitis: all patients begin treatment with ursodeoxycholic acid (UDCA) monotherapy and those with an inadequate biochemical response after 12 months are subsequently considered for second-line therapies. However, as a result, patients at the highest risk can wait the longest for effective treatment. We determined whether UDCA response can be accurately predicted using pretreatment clinical parameters. METHODS: We did logistic regression analysis of pretreatment variables in a discovery cohort of patients in the UK with primary biliary cholangitis to derive the best-fitting model of UDCA response, defined as alkaline phosphatase less than 1·67 times the upper limit of normal (ULN), measured after 12 months of treatment with UDCA. We validated the model in an external cohort of patients with primary biliary cholangitis and treated with UDCA in Italy. Additionally, we assessed correlations between model predictions and key histological features, such as biliary injury and fibrosis, on liver biopsy samples. FINDINGS: 2703 participants diagnosed with primary biliary cholangitis between Jan 1, 1998, and May 31, 2015, were included in the UK-PBC cohort for derivation of the model. The following pretreatment parameters were associated with lower probability of UDCA response: higher alkaline phosphatase concentration (p<0·0001), higher total bilirubin concentration (p=0·0003), lower aminotransferase concentration (p=0·0012), younger age (p<0·0001), longer interval from diagnosis to the start of UDCA treatment (treatment time lag, p<0·0001), and worsening of alkaline phosphatase concentration from diagnosis (p<0·0001). Based on these variables, we derived a predictive score of UDCA response. In the external validation cohort, 460 patients diagnosed with primary biliary cholangitis were treated with UDCA, with follow-up data until May 31, 2016. In this validation cohort, the area under the receiver operating characteristic curve for the score was 0·83 (95% CI 0·79-0·87). In 20 liver biopsy samples from patients with primary biliary cholangitis, the UDCA response score was associated with ductular reaction (r=-0·556, p=0·0130) and intermediate hepatocytes (probability of response was 0·90 if intermediate hepatocytes were absent vs 0·51 if present). INTERPRETATION: We have derived and externally validated a model based on pretreatment variables that accurately predicts UDCA response. Association with histological features provides face validity. This model provides a basis to explore alternative approaches to treatment stratification in patients with primary biliary cholangitis. FUNDING: UK Medical Research Council and University of Milan-Bicocca.

An a priori prediction model of response to peginterferon plus ribavirin dual therapy in naïve patients with genotype 1 chronic hepatitis C.

BACKGROUND: Aim was to select naïve patients with genotype 1 chronic hepatitis C having a high probability of response to Peg-interferon+ribavirin therapy. METHODS: In 1073 patients (derivation cohort), predictors of rapid and sustained virological response were identified by logistic analysis; regression coefficients were used to generate prediction models for sustained virological response. Probabilities at baseline and treatment week 4 were utilized to develop a decision rule to select patients with high likelihood of response. The model was then validated in 423 patients (validation cohort). RESULTS: In the derivation cohort, 257 achieved rapid virological response and 818 did not, with sustained virological response rates of 80.2% and 25.4%, respectively; interleukin-28B polymorphisms, fibrosis staging, gamma-glutamyl transferase, and viral load predicted sustained virological response. Assuming a <30% sustained virological response probability for not recommending Peg-interferon+ribavirin, 100 patients (25.6%) in the validation cohort were predicted a priori to fail this regimen. Assuming a ≥80% sustained virological response probability as a threshold to continue with Peg-interferon+ribavirin, 61 patients were predicted to obtain sustained virological response, and 55 of them (90.2%) eventually did. CONCLUSIONS: This model uses easily determined variables for a personalized estimate of the probability of sustained virological response with Peg-interferon+ribavirin, allowing to identify patients who may benefit from conventional therapy.

A Bayesian methodology to improve prediction of early graft loss after liver transplantation derived from the liver match study.

BACKGROUND: To generate a robust predictive model of Early (3 months) Graft Loss after liver transplantation, we used a Bayesian approach to combine evidence from a prospective European cohort (Liver-Match) and the United Network for Organ Sharing registry. METHODS: Liver-Match included 1480 consecutive primary liver transplants performed from 2007 to 2009 and the United Network for Organ Sharing a time-matched series of 9740 transplants. There were 173 and 706 Early Graft Loss, respectively. Multivariate analysis identified as significant predictors of Early Graft Loss: donor age, donation after cardiac death, cold ischaemia time, donor body mass index and height, recipient creatinine, bilirubin, disease aetiology, prior upper abdominal surgery and portal thrombosis. RESULTS: A Bayesian Cox model was fitted to Liver-Match data using the United Network for Organ Sharing findings as prior information, allowing to generate an Early Graft Loss-Donor Risk Index and an Early Graft Loss-Recipient Risk Index. A Donor-Recipient Allocation Model, obtained by adding Early Graft Loss-Donor Risk Index to Early Graft Loss-Recipient Risk Index, was then validated in a distinct United Network for Organ Sharing (year 2010) cohort including 2964 transplants. Donor-Recipient Allocation Model updating using the independent Turin Transplant Centre dataset, allowed to predict Early Graft Loss with good accuracy (c-statistic: 0.76). CONCLUSION: Donor-Recipient Allocation Model allows a reliable donor and recipient-based Early Graft Loss prediction. The Bayesian approach permits to adapt the original Donor-Recipient Allocation Model by incorporating evidence from other cohorts, resulting in significantly improved predictive capability.

Liver Match, a prospective observational cohort study on liver transplantation in Italy: study design and current practice of donor-recipient matching.

BACKGROUND: The Liver Match is an observational cohort study that prospectively enrolled liver transplantations performed at 20 out of 21 Italian Transplant Centres between June 2007 and May 2009. Aim of the study is to investigate the impact of donor/recipient matching on outcomes. In this report we describe the study methodology and provide a cross-sectional description of donor and recipient characteristics and of graft allocation. METHODS: Adult primary transplants performed with deceased heart-beating donors were included. Relevant information on donors and recipients, organ procurement and allocation were prospectively entered in an ad hoc database within the National Transplant Centre web-based Network. Data were blindly analysed by an independent Biostatistical Board. RESULTS: The study enrolled 1530 donor/recipient matches. Median donor age was 56 years. Female donors (n = 681, median 58, range 12-92 years) were older than males (n = 849, median 53, range 2-97 years, p < 0.0001). Donors older than 60 years were 42.2%, including 4.2% octogenarians. Brain death was due to non-traumatic causes in 1126 (73.6%) cases. Half of the donor population was overweight, 10.1% was obese and 7.6% diabetic. Hepatitis B core antibody (HBcAb) was present in 245 (16.0%) donors. The median Donor Risk Index (DRI) was 1.57 (>1.7 in 35.8%). The median cold ischaemia time was 7.3h (≥ 10 in 10.6%). Median age of recipients was 54 years, and 77.7% were males. Hepatocellular carcinoma (HCC) was the most frequent indication overall (44.4%), being a coindication in roughly 1/3 of cases, followed by viral cirrhosis without HCC (28.2%) and alcoholic cirrhosis without HCC (10.2%). Hepatitis C virus infection (with or without HCC) was the most frequent etiologic factor (45.9% of the whole population and 71.4% of viral-related cirrhosis), yet hepatitis B virus infection accounted for 28.6% of viral-related cirrhosis, and HBcAb positivity was found in 49.7% of recipients. The median Model for End Stage Liver Disease (MELD) at transplant was 12 in patients with HCC and 18 in those without. Multivariate analysis showed a slight but significant inverse association between DRI and MELD at transplant. CONCLUSIONS: The deceased donor population in Italy has a high-risk profile compared to other countries, mainly due to older donor age. Almost half of the grafts are transplanted in recipients with HCC. Higher risk donors tend to be preferentially allocated to recipients with HCC, who are usually less ill and older. No other relevant allocation strategy is currently adopted at national level.

ProtNet: a tool for stochastic simulations of protein interaction networks dynamics.

BACKGROUND: Protein interactions support cell organization and mediate its response to any specific stimulus. Recent technological advances have produced large data-sets that aim at describing the cell interactome. These data are usually presented as graphs where proteins (nodes) are linked by edges to their experimentally determined partners. This representation reveals that protein-protein interaction (PPI) networks, like other kinds of complex networks, are not randomly organized and display properties that are typical of "hierarchical" networks, combining modularity and local clustering to scale free topology. However informative, this representation is static and provides no clue about the dynamic nature of protein interactions inside the cell. RESULTS: To fill this methodological gap, we designed and implemented a computer model that captures the discrete and stochastic nature of protein interactions. In ProtNet, our simplified model, the intracellular space is mapped onto either a two-dimensional or a three-dimensional lattice with each lattice site having a linear size (5 nm) comparable to the diameter of an average globular protein. The protein filled lattice has an occupancy (e.g. 20%) compatible with the estimated crowding of proteins in the cell cytoplasm. Proteins or protein complexes are free to translate and rotate on the lattice that represents a sort of naïve unstructured cell (devoid of compartments). At each time step, molecular entities (proteins or complexes) that happen to be in neighboring cells may interact and form larger complexes or dissociate depending on the interaction rules defined in an experimental protein interaction network. This whole procedure can be seen as a sort of "discrete molecular dynamics" applied to interacting proteins in a cell. We have tested our model by performing different simulations using as interaction rules those derived from an experimental interactome of Saccharomyces cerevisiae (1378 nodes, 2491 edges) and we have compared the dynamics of complex formation in a two and a three dimensional lattice model. CONCLUSION: ProtNet is a cellular automaton model, where each protein molecule or complex is explicitly represented and where simple interaction rules are applied to populations of discrete particles. This tool can be used to simulate the dynamics of protein interactions in the cell.

HomoMINT: an inferred human network based on orthology mapping of protein interactions discovered in model organisms.

BACKGROUND: The application of high throughput approaches to the identification of protein interactions has offered for the first time a glimpse of the global interactome of some model organisms. Until now, however, such genome-wide approaches have not been applied to the human proteome. RESULTS: In order to fill this gap we have assembled an inferred human protein interaction network where interactions discovered in model organisms are mapped onto the corresponding human orthologs. In addition to a stringent assignment to orthology classes based on the InParanoid algorithm, we have implemented a string matching algorithm to filter out orthology assignments of proteins whose global domain organization is not conserved. Finally, we have assessed the accuracy of our own, and related, inferred networks by benchmarking them against i) an assembled experimental interactome, ii) a network derived by mining of the scientific literature and iii) by measuring the enrichment of interacting protein pairs sharing common Gene Ontology annotation. CONCLUSION: The resulting networks are named HomoMINT and HomoMINT_filtered, the latter being based on the orthology table filtered by the domain architecture matching algorithm. They contains 9749 and 5203 interactions respectively and can be analyzed and viewed in the context of the experimentally verified interactions between human proteins stored in the MINT database. HomoMINT is constantly updated to take into account the growing information in the MINT database.

Comparative interactomics.

The behavior, morphology and response to stimuli in biological systems are dictated by the interactions between their components. These interactions, as we observe them now, are therefore shaped by genetic variations and selective pressure. Similar to what has been achieved by comparing genome structures and protein sequences, we hope to obtain valuable information about systems' evolution by comparing the organization of interaction networks and by analyzing their variation and conservation. Equally, significantly we can learn whether and how to extend the network information obtained experimentally in well-characterized model systems to different organisms. We conclude from our analysis that, despite the recent completion of several high throughput experiments aimed at the description of complete interactomes, the available interaction information is not yet of sufficient coverage and quality to draw any biologically meaningful conclusion from the comparison of different interactomes. Thus, the transfer of network information obtained from simple organism to evolutionary distant species should be carried out and considered with caution. By using smaller higher-confidence datasets, a larger fraction of interactions is shown to be conserved; this suggests that with the development of more accurate experimental and informatic approaches, we will soon be in the position to study the network evolution.