Identification of novel markers for liver fibrosis in HIV/hepatitis C virus coinfected individuals using genomics-based approach

Proteomic Analysis of Hepatic Fibrosis in Human Immunodeficiency Virus-Associated Nonalcoholic Fatty Liver Disease Demonstrates Up-regulation of Immune Response and Tissue Repair Pathways

BACKGROUND

Human immunodeficiency virus (HIV)-associated nonalcoholic fatty liver disease (NAFLD) is characterized by a high prevalence of hepatic fibrosis as a strong clinical predictor of all-cause and liver-specific mortality risk.

METHODS

We leveraged data from an earlier clinical trial to define the circulating proteomic signature of hepatic fibrosis in HIV-associated NAFLD. A total of 183 plasma proteins within 2 high-multiplex panels were quantified at baseline and at 12 months (Olink Cardiovascular III; Immuno-Oncology).

RESULTS

Twenty proteins were up-regulated at baseline among participants with fibrosis stages 2-3 versus 0-1. Proteins most differentially expressed included matrix metalloproteinase 2 (P < .001), insulin-like growth factor-binding protein 7 (P = .001), and collagen α1(I) chain (P = .001). Proteins were enriched within pathways including response to tumor necrosis factor and aminopeptidase activity. Key proteins correlated directly with visceral adiposity and glucose intolerance and inversely with CD4+ T-cell count. Within the placebo-treated arm, 11 proteins differentially increased among individuals with hepatic fibrosis progression over a 12-month period (P < .05).

CONCLUSIONS

Among individuals with HIV-associated NAFLD, hepatic fibrosis was associated with a distinct proteomic signature involving up-regulation of tissue repair and immune response pathways. These findings enhance our understanding of potential mechanisms and biomarkers of hepatic fibrosis in HIV.

Semiparametric pseudoscore for regression with multidimensional but incompletely observed regressor

We study the regression f_β (Y|X,Z), where Y is the response, Z∈Rd is a vector of fully observed regressors and X is the regressor with incomplete observation. To handle missing data, maximum likelihood estimation via expectation-maximisation (EM) is the most efficient but is sensitive to the specification of the distribution of X. Under a missing at random assumption, we propose an EM-type estimation via a semiparametric pseudoscore. Like in EM, we derive the conditional expectation of the score function given Y and Z, or the mean score, over the incompletely observed units under a postulated distribution of X. Instead of directly using the 'mean score' in estimating equation, we use it as a working index to construct the semiparametric pseudoscore via nonparametric regression. Introduction of semiparametric pseudoscore into the EM framework reduces sensitivity to the specified distribution of X. It also avoids the curse of dimensionality when Z is multidimensional. The resulting regression estimator is more than doubly robust: it is consistent if either the pattern of missingness in X is correctly specified or the working index is appropriately, but not necessarily correctly, specified. It attains optimal efficiency when both conditions are satisfied. Numerical performance is explored by Monte Carlo simulations and a study on treating hepatitis C patients with HIV coinfection. Published 2017. This article is a U.S. Government work and is in the public domain in the USA.

Immuno-pathomechanism of liver fibrosis: targeting chemokine CCL2-mediated HIV:HCV nexus

Even in the era of successful combination antiretroviral therapy (cART), co-infection of Hepatitis C virus (HCV) remains one of the leading causes of non-AIDS-related mortality and morbidity among HIV-positive individuals as a consequence of accelerated liver fibrosis and end-stage liver disease (ESLD). The perturbed liver microenvironment and induction of host pro-inflammatory mediators in response to HIV and HCV infections, play a pivotal role in orchestrating the disease pathogenesis and clinical outcomes. How these viruses communicate each other via chemokine CCL2 and exploit the liver specific cellular environment to exacerbate liver fibrosis in HIV/HCV co-infection setting is a topic of intense discussion. Herein, we provide recent views and insights on potential mechanisms of CCL2 mediated immuno-pathogenesis, and HIV-HCV cross-talk in driving liver inflammation. We believe CCL2 may potentially serve an attractive target of anti-fibrotic intervention against HIV/HCV co-infection associated co-morbidities.

Biomarkers of liver fibrosis

Liver fibrosis is the final common pathway for almost all causes of chronic liver injury. Liver fibrosis is now known to be a dynamic process having significant potential for resolution. Therefore, fibrosis prediction is an essential part of the assessment and management of patients with chronic liver disease. As such, there is strong demand for reliable liver biomarkers that provide insight into disease etiology, diagnosis, therapy, and prognosis in lieu of more invasive approaches such as liver biopsy. Current diagnostic strategies range from use of serum biomarkers to more advanced imaging techniques including transient elastography and magnetic resonance imaging. In addition to these modalities, there are other approaches including the use of novel, but yet to be validated, biomarkers. In this chapter, we discuss the biomarkers of liver fibrosis including the use of invasive and noninvasive biomarkers and disease-specific biomarkers in various chronic liver diseases.

Unique and differential protein signatures within the mononuclear cells of HIV-1 and HCV mono-infected and co-infected patients

BACKGROUND

Pathogenesis of liver damage in patients with HIV and HCV co-infection is complex and multifactorial. Although global awareness regarding HIV-1/HCV co-infection is increasing little is known about the pathophysiology that mediates the rapid progression to hepatic disease in the co-infected individuals.

RESULTS

In this study, we investigated the proteome profiles of peripheral blood mononuclear cells from HIV-1 mono-, HCV mono-, and HIV-1/HCV co-infected patients. The results of high-resolution 2D gel electrophoresis and PD quest software quantitative analysis revealed that several proteins were differentially expressed in HIV-1, HCV, and HIV-1/HCV co-infection. Liquid chromatography-mass spectrometry and Mascot database matching (LC-MS/MS analysis) successfully identified 29 unique and differentially expressed proteins. These included cytoskeletal proteins (tropomyosin, gelsolin, DYPLSL3, DYPLSL4 and profilin-1), chaperones and co-chaperones (HSP90-beta and stress-induced phosphoprotein), metabolic and pre-apoptotic proteins (guanosine triphosphate [GTP]-binding nuclear protein Ran, the detoxifying enzyme glutathione S-transferase (GST) and Rho GDP-dissociation inhibitor (Rho-GDI), proteins involved in cell prosurvival mechanism, and those involved in matrix synthesis (collagen binding protein 2 [CBP2]). The six most significant and relevant proteins were further validated in a group of mono- and co-infected patients (n = 20) at the transcriptional levels.

CONCLUSIONS

The specific pro- and anti- apoptotic protein signatures revealed in this study could facilitate the understanding of apoptotic and protective immune-mediated mechanisms underlying HIV-1 and HCV co-infection and their implications on liver disease progression in co-infected patients.

Investigation of plasma biomarkers in HIV-1/HCV mono- and coinfected individuals by multiplex iTRAQ quantitative proteomics

The analysis of plasma samples from HIV-1/HCV mono- and coinfected individuals by quantitative proteomics is an efficient strategy to investigate changes in protein abundances and to characterize the proteins that are the effectors of cellular functions involved in viral pathogenesis. In this study, the infected and healthy plasma samples (in triplicate) were treated with ProteoMiner beads to equalize protein concentrations and subjected to 4-plex iTRAQ labeling and liquid chromatography/mass spectrometry (LC-MS/MS) analysis. A total of 70 proteins were identified with high confidence in the triplicate analysis of plasma proteins and 65% of the proteins were found to be common among the three replicates. Apolipoproteins and complement proteins are the two major classes of proteins that exhibited differential regulation. The results of quantitative analysis revealed that APOA2, APOC2, APOE, C3, HRG proteins were upregulated in the plasma of all the three HIV-1 mono-, HCV mono-, and coinfected patient samples compared to healthy control samples. Ingenuity pathway analysis (IPA) of the upregulated proteins revealed that they are implicated in the hepatic lipid metabolism, inflammation, and acute-phase response signaling pathways. Thus, we identified several differentially regulated proteins in HIV-1/HCV mono and coinfected plasma samples that may be potential biomarkers for liver disease.

Genomics and proteomics in liver fibrosis and cirrhosis

Genomics and proteomics have become increasingly important in biomedical science in the past decade, as they provide an opportunity for hypothesis-free experiments that can yield major insights not previously foreseen when scientific and clinical questions are based only on hypothesis-driven approaches. Use of these tools, therefore, opens new avenues for uncovering physiological and pathological pathways. Liver fibrosis is a complex disease provoked by a range of chronic injuries to the liver, among which are viral hepatitis, (non-) alcoholic steatohepatitis and autoimmune disorders. Some chronic liver patients will never develop fibrosis or cirrhosis, whereas others rapidly progress towards cirrhosis in a few years. This variety can be caused by disease-related factors (for example, viral genotype) or host-factors (genetic/epigenetic). It is vital to establish accurate tools to identify those patients at highest risk for disease severity or progression in order to determine who are in need of immediate therapies. Moreover, there is an urgent imperative to identify non-invasive markers that can accurately distinguish mild and intermediate stages of fibrosis. Ideally, biomarkers can be used to predict disease progression and treatment response, but these studies will take many years due to the requirement for lengthy follow-up periods to assess outcomes. Current genomic and proteomic research provides many candidate biomarkers, but independent validation of these biomarkers is lacking, and reproducibility is still a key concern. Thus, great opportunities and challenges lie ahead in the field of genomics and proteomics, which, if successful, could transform the diagnosis and treatment of chronic fibrosing liver diseases.

HIV/HCV co-infection: pathogenesis, clinical complications, treatment, and new therapeutic technologies

World-wide, hepatitis C virus (HCV) accounts for approximately 130 million chronic infections, with an overall 3% prevalence. Four to 5 million persons are co-infected with HIV. It is well established that HIV has a negative impact on the natural history of HCV, including a higher rate of viral persistence, increased viral load, and more rapid progression to fibrosis, end-stage liver disease, and death. Whether HCV has a negative impact on HIV disease progression continues to be debated. However, following the introduction of effective combination antiretroviral therapy, the survival of coinfected individuals has significantly improved and HCV-associated diseases have emerged as the most important co-morbidities. In this review, we summarize the newest studies regarding the pathogenesis of HIV/HCV coinfection, including effects of coinfection on HIV disease progression, HCV-associated liver disease, the immune system, kidney and cardiovascular disease, and neurologic status; and effectiveness of current anti-HIV and HCV therapies and proposed new treatment strategies.

Care of hepatitis C virus infection in human immunodeficiency virus-infected patients: modifications in three consecutive large surveys between 2004 and 2009

BACKGROUND & AIMS

To analyze the care of HCV infection in HIV-HCV coinfected patients and its progression between 2004 and 2009.

METHODS

Three hundred eighty HIV-HCV coinfected patients were prospectively included from November 22 to 29, 2004 (2004 survey), 416 patients from April 3 to 10, 2006 (2006 survey), and 419 patients from June 15 to 22, 2009 (2009 survey).

RESULTS

The rate of liver biopsy decreased (14% vs. 38% vs. 56%), while the use of non-invasive liver damage tests increased (47% vs. 24% vs. ND) in the 2009, 2006, and 2004 surveys, respectively. The rate of patients that had never been treated for HCV infection progressively decreased in the 2009, 2006, and 2004 surveys (37%, 42%, and 54%). The main reasons for HCV non-treatment changed as HCV treatment was deemed less questionable and the lack of liver biopsy became a very rare reason (6%, 18%, and 34%). Among patients treated for HCV infection, the rate of sustained virological response increased (49%, 29%, and 29%). The main factors independently associated with HCV treatment were a liver fibrosis score > or =F2 (odds ratio=3.5; 95% CI 2.1-5.7), a liver biopsy activity grade > or =A2 (2.7; 1.4-5.3), a CD4 cell count > or =350 ml (2.7; 1.6-4.4), European origin (2.1; 1.3-3.4), daily alcohol consumption<30 g (2.1; 1.2-3.8), and male gender (2.0; 1.2-3.3).

CONCLUSION

Compared to the 2004 and 2006 surveys, the 2009 coinfected patients had liver damage assessment more frequently, more patients had received HCV treatment and more patients had achieved a sustained virological response.

Introduction to the development and validation of predictive biomarker models from high-throughput data sets

High-throughput technologies can routinely assay biological or clinical samples and produce wide data sets where each sample is associated with tens of thousands of measurements. Such data sets can be mined to discover biomarkers and develop statistical models capable of predicting an endpoint of interest from data measured in the samples. The field of biomarker model development combines methods from statistics and machine learning to develop and evaluate predictive biomarker models. In this chapter, we discuss the computational steps involved in the development of biomarker models designed to predict information about individual samples and review approaches often used to implement each step. A practical example of biomarker model development in a large gene expression data set is presented. This example leverages BDVal, a suite of biomarker model development programs developed as an open-source project (see http://bdval.org /).

Systematic review: non-invasive methods of fibrosis analysis in chronic hepatitis C

BACKGROUND

Accurate determination of the presence and degree of liver fibrosis is essential for prognosis and for planning treatment of patients with chronic hepatitis C virus (HCV). Non-invasive methods of assessing fibrosis have been developed to reduce the need for biopsy.

AIM

To perform a review of these non-invasive measures and their ability to replace biopsy for assessing hepatic fibrosis in patients with chronic HCV.

METHODS

A systematic review of PUBMED and EMBASE was performed through 2008 using the following search terms: HCV, liver, elastography, hepatitis, Fibroscan, SPECT, noninvasive liver fibrosis, ultrasonography, Doppler, MRI, Fibrotest, Fibrosure, Actitest, APRI, Forns and breath tests, alone or in combination.

RESULTS

We identified 151 studies: 87 using biochemical, 57 imaging and seven breath tests either alone or in combination.

CONCLUSIONS

Great strides are being made in the development of accurate non-invasive methods for determination of fibrosis. Although no single non-invasive test or model developed to date can match that information obtained from actual histology (i.e. inflammation, fibrosis, steatosis), combinations of two modalities of non-invasive methods can reliably differentiate between minimal and significant fibrosis, and thereby avoid liver biopsy in a significant percentage of patients.