Increased levels of XPA might be the basis of cisplatin resistance in germ cell tumours

Background

Germ cell tumours (GCTs) represent a highly curable malignity as they respond well to cisplatin (CDDP)-based chemotherapy. Nevertheless, a small proportion of GCT patients relapse or do not respond to therapy. As this might be caused by an increased capacity to repair CDDP-induced DNA damage, identification of DNA repair biomarkers predicting inadequate or aberrant response to CDDP, and thus poor prognosis for GCT patients, poses a challenge. The objective of this study is to examine the expression levels of the key nucleotide excision repair (NER) factors, XPA, ERCC1 and XPF, in GCT patients and cell lines.

Methods

Two hundred seven GCT patients’ specimens with sufficient follow-up clinical-pathological data and pairwise combinations of CDDP-resistant and -sensitive GCT cell lines were included. Immunohistochemistry was used to detect the ERCC1, XPF and XPA protein expression levels in GCT patients’ specimen and Western blot and qRT-PCR examined the protein and mRNA expression levels in GCT cell lines.

Results

GCT patients with low XPA expression had significantly better overall survival than patients with high expression (hazard ratio = 0.38, 95% confidence interval: 0.12–1.23, p = 0.0228). In addition, XPA expression was increased in the non-seminomatous histological subtype, IGCCCG poor prognosis group, increasing S stage, as well as the presence of lung, liver and non-pulmonary visceral metastases. Importantly, a correlation between inadequate or aberrant CDDP response and XPA expression found in GCT patients was also seen in GCT cell lines.

Conclusions

XPA expression is an additional independent prognostic biomarker for stratifying GCT patients, allowing for improvements in decision-making on treatment for those at high risk of refractoriness or relapse. In addition, it could represent a novel therapeutic target in GCTs.

Design, construction and evaluation of multi-epitope antigens for diagnosis of Lyme disease

INTRODUCTION

Introduction and objective. Lyme disease (LD) is the most common vector-borne disease in the temperate zone of the Northern Hemisphere. Diagnosis of LD is mainly based on clinical symptoms supported with serology (detection of anti-Borrelia antibodies) and is often misdiagnosed in areas of endemicity.

MATERIAL AND METHODS

In this study, the chimeric proteins (A/C-2, A/C-4 and A/C-7.1) consisting of B-cell epitopes of outer surface proteins OspA and OspC from Borrelia genospecies prevalent in Eastern Slovakia, were designed, over-expressed in E. coli, and used to detect specific anti-Borrelia antibodies in serologically characterized sera from patients with Lyme-like symptoms to evaluate their diagnostic potential.

RESULTS

Results showed that chimeras vary in their immuno-reactivity when tested with human sera. Compared with the results obtained from a two-tier test, the application of recombinant multi-epitope chimeric proteins as diagnosis antigens, produced fair agreement in the case of A/C-2 (0.20<κ<0.40) and good agreement (0.60<κ<0.80) when A/C-7.1 was used as capture antigen. Chimera A/C-4 were excluded from further study due to loss of reactivity with OspA-specific antibodies.

CONCLUSIONS

The combination of specific B-cell epitopes from OspA and OspC proteins may improve the diagnostic accuracy of serologic assays, but further studies are required to address this hypothesis.

An Introduction to B-Cell Epitope Mapping and In Silico Epitope Prediction

Identification of B-cell epitopes is a fundamental step for development of epitope-based vaccines, therapeutic antibodies, and diagnostic tools. Epitope-based antibodies are currently the most promising class of biopharmaceuticals. In the last decade, in-depth in silico analysis and categorization of the experimentally identified epitopes stimulated development of algorithms for epitope prediction. Recently, various in silico tools are employed in attempts to predict B-cell epitopes based on sequence and/or structural data. The main objective of epitope identification is to replace an antigen in the immunization, antibody production, and serodiagnosis. The accurate identification of B-cell epitopes still presents major challenges for immunologists. Advances in B-cell epitope mapping and computational prediction have yielded molecular insights into the process of biorecognition and formation of antigen-antibody complex, which may help to localize B-cell epitopes more precisely. In this paper, we have comprehensively reviewed state-of-the-art experimental methods for B-cell epitope identification, existing databases for epitopes, and novel in silico resources and prediction tools available online. We have also elaborated new trends in the antibody-based epitope prediction. The aim of this review is to assist researchers in identification of B-cell epitopes.