Identification of the proteins of Borrelia garinii interacting with human brain microvascular endothelial cells.

Lyme borreliosis is one of the major tick-borne diseases in Europe. Events of the translocation of Borrelia across the blood-brain barrier (BBB) involve multiple interactions between borrelial surface proteins and receptors on the brain microvascular endothelial cells (hBMECs). In this study, we aimed to identify proteins of Borrelia that plausibly interact with hBMECs. The surface proteome of live Borrelia (a neuroinvasive strain of B. garinii) was crosslinked with biotin prior to its incubation with hBMECs. The interacting proteins were recovered by affinity purification, followed by SWATH-MS. Twenty-four interacting candidates were grouped into outer membrane proteins (n = 12) and inner membrane proteins (n = 12) based on the subcellular location as per the predictions of LocateP. Other algorithms like TMHMM 2.0 and LipoP, ontology search and literature review were subsequently applied to each of the identified protein candidates to shortlist the most probable interactors. Six proteins namely, LysM domain protein, BESBP-5, Antigen S1, CRASP-1 (Bg071), Erp23 protein and Mlp family Lipoprotein were selected to produce their recombinant forms and experimentally validate their interaction with hBMECs. All the recombinant proteins interacted with hBMECs, in ELISA and immunocytochemistry. We present here a high-throughput approach of generating a dataset of plausible borrelial ligands followed by a systematic bioinformatic pipeline to categorize the proteins for experimental validation.

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.

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.