Integrative modeling of transcriptional regulation in response to antirheumatic therapy

<p>Abstract</p> <p>Background</p> <p>The investigation of gene regulatory networks is an important issue in molecular systems biology and significant progress has been made by combining different types of biological data. The purpose of this study was to characterize the transcriptional program induced by etanercept therapy in patients with rheumatoid arthritis (RA). Etanercept is known to reduce disease symptoms and progression in RA, but the underlying molecular mechanisms have not been fully elucidated.</p> <p>Results</p> <p>Using a DNA microarray dataset providing genome-wide expression profiles of 19 RA patients within the first week of therapy we identified significant transcriptional changes in 83 genes. Most of these genes are known to control the human body's immune response. A novel algorithm called TILAR was then applied to construct a linear network model of the genes' regulatory interactions. The inference method derives a model from the data based on the Least Angle Regression while incorporating DNA-binding site information. As a result we obtained a scale-free network that exhibits a self-regulating and highly parallel architecture, and reflects the pleiotropic immunological role of the therapeutic target TNF-alpha. Moreover, we could show that our integrative modeling strategy performs much better than algorithms using gene expression data alone.</p> <p>Conclusion</p> <p>We present TILAR, a method to deduce gene regulatory interactions from gene expression data by integrating information on transcription factor binding sites. The inferred network uncovers gene regulatory effects in response to etanercept and thus provides useful hypotheses about the drug's mechanisms of action.</p

Epitope Structure of the Carbohydrate Recognition Domain of Asialoglycoprotein Receptor to a Monoclonal Antibody Revealed by High-Resolution Proteolytic Excision Mass Spectrometry

Recent studies suggest that the H1 subunit of the carbohydrate recognition domain (H1CRD) of the asialoglycoprotein receptor is used as entry site into hepatocytes by hepatitis A and B virus, and Marburg virus. Thus, molecules binding specifically to the CRD might exert inhibition towards these diseases by blocking the virus entry site. We report here the identification of the epitope structure of H1CRD to a monoclonal antibody by proteolytic epitope excision of the immune complex and high resolution MALDI-FTICR mass spectrometry. As a prerequisite of the epitope determination, the primary structure of the H1CRD antigen was characterised by ESI-FTICR-MS of the intact protein and by LCMS/MS of tryptic digest mixtures. Molecular mass determination and proteolytic fragments provided the identification of 2 intra-molecular disulfide bridges (7 Cys residues), and a Cysmercaptoethanol adduct formed by treatment with ß-mercaptoethanol during protein extraction. The H1CRD antigen binds to the monoclonal antibody in both native and Cysalkylated form. For identification of the epitope, the antibody was immobilized on N-hydroxysuccinimide activated Sepharose. Epitope- excision and - extraction with trypsin and FTICR-MS of affinity-bound peptides provided the identification of two specific epitope peptides, (5-16) and (17-23) which showed high affinity to the antibody. Affinity studies of the synthetic epitope peptides revealed independent binding of each peptide to the antibody

In silico Epitope Mapping of Glucose-6-Phosphate Isomerase: A Rheumatoid Arthritis Autoantigen

Rheumatoid arthritis-like symptoms can be initiated experimentally in naive K/BxN mice by simultaneously administering the two monoclonal antibodies 11H3 and 46H9. Both antibodies specifically recognize Glucose-6-Phosphate Isomerase (GPI), a known auto antigen in RA patients. Amino acid sequences of the Fv parts of the antibodies were determined by translating the respective hybridoma DNA sequences and served for threedimensional structure modeling of the paratope regions. In silico docking of both Fv antibody structure models to the X-ray structures of the homodimeric murine GPI as well as to the homodimeric human GPI predicted the murine epitope of the 11H3 antibodies to comprise partial amino acid sequences QRVRSGDWKGYTGKS (aa134-148) and AAKDPSAVAK (aa232-241), generating an assembled (conformational) epitope. The 11H3 epitope on human GPI encompasses the matching partial amino acid sequences QRVRSGDWKGYTGKT (aa134-148) and AAKDPSAVAK (aa232-241). The epitope of the 46H9 antibody was determined to consist of the partial murine GPI amino acid sequence RKELQAAGKSPEDLEK (aa446-461) and the human GPI amino acid sequence RKELQAAGKSPEDLER (aa446-461), respectively, resembling consecutive (linear) epitopes. The predicted epitopes were verified by mass spectrometric epitope mapping using synthetic epitope peptides. Peptide QRVRSGDWKGYTGKS[GSMSGS] AAKDPSAAK included a small spacer sequence in between the epitope sequences, mimicking the assembled epitope for the 11H3 antibody. The peptide RKELQAAGKSPEDLEK represented the consecutive epitope for the 46H9 antibody. The determined B-cell epitopes of GPI and their interactions with the monoclonal antibodies provide a detailed structural understanding of immunological disease onset mechanisms in a mouse model of rheumatoid arthritis