Development of machine learning support for reading whole body diffusion-weighted MRI (WB-MRI) in myeloma for the detection and quantification of the extent of disease before and after treatment (MALIMAR): protocol for a cross-sectional diagnostic test accuracy study.

INTRODUCTION: Whole-body MRI (WB-MRI) is recommended by the National Institute of Clinical Excellence as the first-line imaging tool for diagnosis of multiple myeloma. Reporting WB-MRI scans requires expertise to interpret and can be challenging for radiologists who need to meet rapid turn-around requirements. Automated computational tools based on machine learning (ML) could assist the radiologist in terms of sensitivity and reading speed and would facilitate improved accuracy, productivity and cost-effectiveness. The MALIMAR study aims to develop and validate a ML algorithm to increase the diagnostic accuracy and reading speed of radiological interpretation of WB-MRI compared with standard methods. METHODS AND ANALYSIS: This phase II/III imaging trial will perform retrospective analysis of previously obtained clinical radiology MRI scans and scans from healthy volunteers obtained prospectively to implement training and validation of an ML algorithm. The study will comprise three project phases using approximately 633 scans to (1) train the ML algorithm to identify active disease, (2) clinically validate the ML algorithm and (3) determine change in disease status following treatment via a quantification of burden of disease in patients with myeloma. Phase 1 will primarily train the ML algorithm to detect active myeloma against an expert assessment ('reference standard'). Phase 2 will use the ML output in the setting of radiology reader study to assess the difference in sensitivity when using ML-assisted reading or human-alone reading. Phase 3 will assess the agreement between experienced readers (with and without ML) and the reference standard in scoring both overall burden of disease before and after treatment, and response. ETHICS AND DISSEMINATION: MALIMAR has ethical approval from South Central-Oxford C Research Ethics Committee (REC Reference: 17/SC/0630). IRAS Project ID: 233501. CPMS Portfolio adoption (CPMS ID: 36766). Participants gave informed consent to participate in the study before taking part. MALIMAR is funded by National Institute for Healthcare Research Efficacy and Mechanism Evaluation funding (NIHR EME Project ID: 16/68/34). Findings will be made available through peer-reviewed publications and conference dissemination. TRIAL REGISTRATION NUMBER: NCT03574454

Interaction between NOD2 and CARD9 involves the NOD2 NACHT and the linker region between the NOD2 CARDs and NACHT domain.

NOD2 activation by muramyl dipeptide causes a proinflammatory immune response in which the adaptor protein CARD9 works synergistically with NOD2 to drive p38 and c-Jun N-terminal kinase (JNK) signalling. To date the nature of the interaction between NOD2 and CARD9 remains undetermined. Here we show that this interaction is not mediated by the CARDs of NOD2 and CARD9 as previously suggested, but that NOD2 possesses two interaction sites for CARD9; one in the CARD-NACHT linker and one in the NACHT itself.This work was funded by a Wellcome Trust Career Development Fellowship (WT085090MA) to TPM and a Medical Research Council grant (U117565398) to KR. RP and JPB were supported by BBSRC Doctoral Training Grants.This is the final published version of the article, which can also be found on the publisher's website at: http://www.sciencedirect.com/science/article/pii/S0014579314004979

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

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