Autoantibodies targeting TLR and SMAD pathways define new subgroups in systemic lupus erythematosus.

OBJECTIVES: The molecular targets of the vast majority of autoantibodies in systemic lupus erythematosus (SLE) are unknown. We set out to identify novel autoantibodies in SLE to improve diagnosis and identify subgroups of SLE individuals. METHODS: A baculovirus-insect cell expression system was used to create an advanced protein microarray with 1543 full-length human proteins expressed with a biotin carboxyl carrier protein (BCCP) folding tag, to enrich for correctly folded proteins. Sera from a discovery cohort of UK and US SLE individuals (n = 186) and age/ethnicity matched controls (n = 188) were assayed using the microarray to identify novel autoantibodies. Autoantibodies were validated in a second validation cohort (91 SLE, 92 controls) and a confounding rheumatic disease cohort (n = 92). RESULTS: We confirmed 68 novel proteins as autoantigens in SLE and 11 previous autoantigens in both cohorts (FDR<0.05). Using hierarchical clustering and principal component analysis, we observed four subgroups of SLE individuals associated with four corresponding clusters of functionally linked autoantigens. Two clusters of novel autoantigens revealed distinctive networks of interacting proteins: SMAD2, SMAD5 and proteins linked to TGF-ß signalling; and MyD88 and proteins involved in TLR signalling, apoptosis, NF-κB regulation and lymphocyte development. The autoantibody clusters were associated with different patterns of organ involvement (arthritis, pulmonary, renal and neurological). A panel of 26 autoantibodies, which accounted for four SLE clusters, showed improved diagnostic accuracy compared to conventional antinuclear antibody and anti-dsDNA antibody assays. CONCLUSIONS: These data suggest that the novel SLE autoantibody clusters may be of prognostic utility for predicting organ involvement in SLE patients and for stratifying SLE patients for specific therapies.

Rapid Whole-Genome Sequencing of Mycobacterium tuberculosis Isolates Directly from Clinical Samples.

The rapid identification of antimicrobial resistance is essential for effective treatment of highly resistant Mycobacterium tuberculosis. Whole-genome sequencing provides comprehensive data on resistance mutations and strain typing for monitoring transmission, but unlike for conventional molecular tests, this has previously been achievable only from cultures of M. tuberculosis. Here we describe a method utilizing biotinylated RNA baits designed specifically for M. tuberculosis DNA to capture full M. tuberculosis genomes directly from infected sputum samples, allowing whole-genome sequencing without the requirement of culture. This was carried out on 24 smear-positive sputum samples, collected from the United Kingdom and Lithuania where a matched culture sample was available, and 2 samples that had failed to grow in culture. M. tuberculosis sequencing data were obtained directly from all 24 smear-positive culture-positive sputa, of which 20 were of high quality (>20× depth and >90% of the genome covered). Results were compared with those of conventional molecular and culture-based methods, and high levels of concordance between phenotypical resistance and predicted resistance based on genotype were observed. High-quality sequence data were obtained from one smear-positive culture-negative case. This study demonstrated for the first time the successful and accurate sequencing of M. tuberculosis genomes directly from uncultured sputa. Identification of known resistance mutations within a week of sample receipt offers the prospect for personalized rather than empirical treatment of drug-resistant tuberculosis, including the use of antimicrobial-sparing regimens, leading to improved outcomes.

Seromic analysis of antibody responses in non-small cell lung cancer patients and healthy donors using conformational protein arrays.

Analysis of antibody responses to self-antigens has driven the development of the field of tumor immunology, with the identification of many protein targets found in cancer but with limited expression in normal tissues. Protein microarray technologies offer an unprecedented platform to assay the serological response of cancer patients to tumor antigens in a comprehensive fashion, against many proteins simultaneously. We developed an array containing 329 full-length proteins, originally identified as antigenic in various cancer patients by serological expression cloning (SEREX), that were immobilized as folded, functional products accessible for antibody binding. To validate the use of these microarrays, we selected 31 sera from non-small cell lung cancer patients previously known to react to the following antigens by ELISA: LAGE-1/CTAG2, MAGEA4, TP53, SSX and SOX2. These sera were compared with 22 sera from healthy donors for reactivity against a series of antigens present on microarrays. The sensitivity and specificity of the arrays compared favorably with standard ELISA techniques (94% concordance). We present here a stringent strategy for data analysis and normalization that is applicable to protein arrays in general, and describe findings suggesting that this approach is suitable for defining potential antigenic targets for cancer vaccine development, serum antibody signatures with clinical value, characterization of predictive serum markers for experimental therapeutics, and eventually for the serological definition of the cancer proteome (seromics).