100 years post-insulin: immunotherapy as the next frontier in type 1 diabetes.

Type 1 diabetes (T1D) is an autoimmune disease characterised by T cell-mediated destruction of the insulin-producing β cells in the pancreas. Similar to other autoimmune diseases, the incidence of T1D is increasing globally. The discovery of insulin 100 years ago dramatically changed the outlook for people with T1D, preventing this from being a fatal condition. As we celebrate the centenary of this milestone, therapeutic options for T1D are once more at a turning point. Years of effort directed at developing immunotherapies are finally starting to pay off, with signs of progress in new onset and even preventative settings. Here, we review a selection of immunotherapies that have shown promise in preserving β cell function and highlight future considerations for immunotherapy in the T1D setting

Emerging concepts in the pathogenesis of antineutrophil cytoplasmic antibody-associated vasculitis.

PURPOSE OF REVIEW: Antineutrophil cytoplasmic antibodies (ANCAs) remain central to our current understanding of the pathogenesis of ANCA-associated vasculitis (AAV), and this review considers recent developments in the context of four key questions: are there targets for ANCA beyond myeloperoxidase (MPO) and proteinase 3 (PR3); are all ANCA pathogenic; how are ANCAs generated; and how do ANCA cause disease? RECENT FINDINGS: B-cell epitope mapping raises the possibility that only a subset of ANCA may be pathogenic. Anti-lysosomal-associated membrane protein 2 autoantibodies have recently emerged as a novel form of ANCA and can be found in anti-MPO and anti-PR3 negative disease. These also provide recent evidence for molecular mimicry in the pathogenesis of AAV, but a definitive proof in human AAV remains elusive. Neutrophil extracellular traps may represent an important mechanism by which MPO and PR3 are taken up by dendritic cells for presentation to the adaptive immune system, and the role of the alternative pathway of complement in AAV has recently been emphasized, with therapeutic implications. SUMMARY: Our current understanding of the pathogenesis of AAV not only reinforces the central role of neutrophils but also provides a sound rationale for B-cell and complement-directed therapies.This work was supported by the Wellcome Trust through a Translational Medicine and Therapeutics PhD Studentship, the National Institute of Health Research Cambridge Biomedical Research Centre and an MRC Programme grant. The Cambridge Institute for Medical Research is in receipt of Wellcome Trust Strategic Award 079895.This is the accepted manuscript. The final version is available from Wolters Kluwer at http://journals.lww.com/co-rheumatology/pages/articleviewer.aspx?year=2015&issue=03000&article=00016&type=abstract

100 years post-insulin: immunotherapy as the next frontier in type 1 diabetes.

Type 1 diabetes (T1D) is an autoimmune disease characterised by T cell-mediated destruction of the insulin-producing β cells in the pancreas. Similar to other autoimmune diseases, the incidence of T1D is increasing globally. The discovery of insulin 100 years ago dramatically changed the outlook for people with T1D, preventing this from being a fatal condition. As we celebrate the centenary of this milestone, therapeutic options for T1D are once more at a turning point. Years of effort directed at developing immunotherapies are finally starting to pay off, with signs of progress in new onset and even preventative settings. Here, we review a selection of immunotherapies that have shown promise in preserving β cell function and highlight future considerations for immunotherapy in the T1D setting

The Contribution of Transcriptomics to Biomarker Development in Systemic Vasculitis and SLE.

A small but increasing number of gene expression based biomarkers are becoming available for routine clinical use, principally in oncology and transplantation. These underscore the potential of gene expression arrays and RNA sequencing for biomarker development, but this potential has not yet been fully realized and most candidates do not progress beyond the initial report. The first part of this review examines the process of gene expression- based biomarker development, highlighting how systematic biases and confounding can significantly skew study outcomes. Adequate validation in an independent cohort remains the single best means of protecting against these concerns. The second part considers gene-expression based biomarkers in Systemic Lupus Erythematosus (SLE) and systemic vasculitis. The type 1 interferon inducible gene signature remains by far the most studied in autoimmune rheumatic disease. While initially presented as an objective, blood-based biomarker of active SLE, subsequent research has shown that it is not specific to SLE and that its association with disease activity is considerably more nuanced than first thought. Nonetheless, it is currently under evaluation in ongoing trials of anti-interferon therapy. Other candidate markers of note include a prognostic CD8+ T-cell gene signature validated in SLE and ANCA-associated vasculitis, and a disease activity biomarker for SLE derived from modules of tightly correlated genes.This is the author accepted manuscript. The final version is available from Bentham Science via http://dx.doi.org/10.2174/138161282166615031313025

Comparison of gene expression microarray data with count-based RNA measurements informs microarray interpretation.

BACKGROUND: Although numerous investigations have compared gene expression microarray platforms, preprocessing methods and batch correction algorithms using constructed spike-in or dilution datasets, there remains a paucity of studies examining the properties of microarray data using diverse biological samples. Most microarray experiments seek to identify subtle differences between samples with variable background noise, a scenario poorly represented by constructed datasets. Thus, microarray users lack important information regarding the complexities introduced in real-world experimental settings. The recent development of a multiplexed, digital technology for nucleic acid measurement enables counting of individual RNA molecules without amplification and, for the first time, permits such a study. RESULTS: Using a set of human leukocyte subset RNA samples, we compared previously acquired microarray expression values with RNA molecule counts determined by the nCounter Analysis System (NanoString Technologies) in selected genes. We found that gene measurements across samples correlated well between the two platforms, particularly for high-variance genes, while genes deemed unexpressed by the nCounter generally had both low expression and low variance on the microarray. Confirming previous findings from spike-in and dilution datasets, this "gold-standard" comparison demonstrated signal compression that varied dramatically by expression level and, to a lesser extent, by dataset. Most importantly, examination of three different cell types revealed that noise levels differed across tissues. CONCLUSIONS: Microarray measurements generally correlate with relative RNA molecule counts within optimal ranges but suffer from expression-dependent accuracy bias and precision that varies across datasets. We urge microarray users to consider expression-level effects in signal interpretation and to evaluate noise properties in each dataset independently

Transcript analysis reveals a specific HOX signature associated with positional identity of human endothelial cells.

The endothelial cell has a remarkable ability for sub-specialisation, adapted to the needs of a variety of vascular beds. The role of developmental programming versus the tissue contextual environment for this specialization is not well understood. Here we describe a hierarchy of expression of HOX genes associated with endothelial cell origin and location. In initial microarray studies, differential gene expression was examined in two endothelial cell lines: blood derived outgrowth endothelial cells (BOECs) and pulmonary artery endothelial cells. This suggested shared and differential patterns of HOX gene expression between the two endothelial lines. For example, this included a cluster on chromosome 2 of HOXD1, HOXD3, HOXD4, HOXD8 and HOXD9 that was expressed at a higher level in BOECs. Quantative PCR confirmed the higher expression of these HOXs in BOECs, a pattern that was shared by a variety of microvascular endothelial cell lines. Subsequently, we analysed publically available microarrays from a variety of adult cell and tissue types using the whole "HOX transcriptome" of all 39 HOX genes. Using hierarchical clustering analysis the HOX transcriptome was able to discriminate endothelial cells from 61 diverse human cell lines of various origins. In a separate publically available microarray dataset of 53 human endothelial cell lines, the HOX transcriptome additionally organized endothelial cells related to their organ or tissue of origin. Human tissue staining for HOXD8 and HOXD9 confirmed endothelial expression and also supported increased microvascular expression of these HOXs. Together these observations suggest a significant involvement of HOX genes in endothelial cell positional identity

Gene expression profiling of CD8+ T cells predicts prognosis in patients with Crohn disease and ulcerative colitis.

Crohn disease (CD) and ulcerative colitis (UC) are increasingly common, chronic forms of inflammatory bowel disease. The behavior of these diseases varies unpredictably among patients. Identification of reliable prognostic biomarkers would enable treatment to be personalized so that patients destined to experience aggressive disease could receive appropriately potent therapies from diagnosis, while those who will experience more indolent disease are not exposed to the risks and side effects of unnecessary immunosuppression. Using transcriptional profiling of circulating T cells isolated from patients with CD and UC, we identified analogous CD8+ T cell transcriptional signatures that divided patients into 2 otherwise indistinguishable subgroups. In both UC and CD, patients in these subgroups subsequently experienced very different disease courses. A substantially higher incidence of frequently relapsing disease was experienced by those patients in the subgroup defined by elevated expression of genes involved in antigen-dependent T cell responses, including signaling initiated by both IL-7 and TCR ligation - pathways previously associated with prognosis in unrelated autoimmune diseases. No equivalent correlation was observed with CD4+ T cell gene expression. This suggests that the course of otherwise distinct autoimmune and inflammatory conditions may be influenced by common pathways and identifies what we believe to be the first biomarker that can predict prognosis in both UC and CD from diagnosis, a major step toward personalized therapy

Transcriptional networks in at-risk individuals identify signatures of type 1 diabetes progression.

Type 1 diabetes (T1D) is a disease of insulin deficiency that results from autoimmune destruction of pancreatic islet β cells. The exact cause of T1D remains unknown, although asymptomatic islet autoimmunity lasting from weeks to years before diagnosis raises the possibility of intervention before the onset of clinical disease. The number, type, and titer of islet autoantibodies are associated with long-term disease risk but do not cause disease, and robust early predictors of individual progression to T1D onset remain elusive. The Environmental Determinants of Diabetes in the Young (TEDDY) consortium is a prospective cohort study aiming to determine genetic and environmental interactions causing T1D. Here, we analyzed longitudinal blood transcriptomes of 2013 samples from 400 individuals in the TEDDY study before both T1D and islet autoimmunity. We identified and interpreted age-associated gene expression changes in healthy infancy and age-independent changes tracking with progression to both T1D and islet autoimmunity, beginning before other evidence of islet autoimmunity was present. We combined multivariate longitudinal data in a Bayesian joint model to predict individual risk of T1D onset and validated the association of a natural killer cell signature with progression and the model's predictive performance on an additional 356 samples from 56 individuals in the independent Type 1 Diabetes Prediction and Prevention study. Together, our results indicate that T1D is characterized by early and longitudinal changes in gene expression, informing the immunopathology of disease progression and facilitating prediction of its course.The TEDDY Study is funded by U01 DK63829, U01 DK63861, U01 DK63821, U01 DK63865, U01 DK63863, U01 DK63836, U01 DK63790, UC4 DK63829, UC4 DK63861, UC4 DK63821, UC4 DK63865, UC4 DK63863, UC4 DK63836, UC4 DK95300, UC4 DK100238, UC4 DK106955, UC4 DK112243, UC4 DK117483, and Contract No. HHSN267200700014C from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institute of Allergy and Infectious Diseases (NIAID), Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institute of Environmental Health Sciences (NIEHS), Centers for Disease Control and Prevention (CDC), and JDRF. This work supported in part by the NIH/NCATS Clinical and Translational Science Awards to the University of Florida (UL1 TR000064) and the University of Colorado (UL1 TR001082). KGCS is a Lister Prize fellow and is supported by a Wellcome Trust Senior Investigator award (200871/Z/16/Z). EFM is a Wellcome-Beit prize fellow (10406/Z/14/A) supported by the Wellcome Trust and Beit Foundation (10406/Z/14/Z) and by the National Institutes for Health Research Biomedical Research Centre (Cambridge). LPX’s affiliation changed after completion of the manuscript and is now Département d'informatique et de recherche opérationnelle, Université de Montréal, Montréal, Canada and Mila, Quebec Institute for Learning Algorithms, Montréal, Canada

From Big Data to Precision Medicine.

For over a decade the term "Big data" has been used to describe the rapid increase in volume, variety and velocity of information available, not just in medical research but in almost every aspect of our lives. As scientists, we now have the capacity to rapidly generate, store and analyse data that, only a few years ago, would have taken many years to compile. However, "Big data" no longer means what it once did. The term has expanded and now refers not to just large data volume, but to our increasing ability to analyse and interpret those data. Tautologies such as "data analytics" and "data science" have emerged to describe approaches to the volume of available information as it grows ever larger. New methods dedicated to improving data collection, storage, cleaning, processing and interpretation continue to be developed, although not always by, or for, medical researchers. Exploiting new tools to extract meaning from large volume information has the potential to drive real change in clinical practice, from personalized therapy and intelligent drug design to population screening and electronic health record mining. As ever, where new technology promises "Big Advances," significant challenges remain. Here we discuss both the opportunities and challenges posed to biomedical research by our increasing ability to tackle large datasets. Important challenges include the need for standardization of data content, format, and clinical definitions, a heightened need for collaborative networks with sharing of both data and expertise and, perhaps most importantly, a need to reconsider how and when analytic methodology is taught to medical researchers. We also set "Big data" analytics in context: recent advances may appear to promise a revolution, sweeping away conventional approaches to medical science. However, their real promise lies in their synergy with, not replacement of, classical hypothesis-driven methods. The generation of novel, data-driven hypotheses based on interpretable models will always require stringent validation and experimental testing. Thus, hypothesis-generating research founded on large datasets adds to, rather than replaces, traditional hypothesis driven science. Each can benefit from the other and it is through using both that we can improve clinical practice.Wellcome Trus