shinyExprPortal: a configurable 'shiny' portal for sharing analysis of molecular expression data.

MOTIVATION: The scale of omics research presents many obstacles to full sharing and access to analysis results. Current publication models impose limits on the number of pages and figures, requiring careful preparation and selection of content. At the same time, depositing data in open repositories significantly shifts the burden of access and reproduction to readers, who may include people who are not programmers or analysts. RESULTS: We introduce shinyExprPortal, an R package that implements omics web portals with minimal coding effort. The portals allow exploration of transcriptomic or proteomic expression data and phenotypes, showcasing results of various types of analysis including differential expression, co-expression and pathways analysis. The integration with bioinformatics workflows enables researchers to focus on their results and share findings using interactive and publication-quality plots. AVAILABILITY AND IMPLEMENTATION: The shinyExprPortal package is available to download and install from CRAN and https://github.com/C4TB/shinyExprPortal.

100,000 Genomes Pilot on Rare-Disease Diagnosis in Health Care - Preliminary Report.

BACKGROUND: The U.K. 100,000 Genomes Project is in the process of investigating the role of genome sequencing in patients with undiagnosed rare diseases after usual care and the alignment of this research with health care implementation in the U.K. National Health Service. Other parts of this project focus on patients with cancer and infection. METHODS: We conducted a pilot study involving 4660 participants from 2183 families, among whom 161 disorders covering a broad spectrum of rare diseases were present. We collected data on clinical features with the use of Human Phenotype Ontology terms, undertook genome sequencing, applied automated variant prioritization on the basis of applied virtual gene panels and phenotypes, and identified novel pathogenic variants through research analysis. RESULTS: Diagnostic yields varied among family structures and were highest in family trios (both parents and a proband) and families with larger pedigrees. Diagnostic yields were much higher for disorders likely to have a monogenic cause (35%) than for disorders likely to have a complex cause (11%). Diagnostic yields for intellectual disability, hearing disorders, and vision disorders ranged from 40 to 55%. We made genetic diagnoses in 25% of the probands. A total of 14% of the diagnoses were made by means of the combination of research and automated approaches, which was critical for cases in which we found etiologic noncoding, structural, and mitochondrial genome variants and coding variants poorly covered by exome sequencing. Cohortwide burden testing across 57,000 genomes enabled the discovery of three new disease genes and 19 new associations. Of the genetic diagnoses that we made, 25% had immediate ramifications for clinical decision making for the patients or their relatives. CONCLUSIONS: Our pilot study of genome sequencing in a national health care system showed an increase in diagnostic yield across a range of rare diseases. (Funded by the National Institute for Health Research and others.).

Expression quantitative trait loci analysis in rheumatoid arthritis identifies tissue specific variants associated with severity and outcome.

OBJECTIVE: Genome-wide association studies have successfully identified more than 100 loci associated with susceptibility to rheumatoid arthritis (RA). However, our understanding of the functional effects of genetic variants in causing RA and their effects on disease severity and response to treatment remains limited. METHODS: In this study, we conducted expression quantitative trait locus (eQTL) analysis to dissect the link between genetic variants and gene expression comparing the disease tissue against blood using RNA-Sequencing of synovial biopsies (n=85) and blood samples (n=51) from treatment-naïve patients with RA from the Pathobiology of Early Arthritis Cohort. RESULTS: This identified 898 eQTL genes in synovium and genes loci in blood, with 232 genes in common to both synovium and blood, although notably many eQTL were tissue specific. Examining the HLA region, we uncovered a specific eQTL at HLA-DPB2 with the critical triad of single-nucleotide polymorphisms (SNPs) rs3128921 driving synovial HLA-DPB2 expression, and both rs3128921 and HLA-DPB2 gene expression correlating with clinical severity and increasing probability of the lympho-myeloid pathotype. CONCLUSIONS: This analysis highlights the need to explore functional consequences of genetic associations in disease tissue. HLA-DPB2 SNP rs3128921 could potentially be used to stratify patients to more aggressive treatment immediately at diagnosis.

GEOexplorer: a webserver for gene expression analysis and visualisation.

Gene Expression Omnibus (GEO) is a database repository hosting a substantial proportion of publicly available high throughput gene expression data. Gene expression analysis is a powerful tool to gain insight into the mechanisms and processes underlying the biological and phenotypic differences between sample groups. Despite the wide availability of gene expression datasets, their access, analysis, and integration are not trivial and require specific expertise and programming proficiency. We developed the GEOexplorer webserver to allow scientists to access, integrate and analyse gene expression datasets without requiring programming proficiency. Via its user-friendly graphic interface, users can easily apply GEOexplorer to perform interactive and reproducible gene expression analysis of microarray and RNA-seq datasets, while producing a wealth of interactive visualisations to facilitate data exploration and interpretation, and generating a range of publication ready figures. The webserver allows users to search and retrieve datasets from GEO as well as to upload user-generated data and combine and harmonise two datasets to perform joint analyses. GEOexplorer, available at https://geoexplorer.rosalind.kcl.ac.uk, provides a solution for performing interactive and reproducible analyses of microarray and RNA-seq gene expression data, empowering life scientists to perform exploratory data analysis and differential gene expression analysis on-the-fly without informatics proficiency.

The benefits and pitfalls of machine learning for biomarker discovery.

Prospects for the discovery of robust and reproducible biomarkers have improved considerably with the development of sensitive omics platforms that can enable measurement of biological molecules at an unprecedented scale. With technical barriers to success lowering, the challenge is now moving into the analytical domain. Genome-wide discovery presents a problem of scale and multiple testing as standard statistical methods struggle to distinguish signal from noise in increasingly complex biological systems. Machine learning and AI methods are good at finding answers in large datasets, but they have a tendency to overfit solutions. It may be possible to find a local answer or mechanism in a specific patient sample or small group of samples, but this may not generalise to wider patient populations due to the high likelihood of false discovery. The rise of explainable AI offers to improve the opportunity for true discovery by providing explanations for predictions that can be explored mechanistically before proceeding to costly and time-consuming validation studies. This review aims to introduce some of the basic concepts of machine learning and AI for biomarker discovery with a focus on post hoc explanation of predictions. To illustrate this, we consider how explainable AI has already been used successfully, and we explore a case study that applies AI to biomarker discovery in rheumatoid arthritis, demonstrating the accessibility of tools for AI and machine learning. We use this to illustrate and discuss some of the potential challenges and solutions that may enable AI to critically interrogate disease and response mechanisms.

Local delivery of nitric oxide prevents endothelial dysfunction in periodontitis.

AIMS: Increased cardiovascular disease risk underlies elevated rates of mortality in individuals with periodontitis. A key characteristic of those with increased cardiovascular risk is endothelial dysfunction, a phenomenon synonymous with deficiencies of bioavailable nitric oxide (NO), and prominently expressed in patients with periodontitis. Also, inorganic nitrate can be reduced to NO in vivo to restore NO levels, leading us to hypothesise that its use may be beneficial in reducing periodontitis-associated endothelial dysfunction. Herein we sought to determine whether inorganic nitrate improves endothelial function in the setting of periodontitis and if so to determine the mechanisms underpinning any responses seen. METHODS AND RESULTS: Periodontitis was induced in mice by placement of a ligature for 14 days around the second molar. Treatment in vivo with potassium nitrate, either prior to or following establishment of experimental periodontitis, attenuated endothelial dysfunction, as determined by assessment of acetylcholine-induced relaxation of aortic rings, compared to control (potassium chloride treatment). These beneficial effects were associated with a suppression of vascular wall inflammatory pathways (assessed by quantitative-PCR), increases in the anti-inflammatory cytokine interleukin (IL)-10 and reduced tissue oxidative stress due to attenuation of xanthine oxidoreductase-dependent superoxide generation. In patients with periodontitis, plasma nitrite levels were not associated with endothelial function indicating dysfunction. CONCLUSION: Our results suggest that inorganic nitrate protects against, and can partially reverse pre-existing, periodontitis-induced endothelial dysfunction through restoration of nitrite and thus NO levels. This research highlights the potential of dietary nitrate as adjunct therapy to target the associated negative cardiovascular outcomes in patients with periodontitis.

Interferon-α-mediated therapeutic resistance in early rheumatoid arthritis implicates epigenetic reprogramming.

OBJECTIVES: An interferon (IFN) gene signature (IGS) is present in approximately 50% of early, treatment naive rheumatoid arthritis (eRA) patients where it has been shown to negatively impact initial response to treatment. We wished to validate this effect and explore potential mechanisms of action. METHODS: In a multicentre inception cohort of eRA patients (n=191), we examined the whole blood IGS (MxA, IFI44L, OAS1, IFI6, ISG15) with reference to circulating IFN proteins, clinical outcomes and epigenetic influences on circulating CD19+ B and CD4+ T lymphocytes. RESULTS: We reproduced our previous findings demonstrating a raised baseline IGS. We additionally showed, for the first time, that the IGS in eRA reflects circulating IFN-α protein. Paired longitudinal analysis demonstrated a significant reduction between baseline and 6-month IGS and IFN-α levels (p<0.0001 for both). Despite this fall, a raised baseline IGS predicted worse 6-month clinical outcomes such as increased disease activity score (DAS-28, p=0.025) and lower likelihood of a good EULAR clinical response (p=0.034), which was independent of other conventional predictors of disease activity and clinical response. Molecular analysis of CD4+ T cells and CD19+ B cells demonstrated differentially methylated CPG sites and dysregulated expression of disease relevant genes, including PARP9, STAT1, and EPSTI1, associated with baseline IGS/IFNα levels. Differentially methylated CPG sites implicated altered transcription factor binding in B cells (GATA3, ETSI, NFATC2, EZH2) and T cells (p300, HIF1α). CONCLUSIONS: Our data suggest that, in eRA, IFN-α can cause a sustained, epigenetically mediated, pathogenic increase in lymphocyte activation and proliferation, and that the IGS is, therefore, a robust prognostic biomarker. Its persistent harmful effects provide a rationale for the initial therapeutic targeting of IFN-α in selected patients with eRA.

Enzymatic degradation of RNA causes widespread protein aggregation in cell and tissue lysates.

Most proteins in cell and tissue lysates are soluble. We show here that in lysate from human neurons, more than 1,300 proteins are maintained in a soluble and functional state by association with endogenous RNA, as degradation of RNA invariably leads to protein aggregation. The majority of these proteins lack conventional RNA-binding domains. Using synthetic oligonucleotides, we identify the importance of nucleic acid structure, with single-stranded pyrimidine-rich bulges or loops surrounded by double-stranded regions being particularly efficient in the maintenance of protein solubility. These experiments also identify an apparent one-to-one protein-nucleic acid stoichiometry. Furthermore, we show that protein aggregates isolated from brain tissue from Amyotrophic Lateral Sclerosis patients can be rendered soluble after refolding by both RNA and synthetic oligonucleotides. Together, these findings open new avenues for understanding the mechanism behind protein aggregation and shed light on how certain proteins remain soluble.

Over 1000 genetic loci influencing blood pressure with multiple systems and tissues implicated.

High blood pressure (BP) remains the major heritable and modifiable risk factor for cardiovascular disease. Persistent high BP, or hypertension, is a complex trait with both genetic and environmental interactions. Despite swift advances in genomics, translating new discoveries to further our understanding of the underlying molecular mechanisms remains a challenge. More than 500 loci implicated in the regulation of BP have been revealed by genome-wide association studies (GWAS) in 2018 alone, taking the total number of BP genetic loci to over 1000. Even with the large number of loci now associated to BP, the genetic variance explained by all loci together remains low (~5.7%). These genetic associations have elucidated mechanisms and pathways regulating BP, highlighting potential new therapeutic and drug repurposing targets. A large proportion of the BP loci were discovered and reported simultaneously by multiple research groups, creating a knowledge gap, where the reported loci to date have not been investigated in a harmonious way. Here, we review the BP-associated genetic variants reported across GWAS studies and investigate their potential impact on the biological systems using in silico enrichment analyses for pathways, tissues, gene ontology and genetic pleiotropy.

EAP1 regulation of GnRH promoter activity is important for human pubertal timing.

The initiation of puberty is orchestrated by an augmentation of gonadotropin-releasing hormone (GnRH) secretion from a few thousand hypothalamic neurons. Recent findings have indicated that the neuroendocrine control of puberty may be regulated by a hierarchically organized network of transcriptional factors acting upstream of GnRH. These include enhanced at puberty 1 (EAP1), which contributes to the initiation of female puberty through transactivation of the GnRH promoter. However, no EAP1 mutations have been found in humans with disorders of pubertal timing. We performed whole-exome sequencing in 67 probands and 93 relatives from a large cohort of familial self-limited delayed puberty (DP). Variants were analyzed for rare, potentially pathogenic variants enriched in case versus controls and relevant to the biological control of puberty. We identified one in-frame deletion (Ala221del) and one rare missense variant (Asn770His) in EAP1 in two unrelated families; these variants were highly conserved and potentially pathogenic. Expression studies revealed Eap1 mRNA abundance in peri-pubertal mouse hypothalamus. EAP1 binding to the GnRH1 promoter increased in monkey hypothalamus at the onset of puberty as determined by chromatin immunoprecipitation. Using a luciferase reporter assay, EAP1 mutants showed a reduced ability to trans-activate the GnRH promoter compared to wild-type EAP1, due to reduced protein levels caused by the Ala221del mutation and subcellular mislocation caused by the Asn770His mutation, as revealed by western blot and immunofluorescence, respectively. In conclusion, we have identified the first EAP1 mutations leading to reduced GnRH transcriptional activity resulting in a phenotype of self-limited DP.

Spectrum: fast density-aware spectral clustering for single and multi-omic data.

MOTIVATION: Clustering patient omic data is integral to developing precision medicine because it allows the identification of disease subtypes. A current major challenge is the integration multi-omic data to identify a shared structure and reduce noise. Cluster analysis is also increasingly applied on single-omic data, for example, in single cell RNA-seq analysis for clustering the transcriptomes of individual cells. This technology has clinical implications. Our motivation was therefore to develop a flexible and effective spectral clustering tool for both single and multi-omic data. RESULTS: We present Spectrum, a new spectral clustering method for complex omic data. Spectrum uses a self-tuning density-aware kernel we developed that enhances the similarity between points that share common nearest neighbours. It uses a tensor product graph data integration and diffusion procedure to reduce noise and reveal underlying structures. Spectrum contains a new method for finding the optimal number of clusters (K) involving eigenvector distribution analysis. Spectrum can automatically find K for both Gaussian and non-Gaussian structures. We demonstrate across 21 real expression datasets that Spectrum gives improved runtimes and better clustering results relative to other methods. AVAILABILITY AND IMPLEMENTATION: Spectrum is available as an R software package from CRAN https://cran.r-project.org/web/packages/Spectrum/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genome-Wide Analysis of Left Ventricular Image-Derived Phenotypes Identifies Fourteen Loci Associated With Cardiac Morphogenesis and Heart Failure Development.

BACKGROUND: The genetic basis of left ventricular (LV) image-derived phenotypes, which play a vital role in the diagnosis, management, and risk stratification of cardiovascular diseases, is unclear at present. METHODS: The LV parameters were measured from the cardiovascular magnetic resonance studies of the UK Biobank. Genotyping was done using Affymetrix arrays, augmented by imputation. We performed genome-wide association studies of 6 LV traits-LV end-diastolic volume, LV end-systolic volume, LV stroke volume, LV ejection fraction, LV mass, and LV mass to end-diastolic volume ratio. The replication analysis was performed in the MESA study (Multi-Ethnic Study of Atherosclerosis). We identified the candidate genes at genome-wide significant loci based on the evidence from extensive bioinformatic analyses. Polygenic risk scores were constructed from the summary statistics of LV genome-wide association studies to predict the heart failure events. RESULTS: The study comprised 16 923 European UK Biobank participants (mean age 62.5 years; 45.8% men) without prevalent myocardial infarction or heart failure. We discovered 14 genome-wide significant loci (3 loci each for LV end-diastolic volume, LV end-systolic volume, and LV mass to end-diastolic volume ratio; 4 loci for LV ejection fraction, and 1 locus for LV mass) at a stringent P<1×10-8. Three loci were replicated at Bonferroni significance and 7 loci at nominal significance (P<0.05 with concordant direction of effect) in the MESA study (n=4383). Follow-up bioinformatic analyses identified 28 candidate genes that were enriched in the cardiac developmental pathways and regulation of the LV contractile mechanism. Eight genes (TTN, BAG3, GRK5, HSPB7, MTSS1, ALPK3, NMB, and MMP11) supported by at least 2 independent lines of in silico evidence were implicated in the cardiac morphogenesis and heart failure development. The polygenic risk scores of LV phenotypes were predictive of heart failure in a holdout UK Biobank sample of 3106 cases and 224 134 controls (odds ratio 1.41, 95% CI 1.26 - 1.58, for the top quintile versus the bottom quintile of the LV end-systolic volume risk score). CONCLUSIONS: We report 14 genetic loci and indicate several candidate genes that not only enhance our understanding of the genetic architecture of prognostically important LV phenotypes but also shed light on potential novel therapeutic targets for LV remodeling.

Statin-induced LDL cholesterol response and type 2 diabetes: a bidirectional two-sample Mendelian randomization study.

It remains unclear whether the increased risk of new-onset type 2 diabetes (T2D) seen in statin users is due to low LDL-C concentrations, or due to the statin-induced proportional change in LDL-C. In addition, genetic instruments have not been proposed before to examine whether liability to T2D might cause greater proportional statin-induced LDL-C lowering. Using summary-level statistics from the Genomic Investigation of Statin Therapy (GIST, nmax = 40,914) and DIAGRAM (nmax = 159,208) consortia, we found a positive genetic correlation between LDL-C statin response and T2D using LD score regression (rgenetic = 0.36, s.e. = 0.13). However, mendelian randomization analyses did not provide support for statin response having a causal effect on T2D risk (OR 1.00 (95% CI: 0.97, 1.03) per 10% increase in statin response), nor that liability to T2D has a causal effect on statin-induced LDL-C response (0.20% increase in response (95% CI: -0.40, 0.80) per doubling of odds of liability to T2D). Although we found no evidence to suggest that proportional statin response influences T2D risk, a definitive assessment should be made in populations comprised exclusively of statin users, as the presence of nonstatin users in the DIAGRAM dataset may have substantially diluted our effect estimate.

Investigation of genetically regulated gene expression and response to treatment in rheumatoid arthritis highlights an association between IL18RAP expression and treatment response.

OBJECTIVES: In this study, we sought to investigate whether there was any association between genetically regulated gene expression (as predicted using various reference panels) and anti-tumour necrosis factor (anti-TNF) treatment response (change in erythrocyte sedimentation rate (ESR)) using 3158 European ancestry patients with rheumatoid arthritis. METHODS: The genetically regulated portion of gene expression was estimated in the full cohort of 3158 subjects (as well as within a subcohort consisting of 1575 UK patients) using the PrediXcan software package with three different reference panels. Estimated expression was tested for association with anti-TNF treatment response. As a replication/validation experiment, we also investigated the correlation between change in ESR with measured gene expression at the Interleukin 18 Receptor Accessory Protein (IL18RAP) gene in whole blood and synovial tissue, using an independent replication data set of patients receiving conventional synthetic disease modifying anti-rheumatic drugs, with directly measured (via RNA sequencing) gene expression. RESULTS: We found that predicted expression of IL18RAP showed a consistent signal of association with treatment response across the reference panels. In our independent replication data set, IL18RAP expression in whole blood showed correlation with the change in ESR between baseline and follow-up (r=-0.35, p=0.0091). Change in ESR was also correlated with the expression of IL18RAP in synovial tissue (r=-0.28, p=0.02). CONCLUSION: Our results suggest that IL18RAP expression is worthy of further investigation as a potential predictor of treatment response in rheumatoid arthritis that is not specific to a particular drug type.

Differentially expressed genes for atrial fibrillation identified by RNA sequencing from paired human left and right atrial appendages.

Atrial fibrillation is a significant worldwide contributor to cardiovascular morbidity and mortality. Few studies have investigated the differences in gene expression between the left and right atrial appendages, leaving their characterization largely unexplored. In this study, differential gene expression was investigated in atrial fibrillation and sinus rhythm using left and right atrial appendages from the same patients. RNA sequencing was performed on the left and right atrial appendages from five sinus rhythm (SR) control patients and five permanent AF case patients. Differential gene expression in both the left and right atrial appendages was analyzed using the Bioconductor package edgeR. A selection of differentially expressed genes, with relevance to atrial fibrillation, were further validated using quantitative RT-PCR. The distribution of the samples assessed through principal component analysis showed distinct grouping between left and right atrial appendages and between SR controls and AF cases. Overall 157 differentially expressed genes were identified to be downregulated and 90 genes upregulated in AF. Pathway enrichment analysis indicated a greater involvement of left atrial genes in the Wnt signaling pathway whereas right atrial genes were involved in clathrin-coated vesicle and collagen formation. The differing expression of genes in both left and right atrial appendages indicate that there are different mechanisms for development, support and remodeling of AF within the left and right atria.

Prediction of treatment response in rheumatoid arthritis patients using genome-wide SNP data.

Although a number of treatments are available for rheumatoid arthritis (RA), each of them shows a significant nonresponse rate in patients. Therefore, predicting a priori the likelihood of treatment response would be of great patient benefit. Here, we conducted a comparison of a variety of statistical methods for predicting three measures of treatment response, between baseline and 3 or 6 months, using genome-wide SNP data from RA patients available from the MAximising Therapeutic Utility in Rheumatoid Arthritis (MATURA) consortium. Two different treatments and 11 different statistical methods were evaluated. We used 10-fold cross validation to assess predictive performance, with nested 10-fold cross validation used to tune the model hyperparameters when required. Overall, we found that SNPs added very little prediction information to that obtained using clinical characteristics only, such as baseline trait value. This observation can be explained by the lack of strong genetic effects and the relatively small sample sizes available; in analysis of simulated and real data, with larger effects and/or larger sample sizes, prediction performance was much improved. Overall, methods that were consistent with the genetic architecture of the trait were able to achieve better predictive ability than methods that were not. For treatment response in RA, methods that assumed a complex underlying genetic architecture achieved slightly better prediction performance than methods that assumed a simplified genetic architecture.

Synovial cellular and molecular signatures stratify clinical response to csDMARD therapy and predict radiographic progression in early rheumatoid arthritis patients.

OBJECTIVES: To unravel the hierarchy of cellular/molecular pathways in the disease tissue of early, treatment-naïve rheumatoid arthritis (RA) patients and determine their relationship with clinical phenotypes and treatment response/outcomes longitudinally. METHODS: 144 consecutive treatment-naïve early RA patients (<12 months symptoms duration) underwent ultrasound-guided synovial biopsy before and 6 months after disease-modifying antirheumatic drug (DMARD) initiation. Synovial biopsies were analysed for cellular (immunohistology) and molecular (NanoString) characteristics and results compared with clinical and imaging outcomes. Differential gene expression analysis and logistic regression were applied to define variables correlating with treatment response and predicting radiographic progression. RESULTS: Cellular and molecular analyses of synovial tissue demonstrated for the first time in early RA the presence of three pathology groups: (1) lympho-myeloid dominated by the presence of B cells in addition to myeloid cells; (2) diffuse-myeloid with myeloid lineage predominance but poor in B cells nd (3) pauci-immune characterised by scanty immune cells and prevalent stromal cells. Longitudinal correlation of molecular signatures demonstrated that elevation of myeloid- and lymphoid-associated gene expression strongly correlated with disease activity, acute phase reactants and DMARD response at 6 months. Furthermore, elevation of synovial lymphoid-associated genes correlated with autoantibody positivity and elevation of osteoclast-targeting genes predicting radiographic joint damage progression at 12 months. Patients with predominant pauci-immune pathology showed less severe disease activity and radiographic progression. CONCLUSIONS: We demonstrate at disease presentation, prior to pathology modulation by therapy, the presence of specific cellular/molecular synovial signatures that delineate disease severity/progression and therapeutic response and may pave the way to more precise definition of RA taxonomy, therapeutic targeting and improved outcomes.

Correction: Signatures of inflammation and impending multiple organ dysfunction in the hyperacute phase of trauma: A prospective cohort study.

[This corrects the article DOI: 10.1371/journal.pmed.1002352.].

Genome-wide association study of response to methotrexate in early rheumatoid arthritis patients.

Methotrexate (MTX) monotherapy is a common first treatment for rheumatoid arthritis (RA), but many patients do not respond adequately. In order to identify genetic predictors of response, we have combined data from two consortia to carry out a genome-wide study of response to MTX in 1424 early RA patients of European ancestry. Clinical endpoints were change from baseline to 6 months after starting treatment in swollen 28-joint count, tender 28-joint count, C-reactive protein and the overall 3-component disease activity score (DAS28). No single nucleotide polymorphism (SNP) reached genome-wide statistical significance for any outcome measure. The strongest evidence for association was with rs168201 in NRG3 (p = 10-7 for change in DAS28). Some support was also seen for association with ZMIZ1, previously highlighted in a study of response to MTX in juvenile idiopathic arthritis. Follow-up in two smaller cohorts of 429 and 177 RA patients did not support these findings, although these cohorts were more heterogeneous.

An ectopically expressed serum miRNA signature is prognostic, diagnostic, and biologically related to liver allograft rejection.

The ability to noninvasively diagnose acute cellular rejection (ACR) with high specificity and sensitivity would significantly advance personalized liver transplant recipient care and management of immunosuppression. We performed microRNA (miRNA) profiling in 318 serum samples from 69 liver transplant recipients enrolled in the Immune Tolerance Network immunosuppression withdrawal (ITN030ST) and Clinical Trials in Organ Transplantation (CTOT-03) studies. We quantified serum miRNA at clinically indicated and/or protocol biopsy events (n = 130). The trajectory of ACR diagnostic miRNAs during immunosuppression withdrawal were also evaluated in sera taken at predetermined intervals during immunosuppression minimization before and at clinically indicated liver biopsy (n = 119). Levels of 31 miRNAs were significantly associated with ACR diagnosis with two miRNAs differentiating ACR from non-ACR (area under the receiver operating characteristic curve = 90%, 95% confidence interval = 82%-96%) and predicted ACR events up to 40 days before biopsy-proven rejection. The most differentially expressed miRNAs were low or absent in the blood of healthy individuals but highly expressed in liver tissue, indicating an ectopic origin from the liver allograft. Pathway analyses of rejection-associated miRNAs and their target messenger RNAs (mRNAs) showed induction of proinflammatory and cell death-related pathways. Integration of differentially expressed serum miRNA with concordant liver biopsy mRNA demonstrates interaction between molecules with a known role in transplant rejection. CONCLUSION: Distinct miRNA levels profiled from sera at the time of clinical allograft dysfunction can be used to noninvasively diagnose ACR. Predictive trajectories of the same profile during supervised immunosuppression minimization diagnosed rejection up to 40 days prior to clinical expression. The rejection-associated miRNAs in sera appear to be ectopically expressed liver and specific immune cell miRNAs that are biologically related, and the consequences of immune-mediated damage to the allograft. (Hepatology 2017;65:269-280).