Mini-review: Update on the genetics of schizophrenia.

A number of important findings have recently emerged relevant to identifying genetic risk factors for schizophrenia. Findings using common variants point towards gene sets of interest and also demonstrate an overlap with other psychiatric and nonpsychiatric disorders. Imputation of variants of the gene for complement component 4 (C4) from GWAS data has shown that the predicted expression of the C4A product is associated with schizophrenia risk. Very rare variants disrupting SETD1A, RBM12 or NRXN1 have a large effect on risk. Other rare, damaging variants are enriched in genes that are loss of function intolerant and/or whose products localise to the synapse. These and particular copy number variants can result in increased risk of schizophrenia but also of other neurodevelopmental disorders. The findings for C4 and NRXN1 may be especially helpful for elucidating the biological mechanisms that can lead to disease

Impact of immunogenicity on clinical efficacy and toxicity profile of biologic agents used for treatment of inflammatory arthritis in children compared to adults

The treatment of inflammatory arthritis has been revolutionised by the introduction of biologic treatments. Many biologic agents are currently licensed for use in both paediatric and adult patients with inflammatory arthritis and contribute to improved disease outcomes compared with the pre-biologic era. However, immunogenicity to biologic agents, characterised by an immune reaction leading to the production of anti-drug antibodies (ADAs), can negatively impact the therapeutic efficacy of biologic drugs and induce side effects to treatment. This review explores for the first time the impact of immunogenicity against all licensed biologic treatments currently used in inflammatory arthritis across age, and will examine any significant differences between ADA prevalence, titres and timing of development, as well as ADA impact on therapeutic drug levels, clinical efficacy and side effects between paediatric and adult patients. In addition, we will investigate factors associated with differences in immunogenicity across biologic agents used in inflammatory arthritis, and their potential therapeutic implications

Weighted Burden Analysis of Exome-Sequenced Case-Control Sample Implicates Synaptic Genes in Schizophrenia Aetiology

A previous study of exome-sequenced schizophrenia cases and controls reported an excess of singleton, gene-disruptive variants among cases, concentrated in particular gene sets. The dataset included a number of subjects with a substantial Finnish contribution to ancestry. We have reanalysed the same dataset after removal of these subjects and we have also included non-singleton variants of all types using a weighted burden test which assigns higher weights to variants predicted to have a greater effect on protein function. We investigated the same 31 gene sets as previously and also 1454 GO gene sets. The reduced dataset consisted of 4225 cases and 5834 controls. No individual variants or genes were significantly enriched in cases but 13 out of the 31 gene sets were significant after Bonferroni correction and the "FMRP targets" set produced a signed log p value (SLP) of 7.1. The gene within this set with the highest SLP, equal to 3.4, was FYN, which codes for a tyrosine kinase which phosphorylates glutamate metabotropic receptors and ionotropic NMDA receptors, thus modulating their trafficking, subcellular distribution and function. In the most recent GWAS of schizophrenia it was identified as a "prioritized candidate gene". Two of the subunits of the NMDA receptor which are substrates of FYN are coded for by GRIN1 (SLP = 1.7) and GRIN2B (SLP = 2.1). Of note, for some sets there was a substantial enrichment of non-singleton variants. Of 1454 GO gene sets, three were significant after Bonferroni correction. Identifying specific genes and variants will depend on genotyping them in larger samples and/or demonstrating that they cosegregate with illness within pedigrees

Increased apolipoprotein-B:A1 ratio predicts cardiometabolic risk in patients with juvenile onset SLE

Background: Cardiovascular disease is a leading cause of mortality in patients with juvenile-onset systemic lupus erythematosus (JSLE). Traditional factors for cardiovascular risk (CVR) prediction are less robust in younger patients. More reliable CVR biomarkers are needed for JSLE patient stratification and to identify therapeutic approaches to reduce cardiovascular morbidity and mortality in JSLE. Methods: Serum metabolomic analysis (including >200 lipoprotein measures) was performed on a discovery (n=31, median age 19) and validation (n=31, median age 19) cohort of JSLE patients. Data was analysed using cluster, receiver operating characteristic analysis and logistic regression. RNA-sequencing assessed gene expression in matched patient samples. Findings: Hierarchical clustering of lipoprotein measures identified and validated two unique JSLE groups. Group-1 had an atherogenic and Group-2 had an atheroprotective lipoprotien profile. Apolipoprotein(Apo)B:ApoA1 distinguished the two groups with high specificity (96.2%) and sensitivity (96.7%). JSLE patients with high ApoB:ApoA1 ratio had increased CD8+ T-cell frequencies and a CD8+ T-cell transcriptomic profile enriched in genes associated with atherogenic processes including interferon signaling. These metabolic and immune signatures overlapped statistically significantly with lipid biomarkers associated with sub-clinical atherosclerosis in adult SLE patients and with genes overexpressed in T-cells from human atherosclerotic plaque respectively. Finally, baseline ApoB:ApoA1 ratio correlated positively with SLE disease activity index (r=0.43, p=0.0009) and negatively with Lupus Low Disease Activity State (r=-0.43, p=0.0009) over 5-year follow-up. Interpretation: Multi-omic analysis identified high ApoB:ApoA1 as a potential biomarker of increased cardiometabolic risk and worse clinical outcomes in JSLE. ApoB:ApoA1 could help identify patients that require increased disease monitoring, lipid modification or lifestyle changes. Funding: Lupus UK, The Rosetrees Trust, British Heart Foundation, UCL & Birkbeck MRC Doctoral Training Programme and Versus Arthritis

Using serum metabolomics analysis to predict sub-clinical atherosclerosis in patients with SLE

Background: Patients with systemic lupus erythematosus (SLE) have an increased risk of developing cardiovascular disease (CVD) and 30-40% have sub-clinical atherosclerosis on vascular ultrasound scanning. Standard measurements of serum lipids in clinical practice do not predict CVD risk in patients with SLE. We hypothesise that more detailed analysis of lipoprotein taxonomy could identify better predictors of CVD risk in SLE. / Methods: Eighty patients with SLE and no history of CVD underwent carotid and femoral ultrasound scans; 30 had atherosclerosis plaques (SLE-P) and 50 had no plaques (SLE-NP). Serum samples obtained at the time of the scan were analysed using a lipoprotein-focused metabolomics platform assessing 228 metabolites by nuclear magnetic resonance spectroscopy. Data was analysed using logistic regression and five binary classification models with 10-fold cross validation; decision tree, random forest, support vector machine and lasso (Least Absolute Shrinkage and Selection Operator) logistic regression with and without interactions. / Results: Univariate logistic regression identified four metabolites associated with the presence of sub-clinical plaque; three subclasses of very low density lipoprotein (VLDL) (percentage of free cholesterol in medium and large VLDL particles and percentage of phospholipids in chylomicrons and extremely large VLDL particles) and Leucine. Together with age, these metabolites were also within the top features identified by the lasso logistic regression (with and without interactions) and random forest machine learning models. Logistic regression with interactions differentiated between SLE-P and SLE-NP with greatest accuracy (0.800). Notably, percentage of free cholesterol in large VLDL particles and age were identified by all models as being important to differentiate between SLE-P and SLE-NP patients. / Conclusion: Serum metabolites are a promising biomarker for prediction of sub-clinical atherosclerosis development in SLE patients and could provide novel insight into mechanisms of early atherosclerosis development

Serum Metabolomic Signatures Can Predict Subclinical Atherosclerosis in Patients With Systemic Lupus Erythematosus

OBJECTIVE: Patients with systemic lupus erythematosus (SLE) have an increased risk of developing cardiovascular disease. Standard serum lipid measurements in clinical practice do not predict cardiovascular disease risk in patients with SLE. More detailed analysis of lipoprotein taxonomy could identify better predictors of cardiovascular disease risk in SLE. Approach and Results: Eighty women with SLE and no history of cardiovascular disease underwent carotid and femoral ultrasound scans; 30 had atherosclerosis plaques (patients with SLE with subclinical plaque) and 50 had no plaques (patients with SLE with no subclinical plaque). Serum samples obtained at the time of the scan were analyzed using a lipoprotein-focused metabolomics platform assessing 228 metabolites by nuclear magnetic resonance spectroscopy. Data were analyzed using logistic regression and 5 binary classification models with 10-fold cross validation. Patients with SLE had global changes in complex lipoprotein profiles compared with healthy controls despite having clinical serum lipid levels within normal ranges. In the SLE cohort, univariate logistic regression identified 4 metabolites associated with subclinical plaque; 3 subclasses of VLDL (very low-density lipoprotein; free cholesterol in medium and large VLDL particles and phospholipids in chylomicrons and extremely large VLDL particles) and leucine. Together with age, these metabolites were also within the top features identified by the lasso logistic regression (with and without interactions) and random forest machine learning models. Logistic regression with interactions differentiated between patients with SLE with subclinical plaque and patients with SLE with no subclinical plaque groups with the greatest accuracy (0.800). Notably, free cholesterol in large VLDL particles and age differentiated between patients with SLE with subclinical plaque and patients with SLE with no subclinical plaque in all models. CONCLUSIONS: Serum metabolites are promising biomarkers to uncover and predict multimetabolic phenotypes of subclinical atherosclerosis in SLE

Weighted Burden Analysis of Exome-Sequenced Case-Control Sample Implicates Synaptic Genes in Schizophrenia Aetiology.

A previous study of exome-sequenced schizophrenia cases and controls reported an excess of singleton, gene-disruptive variants among cases, concentrated in particular gene sets. The dataset included a number of subjects with a substantial Finnish contribution to ancestry. We have reanalysed the same dataset after removal of these subjects and we have also included non-singleton variants of all types using a weighted burden test which assigns higher weights to variants predicted to have a greater effect on protein function. We investigated the same 31 gene sets as previously and also 1454 GO gene sets. The reduced dataset consisted of 4225 cases and 5834 controls. No individual variants or genes were significantly enriched in cases but 13 out of the 31 gene sets were significant after Bonferroni correction and the "FMRP targets" set produced a signed log p value (SLP) of 7.1. The gene within this set with the highest SLP, equal to 3.4, was FYN, which codes for a tyrosine kinase which phosphorylates glutamate metabotropic receptors and ionotropic NMDA receptors, thus modulating their trafficking, subcellular distribution and function. In the most recent GWAS of schizophrenia it was identified as a "prioritized candidate gene". Two of the subunits of the NMDA receptor which are substrates of FYN are coded for by GRIN1 (SLP = 1.7) and GRIN2B (SLP = 2.1). Of note, for some sets there was a substantial enrichment of non-singleton variants. Of 1454 GO gene sets, three were significant after Bonferroni correction. Identifying specific genes and variants will depend on genotyping them in larger samples and/or demonstrating that they cosegregate with illness within pedigrees.Samples used for data analysis were provided by the Swedish Cohort Collection supported by the NIMH grant R01MH077139, the Sylvan C. Herman Foundation, the Stanley Medical Research Institute and The Swedish Research Council (Grants 2009-4959 and 2011-4659). Support for the exome sequencing was provided by the NIMH Grand Opportunity grant RCMH089905, the Sylvan C. Herman Foundation, a grant from the Stanley Medical Research Institute and multiple gifts to the Stanley Center for Psychiatric Research at the Broad Institute of MIT and Harvard. JH is supported by MRC studentship 516702

Using Serum Metabolomics to Predict Development of Anti-drug Antibodies in Multiple Sclerosis Patients Treated With IFNβ

Background: Neutralizing anti-drug antibodies (ADA) can greatly reduce the efficacy of biopharmaceuticals used to treat patients with multiple sclerosis (MS). However, the biological factors pre-disposing an individual to develop ADA are poorly characterized. Thus, there is an unmet clinical need for biomarkers to predict the development of immunogenicity, and subsequent treatment failure. Up to 35% of MS patients treated with beta interferons (IFNβ) develop ADA. Here we use machine learning to predict immunogenicity against IFNβ utilizing serum metabolomics data. Methods: Serum samples were collected from 89 MS patients as part of the ABIRISK consortium—a multi-center prospective study of ADA development. Metabolites and ADA were quantified prior to and after IFNβ treatment. Thirty patients became ADA positive during the first year of treatment (ADA+). We tested the efficacy of six binary classification models using 10-fold cross validation; k-nearest neighbors, decision tree, random forest, support vector machine and lasso (Least Absolute Shrinkage and Selection Operator) logistic regression with and without interactions. Results: We were able to predict future immunogenicity from baseline metabolomics data. Lasso logistic regression with/without interactions and support vector machines were the most successful at identifying ADA+ or ADA– cases, respectively. Furthermore, patients who become ADA+ had a distinct metabolic response to IFNβ in the first 3 months, with 29 differentially regulated metabolites. Machine learning algorithms could also predict ADA status based on metabolite concentrations at 3 months. Lasso logistic regressions had the greatest proportion of correct classifications [F1 score (accuracy measure) = 0.808, specificity = 0.913]. Finally, we hypothesized that serum lipids could contribute to ADA development by altering immune-cell lipid rafts. This was supported by experimental evidence demonstrating that, prior to IFNβ exposure, lipid raft-associated lipids were differentially expressed between MS patients who became ADA+ or remained ADA–. Conclusion: Serum metabolites are a promising biomarker for prediction of ADA development in MS patients treated with IFNβ, and could provide novel insight into mechanisms of immunogenicity

Disease-associated and patient-specific immune cell signatures in juvenile-onset systemic lupus erythematosus: patient stratification using a machine-learning approach

Background: Juvenile-onset systemic lupus erythematosus (SLE) is a rare autoimmune rheumatic disease characterised by more severe disease manifestations, earlier damage accrual, and higher mortality than in adult-onset SLE. We aimed to use machine-learning approaches to characterise the immune cell profile of patients with juvenile-onset SLE and investigate links with the disease trajectory over time. Methods: This study included patients who attended the University College London Hospital (London, UK) adolescent rheumatology service, had juvenile-onset SLE according to the 1997 American College of Rheumatology revised classification criteria for lupus or the 2012 Systemic Lupus International Collaborating Clinics criteria, and were diagnosed before 18 years of age. Blood donated by healthy age-matched and sex-matched volunteers who were taking part in educational events in the Centre for Adolescent Rheumatology Versus Arthritis at University College London (London, UK) was used as a control. Immunophenotyping profiles (28 immune cell subsets) of peripheral blood mononuclear cells from patients with juvenile-onset SLE and healthy controls were determined by flow cytometry. We used balanced random forest (BRF) and sparse partial least squares-discriminant analysis (sPLS-DA) to assess classification and parameter selection, and validation was by ten-fold cross-validation. We used logistic regression to test the association between immune phenotypes and k-means clustering to determine patient stratification. Retrospective longitudinal clinical data, including disease activity and medication, were related to the immunological features identified. Findings: Between Sept 5, 2012, and March 7, 2018, peripheral blood was collected from 67 patients with juvenile-onset SLE and 39 healthy controls. The median age was 19 years (IQR 13–25) for patients with juvenile-onset SLE and 18 years (16–25) for healthy controls. The BRF model discriminated patients with juvenile-onset SLE from healthy controls with 90·9% prediction accuracy. The top-ranked immunological features from the BRF model were confirmed using sPLS-DA and logistic regression, and included total CD4, total CD8, CD8 effector memory, and CD8 naive T cells, Bm1, and unswitched memory B cells, total CD14 monocytes, and invariant natural killer T cells. Using these markers patients were clustered into four distinct groups. Notably, CD8 T-cell subsets were important in driving patient stratification, whereas B-cell markers were similarly expressed across the cohort of patients with juvenile-onset SLE. Patients with juvenile-onset SLE and elevated CD8 effector memory T-cell frequencies had more persistently active disease over time, as assessed by the SLE disease activity index 2000, and this was associated with increased treatment with mycophenolate mofetil and an increased prevalence of lupus nephritis. Finally, network analysis confirmed the strong association between immune phenotype and differential clinical features. Interpretation: Machine-learning models can define potential disease-associated and patient-specific immune characteristics in rare disease patient populations. Immunological association studies are warranted to develop data-driven personalised medicine approaches for treatment of patients with juvenile-onset SLE. Funding: Lupus UK, The Rosetrees Trust, Versus Arthritis, and UK National Institute for Health Research University College London Hospital Biomedical Research Centre

Serum Metabolomic Signatures Can Predict Subclinical Atherosclerosis in Patients With Systemic Lupus Erythematosus

OBJECTIVE: Patients with systemic lupus erythematosus (SLE) have an increased risk of developing cardiovascular disease. Standard serum lipid measurements in clinical practice do not predict cardiovascular disease risk in patients with SLE. More detailed analysis of lipoprotein taxonomy could identify better predictors of cardiovascular disease risk in SLE. Approach and Results: Eighty women with SLE and no history of cardiovascular disease underwent carotid and femoral ultrasound scans; 30 had atherosclerosis plaques (patients with SLE with subclinical plaque) and 50 had no plaques (patients with SLE with no subclinical plaque). Serum samples obtained at the time of the scan were analyzed using a lipoprotein-focused metabolomics platform assessing 228 metabolites by nuclear magnetic resonance spectroscopy. Data were analyzed using logistic regression and 5 binary classification models with 10-fold cross validation. Patients with SLE had global changes in complex lipoprotein profiles compared with healthy controls despite having clinical serum lipid levels within normal ranges. In the SLE cohort, univariate logistic regression identified 4 metabolites associated with subclinical plaque; 3 subclasses of VLDL (very low-density lipoprotein; free cholesterol in medium and large VLDL particles and phospholipids in chylomicrons and extremely large VLDL particles) and leucine. Together with age, these metabolites were also within the top features identified by the lasso logistic regression (with and without interactions) and random forest machine learning models. Logistic regression with interactions differentiated between patients with SLE with subclinical plaque and patients with SLE with no subclinical plaque groups with the greatest accuracy (0.800). Notably, free cholesterol in large VLDL particles and age differentiated between patients with SLE with subclinical plaque and patients with SLE with no subclinical plaque in all models. CONCLUSIONS: Serum metabolites are promising biomarkers to uncover and predict multimetabolic phenotypes of subclinical atherosclerosis in SLE