Untargeted screening for novel autoantibodies with prognostic value in first-episode psychosis

Spectrum of Novel Anti-Central Nervous System Autoantibodies in the Cerebrospinal Fluid of 119 Patients With Schizophreniform and Affective Disorders

BACKGROUND

Autoimmune psychosis may be caused by well-characterized anti-neuronal autoantibodies, such as those against the NMDA receptor. However, the presence of additional anti-central nervous system (CNS) autoantibodies in these patients has not been systematically assessed.

METHODS

Serum and cerebrospinal fluid (CSF) from patients with schizophreniform and affective syndromes were analyzed for immunoglobulin G anti-CNS autoantibodies using tissue-based assays with indirect immunofluorescence on unfixed murine brain tissue as part of an extended routine clinical practice. After an initial assessment of patients with red flags for autoimmune psychosis (n = 30), tissue-based testing was extended to a routine procedure (n = 89).

RESULTS

Based on the findings from all 119 patients, anti-CNS immunoglobulin G autoantibodies against brain tissue were detected in 18% (n = 22) of patients (serum 9%, CSF 18%) following five principal patterns: 1) against vascular structures, most likely endothelial cells (serum 3%, CSF 8%); 2) against granule cells in the cerebellum and/or hippocampus (serum 4%, CSF 6%); 3) against myelinated fibers (serum 2%, CSF 2%); 4) against cerebellar Purkinje cells (serum 0%, CSF 2%); and 5) against astrocytes (serum 1%, CSF 1%). The patients with novel anti-CNS autoantibodies showed increased albumin quotients (p = .026) and white matter changes (p = .020) more frequently than those who tested negative for autoantibodies.

CONCLUSIONS

The study demonstrates five novel autoantibody-binding patterns on brain tissue of patients with schizophreniform and affective syndromes. CSF yielded positive findings more frequently than serum analysis. The frequency and spectrum of autoantibodies in these patient groups may be broader than previously thought.

Autoantibody-associated psychiatric symptoms and syndromes in adults: A narrative review and proposed diagnostic approach

Background

Autoimmune-mediated encephalitis is a disease that often encompasses psychiatric symptoms as its first clinical manifestation’s predominant and isolated characteristic. Novel guidelines even distinguish autoimmune psychosis from autoimmune encephalitis. The aim of this review is thus to explore whether a wide range of psychiatric symptoms and syndromes are associated or correlate with autoantibodies.

Methods

We conducted a PubMed search to identify appropriate articles concerning serum and/or cerebrospinal fluid (CSF) autoantibodies associated with psychiatric symptoms and syndromes between 2000 and 2020. Relying on this data, we developed a diagnostic approach to optimize the detection of autoantibodies in psychiatric patients, potentially leading to the approval of an immunotherapy.

Results

We detected 10 major psychiatric symptoms and syndromes often reported to be associated with serum and/or CSF autoantibodies comprising altered consciousness, disorientation, memory impairment, obsessive-compulsive behavior, psychosis, catatonia, mood dysfunction, anxiety, behavioral abnormalities (autism, hyperkinetic), and sleeping dysfunction. The following psychiatric diagnoses were associated with serum and/or CSF autoantibodies: psychosis and schizophrenia spectrum disorders, mood disorders, minor and major neurocognitive impairment, obsessive-compulsive disorder, autism spectrum disorders (ASD), attention deficit hyperactivity disorder (ADHD), anxiety disorders, eating disorders and addiction. By relying on these symptom clusters and diagnoses in terms of onset and their duration, we classified a subacute or subchronic psychiatric syndrome in patients that should be screened for autoantibodies. We propose further diagnostics entailing CSF analysis, electroencephalography and magnetic resonance imaging of the brain. Exploiting these technologies enables standardized and accurate diagnosis of autoantibody-associated psychiatric symptoms and syndromes to deliver early immunotherapy.

Conclusions

We have developed a clinical diagnostic pathway for classifying subgroups of psychiatric patients whose psychiatric symptoms indicate a suspected autoimmune origin.

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Autoantibodies are associated with a broad spectrum of psychiatric syndromes.

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More systematic studies are needed to elucidate the significance of autoantibodies.

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We developed a pathway to identify autoantibody-associated psychiatric syndromes.

Autoantibody profiles associated with clinical features in psychotic disorders

Autoimmune processes are suspected to play a role in the pathophysiology of psychotic disorders. Better understanding of the associations between auto-immunoglobulin G (IgG) repertoires and clinical features of mental illness could yield novel models of the pathophysiology of psychosis, and markers for biological patient stratification. We undertook cross-sectional detection and quantification of auto-IgGs in peripheral blood plasma of 461 people (39% females) with established psychotic disorder diagnoses. Broad screening of 24 individuals was carried out on group level in eight clinically defined groups using planar protein microarrays containing 42,100 human antigens representing 18,914 proteins. Autoantibodies indicated by broad screening and in the previous literature were measured using a 380-plex bead-based array for autoantibody profiling of all 461 individuals. Associations between autoantibody profiles and dichotomized clinical characteristics were assessed using a stepwise selection procedure. Broad screening and follow-up targeted analyses revealed highly individual autoantibody profiles. Females, and people with family histories of obesity or of psychiatric disorders other than schizophrenia had the highest overall autoantibody counts. People who had experienced subjective thought disorder and/or were treated with clozapine (trend) had the lowest overall counts. Furthermore, six autoantibodies were associated with specific psychopathology symptoms: anti-AP3B2 (persecutory delusions), anti-TDO2 (hallucinations), anti-CRYGN (initial insomnia); anti-APMAP (poor appetite), anti-OLFM1 (above-median cognitive function), and anti-WHAMMP3 (anhedonia and dysphoria). Future studies should clarify whether there are causal biological relationships, and whether autoantibodies could be used as clinical markers to inform diagnostic patient stratification and choice of treatment.

Exploring autoantibody signatures in brain tissue from patients with severe mental illness

In recent years, studies have shown higher prevalence of autoantibodies in patients with schizophrenia compared to healthy individuals. This study applies an untargeted and a targeted affinity proteomics approach to explore and characterize the autoantibody repertoire in brain tissues from 73 subjects diagnosed with schizophrenia and 52 control subjects with no psychiatric or neurological disorders. Selected brain tissue lysates were first explored for IgG reactivity on planar microarrays composed of 11,520 protein fragments representing 10,820 unique proteins. Based on these results of ours and other previous studies of autoantibodies related to psychosis, we selected 226 fragments with an average length of 80 amino acids, representing 127 unique proteins. Tissue-based analysis of IgG reactivities using antigen suspension bead arrays was performed in a multiplex and parallel fashion for all 125 subjects. Among the detected autoantigens, higher IgG reactivity in subjects with schizophrenia, as compared to psychiatrically healthy subjects, was found against the glutamate ionotropic receptor NMDA type subunit 2D (anti-GluN2D). In a separate cohort with serum samples from 395 young adults with a wider spectrum of psychiatric disorders, higher levels of serum autoantibodies targeting GluN2D were found when compared to 102 control individuals. By further validating GluN2D and additional potential autoantigens, we will seek insights into how these are associated with severe mental illnesses.

Transition of Substance-Induced, Brief, and Atypical Psychoses to Schizophrenia: A Systematic Review and Meta-analysis

Some people who experience substance-induced psychosis later develop an enduring psychotic disorder such as schizophrenia. This study examines the proportion of people with substance-induced psychoses who transition to schizophrenia, compares this to other brief and atypical psychoses, and examines moderators of this risk. A search of MEDLINE, PsychINFO, and Embase identified 50 eligible studies, providing 79 estimates of transition to schizophrenia among 40 783 people, including 25 studies providing 43 substance-specific estimates in 34 244 people. The pooled proportion of transition from substance-induced psychosis to schizophrenia was 25% (95% CI 18%-35%), compared with 36% (95% CI 30%-43%) for brief, atypical and not otherwise specified psychoses. Type of substance was the primary predictor of transition from drug-induced psychosis to schizophrenia, with highest rates associated with cannabis (6 studies, 34%, CI 25%-46%), hallucinogens (3 studies, 26%, CI 14%-43%) and amphetamines (5 studies, 22%, CI 14%-34%). Lower rates were reported for opioid (12%), alcohol (10%) and sedative (9%) induced psychoses. Transition rates were slightly lower in older cohorts but were not affected by sex, country of the study, hospital or community location, urban or rural setting, diagnostic methods, or duration of follow-up. Substance-induced psychoses associated with cannabis, hallucinogens, and amphetamines have a substantial risk of transition to schizophrenia and should be a focus for assertive psychiatric intervention.

Moringin Pretreatment Inhibits the Expression of Genes Involved in Mitophagy in the Stem Cell of the Human Periodontal Ligament

Moringin [4-(α-L-rhamnosyloxy) benzyl isothiocyanate] is an isothiocyanate extracted from Moringa oleifera seeds. It is an antioxidant known for several biological properties useful in the treatment of neurodegenerative diseases. Several neurodegenerative disorders such as Parkinson’s and Alzheimer’s diseases are linked to dysfunctional mitochondria due to the resulting increase of Reactive Oxygen Species (ROS). Stem cell-based therapeutic treatments in neurodegenerative diseases provide an alternative strategy aimed to replace the impaired tissue. In this study were investigated the deregulated genes involved in mitophagy in the human periodontal ligament stem cells pretreated with moringin. The RNA-seq study reveals the downregulation of PINK1, with a fold change (FC) of −0.56, such as the genes involved in the phagophore formation (MAP1LC3B FC: −0.73, GABARAP FC: −0.52, GABARAPL1 FC: −0.70, GABARAPL2 FC: −0.39). The moringin pretreatment downregulates the pro−apoptotic gene BAX (−0.66) and upregulates the anti-apoptotic genes BCL2L12 (FC: 1.35) and MCL1 (FC: 0.36). The downregulation of the most of the caspases (CASP1 FC: −1.43, CASP4 FC: −0.18, CASP6 FC: −1.34, CASP7 FC: −0.46, CASP8 FC: −0.65) implies the inactivation of the apoptotic process. Our results suggest that mitochondrial dysfunctions induced by oxidative stress can be inhibited by moringin pretreatment in human periodontal ligament stem cells (hPDLSCs).

Oscillatory Dynamics of Feedback Processing in Adolescents with Prenatal Cocaine Exposure

Prenatal cocaine exposure (PCE) has ramifications for feedback processing. Measuring neural oscillatory dynamics (during electroencephalography) provides insight into the time signatures and neural processes of feedback processing in adolescents with PCE. We measured spectral power in alpha and theta frequency bands while 49 adolescents with PCE and 34 non-drug exposed (NDE) performed a task with win/no-win feedback. Compared to NDE individuals, those with PCE showed reduced alpha power and increased theta power in response to no-win feedback. These findings suggest altered reactivity in PCE adolescents.

Individual and stable autoantibody repertoires in healthy individuals

In the era towards precision medicine, we here present the individual specific autoantibody signatures of 193 healthy individuals. The self-reactive IgG signatures are stable over time in a way that each individual profile is recognized in longitudinal sampling. The IgG autoantibody reactivity towards an antigen array comprising 335 protein fragments, representing 204 human proteins with potential relevance to autoimmune disorders, was measured in longitudinal plasma samples from 193 healthy individuals. This analysis resulted in unique autoantibody barcodes for each individual that were maintained over one year's time. The reactivity profiles, or signatures, are person specific in regards to the number of reactivities and antigen specificity. Two independent data sets were consistent in that each healthy individual displayed reactivity towards 0-16 antigens, with a median of six. Subsequently, four selected individuals were profiled on in-house produced high-density protein arrays containing 23,000 protein fragments representing 14,000 unique protein coding genes. Based on a unique, broad and deep longitudinal profiling of autoantibody reactivities, our results demonstrate a unique autoreactive profile in each analyzed healthy individual. The need and interest for broad-ranged and high-resolution molecular profiling of healthy individuals is rising. We have here generated and assessed an initial perspective on the global distribution of the self-reactive IgG repertoire in healthy individuals, by investigating 193 well-characterized healthy individuals. Highlights A unique longitudinal profiling of autoantibody repertoires in healthy individuals Autoantibody profiles are highly individual and stable over time All individuals display IgG binding to human protein fragments The specificity of disease associated autoantigens needs to be thoroughly characterized The identification of a small set of highly reactive autoantigens Importance of stringent antigen and sample specific cut-offs for defining reactivity.

Antibodies in the Diagnosis, Prognosis, and Prediction of Psychotic Disorders

Blood-based biomarker discovery for psychotic disorders has yet to impact upon routine clinical practice. In physical disorders antibodies have established roles as diagnostic, prognostic and predictive (theranostic) biomarkers, particularly in disorders thought to have a substantial autoimmune or infective aetiology. Two approaches to antibody biomarker identification are distinguished: a "top-down" approach, in which antibodies to specific antigens are sought based on the known function of the antigen and its putative role in the disorder, and emerging "bottom-up" or "omics" approaches that are agnostic as to the significance of any one antigen, using high-throughput arrays to identify distinctive components of the antibody repertoire. Here we review the evidence for antibodies (to self-antigens as well as infectious organism and dietary antigens) as biomarkers of diagnosis, prognosis, and treatment response in psychotic disorders. Neuronal autoantibodies have current, and increasing, clinical utility in the diagnosis of organic or atypical psychosis syndromes. Antibodies to selected infectious agents show some promise in predicting cognitive impairment and possibly other symptom domains (eg, suicidality) within psychotic disorders. Finally, infectious antibodies and neuronal and other autoantibodies have recently emerged as potential biomarkers of response to anti-infective therapies, immunotherapies, or other novel therapeutic strategies in psychotic disorders, and have a clear role in stratifying patients for future clinical trials. As in nonpsychiatric disorders, combining biomarkers and large-scale use of "bottom-up" approaches to biomarker identification are likely to maximize the eventual clinical utility of antibody biomarkers in psychotic disorders.

Array-Based Profiling of Proteins and Autoantibody Repertoires in CSF

Protein profiling enabled through affinity proteomics represents a powerful strategy for analysis of complex samples such as human body fluids. Cerebrospinal fluid (CSF) is the proximal fluid of the central nervous system and is commonly analyzed in the context of neurological diseases. Through the presence of brain-derived proteins, this fluid can offer insight into the physiological state of the brain. Here, we describe multiplex and flexible protein and autoantibody profiling approaches using suspension bead arrays. Through minimal sample processing, these methods enable high-throughput analysis of hundreds of samples and proteins in one single assay and thereby provide powerful approaches for discovery of disease-associated proteins and autoantigens.

Targeting mast cell as a neuroprotective strategy

Background: Mast cells (MCs) are perivascularly located immune cells of haematopoietic origin. Emerging evidences suggest that the activation of MCs play important roles in the pathogenesis of blood brain barrier disruption, neuroinflammation, and neurodegeneration. Objectives: In this review, we aimed to discuss the detrimental effects of MCs in response to various types of brain injury, as well as the therapeutic potential and neuroprotective effects of targeting the activation and degranulation of MCs, particularly in the management of the acute phase. Methods: An extensive online literature search was conducted through Pubmed/Central on March 2018. Then, we comprehensively summarized the effects of the activation of brain MCs in acute brain injury along with current pharmacological strategies targeting at the activation of MCs. Results: The review of the current literature indicated that the activation and degranulation of brain MCs significantly contribute to the acute pathological process following different types of brain injury including focal and global cerebral ischaemia, intracerebral haemorrhage, subarachnoid haemorrhage, and traumatic brain injury. Conclusions: Brain MCs significantly contribute to the acute pathological processes following brain injury. In that regard, targeting brain MCs may provide a novel strategy for neuroprotection.

Is It Possible to Predict the Future in First-Episode Psychosis?

The outcome of first-episode psychosis (FEP) is highly variable, ranging from early sustained recovery to antipsychotic treatment resistance from the onset of illness. For clinicians, a possibility to predict patient outcomes would be highly valuable for the selection of antipsychotic treatment and in tailoring psychosocial treatments and psychoeducation. This selective review summarizes current knowledge of prognostic markers in FEP. We sought potential outcome predictors from clinical and sociodemographic factors, cognition, brain imaging, genetics, and blood-based biomarkers, and we considered different outcomes, like remission, recovery, physical comorbidities, and suicide risk. Based on the review, it is currently possible to predict the future for FEP patients to some extent. Some clinical features-like the longer duration of untreated psychosis (DUP), poor premorbid adjustment, the insidious mode of onset, the greater severity of negative symptoms, comorbid substance use disorders (SUDs), a history of suicide attempts and suicidal ideation and having non-affective psychosis-are associated with a worse outcome. Of the social and demographic factors, male gender, social disadvantage, neighborhood deprivation, dysfunctional family environment, and ethnicity may be relevant. Treatment non-adherence is a substantial risk factor for relapse, but a small minority of patients with acute onset of FEP and early remission may benefit from antipsychotic discontinuation. Cognitive functioning is associated with functional outcomes. Brain imaging currently has limited utility as an outcome predictor, but this may change with methodological advancements. Polygenic risk scores (PRSs) might be useful as one component of a predictive tool, and pharmacogenetic testing is already available and valuable for patients who have problems in treatment response or with side effects. Most blood-based biomarkers need further validation. None of the currently available predictive markers has adequate sensitivity or specificity used alone. However, personalized treatment of FEP will need predictive tools. We discuss some methodologies, such as machine learning (ML), and tools that could lead to the improved prediction and clinical utility of different prognostic markers in FEP. Combination of different markers in ML models with a user friendly interface, or novel findings from e.g., molecular genetics or neuroimaging, may result in computer-assisted clinical applications in the near future.