Monozygotic twins discordant for schizophrenia differ in maturation and synaptic transmission.

Schizophrenia affects approximately 1% of the world population. Genetics, epigenetics, and environmental factors are known to play a role in this psychiatric disorder. While there is a high concordance in monozygotic twins, about half of twin pairs are discordant for schizophrenia. To address the question of how and when concordance in monozygotic twins occur, we have obtained fibroblasts from two pairs of schizophrenia discordant twins (one sibling with schizophrenia while the second one is unaffected by schizophrenia) and three pairs of healthy twins (both of the siblings are healthy). We have prepared iPSC models for these 3 groups of patients with schizophrenia, unaffected co-twins, and the healthy twins. When the study started the co-twins were considered healthy and unaffected but both the co-twins were later diagnosed with a depressive disorder. The reprogrammed iPSCs were differentiated into hippocampal neurons to measure the neurophysiological abnormalities in the patients. We found that the neurons derived from the schizophrenia patients were less arborized, were hypoexcitable with immature spike features, and exhibited a significant reduction in synaptic activity with dysregulation in synapse-related genes. Interestingly, the neurons derived from the co-twin siblings who did not have schizophrenia formed another distinct group that was different from the neurons in the group of the affected twin siblings but also different from the neurons in the group of the control twins. Importantly, their synaptic activity was not affected. Our measurements that were obtained from schizophrenia patients and their monozygotic twin and compared also to control healthy twins point to hippocampal synaptic deficits as a central mechanism in schizophrenia.

Meta-analysis of epigenetic aging in schizophrenia reveals multifaceted relationships with age, sex, illness duration, and polygenic risk.

BACKGROUND: The study of biological age acceleration may help identify at-risk individuals and reduce the rising global burden of age-related diseases. Using DNA methylation (DNAm) clocks, we investigated biological aging in schizophrenia (SCZ), a mental illness that is associated with an increased prevalence of age-related disabilities and morbidities. In a whole blood DNAm sample of 1090 SCZ cases and 1206 controls across four European cohorts, we performed a meta-analysis of differential aging using three DNAm clocks (i.e., Hannum, Horvath, and Levine). To dissect how DNAm aging contributes to SCZ, we integrated information on duration of illness and SCZ polygenic risk, as well as stratified our analyses by chronological age and biological sex. RESULTS: We found that blood-based DNAm aging is significantly altered in SCZ independent from duration of the illness since onset. We observed sex-specific and nonlinear age effects that differed between clocks and point to possible distinct age windows of altered aging in SCZ. Most notably, intrinsic cellular age (Horvath clock) is decelerated in SCZ cases in young adulthood, while phenotypic age (Levine clock) is accelerated in later adulthood compared to controls. Accelerated phenotypic aging was most pronounced in women with SCZ carrying a high polygenic burden with an age acceleration of + 3.82 years (CI 2.02-5.61, P = 1.1E-03). Phenotypic aging and SCZ polygenic risk contributed additively to the illness and together explained up to 14.38% of the variance in disease status. CONCLUSIONS: Our study contributes to the growing body of evidence of altered DNAm aging in SCZ and points to intrinsic age deceleration in younger adulthood and phenotypic age acceleration in later adulthood in SCZ. Since increased phenotypic age is associated with increased risk of all-cause mortality, our findings indicate that specific and identifiable patient groups are at increased mortality risk as measured by the Levine clock. Our study did not find that DNAm aging could be explained by the duration of illness of patients, but we did observe age- and sex-specific effects that warrant further investigation. Finally, our results show that combining genetic and epigenetic predictors can improve predictions of disease outcomes and may help with disease management in schizophrenia.

Accelerated Cortical Thinning in Schizophrenia Is Associated With Rare and Common Predisposing Variation to Schizophrenia and Neurodevelopmental Disorders.

BACKGROUND: Schizophrenia is a highly heritable disorder characterized by increased cortical thinning throughout the life span. Studies have reported a shared genetic basis between schizophrenia and cortical thickness. However, no genes whose expression is related to abnormal cortical thinning in schizophrenia have been identified. METHODS: We conducted linear mixed models to estimate the rates of accelerated cortical thinning across 68 regions from the Desikan-Killiany atlas in individuals with schizophrenia compared with healthy control participants from a large longitudinal sample (ncases = 169 and ncontrols = 298, ages 16-70 years). We studied the correlation between gene expression data from the Allen Human Brain Atlas and accelerated thinning estimates across cortical regions. Finally, we explored the functional and genetic underpinnings of the genes that contribute most to accelerated thinning. RESULTS: We found a global pattern of accelerated cortical thinning in individuals with schizophrenia compared with healthy control participants. Genes underexpressed in cortical regions that exhibit this accelerated thinning were downregulated in several psychiatric disorders and were enriched for both common and rare disrupting variation for schizophrenia and neurodevelopmental disorders. In contrast, none of these enrichments were observed for baseline cross-sectional cortical thickness differences. CONCLUSIONS: Our findings suggest that accelerated cortical thinning, rather than cortical thickness alone, serves as an informative phenotype for neurodevelopmental disruptions in schizophrenia. We highlight the genetic and transcriptomic correlates of this accelerated cortical thinning, emphasizing the need for future longitudinal studies to elucidate the role of genetic variation and the temporal-spatial dynamics of gene expression in brain development and aging in schizophrenia.

A naturalistic cohort study of first-episode schizophrenia spectrum disorder: A description of the early phase of illness in the PSYSCAN cohort.

BACKGROUND: We examined the course of illness over a 12-month period in a large, international multi-center cohort of people with a first-episode schizophrenia spectrum disorder (FES) in a naturalistic, prospective study (PSYSCAN). METHOD: Patients with a first episode of schizophrenia, schizoaffective disorder (depressive type) or schizophreniform disorder were recruited at 16 institutions in Europe, Israel and Australia. Participants (N = 304) received clinical treatment as usual throughout the study. RESULTS: The mean age of the cohort was 24.3 years (SD = 5.6), and 67 % were male. At baseline, participants presented with a range of intensities of psychotic symptoms, 80 % were taking antipsychotic medication, 68 % were receiving psychological treatment, with 46.5 % in symptomatic remission. The mean duration of untreated psychosis was 6.2 months (SD = 17.0). After one year, 67 % were in symptomatic remission and 61 % were in functional remission, but 31 % had been readmitted to hospital at some time after baseline. In the cohort as a whole, depressive symptoms remained stable over the follow-up period. In patients with a current depressive episode at baseline, depressive symptoms slightly improved. Alcohol, tobacco and cannabis were the most commonly used substances, with daily users of cannabis ranging between 9 and 11 % throughout the follow-up period. CONCLUSIONS: This study provides valuable insight into the early course of a broad range of clinical and functional aspects of illness in FES patients in routine clinical practice.

Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ): Rationale and Study Design of the Largest Global Prospective Cohort Study of Clinical High Risk for Psychosis.

This article describes the rationale, aims, and methodology of the Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ). This is the largest international collaboration to date that will develop algorithms to predict trajectories and outcomes of individuals at clinical high risk (CHR) for psychosis and to advance the development and use of novel pharmacological interventions for CHR individuals. We present a description of the participating research networks and the data processing analysis and coordination center, their processes for data harmonization across 43 sites from 13 participating countries (recruitment across North America, Australia, Europe, Asia, and South America), data flow and quality assessment processes, data analyses, and the transfer of data to the National Institute of Mental Health (NIMH) Data Archive (NDA) for use by the research community. In an expected sample of approximately 2000 CHR individuals and 640 matched healthy controls, AMP SCZ will collect clinical, environmental, and cognitive data along with multimodal biomarkers, including neuroimaging, electrophysiology, fluid biospecimens, speech and facial expression samples, novel measures derived from digital health technologies including smartphone-based daily surveys, and passive sensing as well as actigraphy. The study will investigate a range of clinical outcomes over a 2-year period, including transition to psychosis, remission or persistence of CHR status, attenuated positive symptoms, persistent negative symptoms, mood and anxiety symptoms, and psychosocial functioning. The global reach of AMP SCZ and its harmonized innovative methods promise to catalyze the development of new treatments to address critical unmet clinical and public health needs in CHR individuals.

Exploring peripheral biomarkers of response to simvastatin supplementation in schizophrenia.

Schizophrenia is one of the most debilitating mental disorders, and its diagnosis and treatment present significant challenges. Several clinical trials have previously evaluated the effectiveness of simvastatin, a lipid-lowering medication, as a novel add-on treatment for schizophrenia. However, treatment effects varied highly between patients and over time. In the present study, we aimed to identify biomarkers of response to simvastatin in recent-onset schizophrenia patients. To this end, we profiled relevant immune and metabolic markers in patient blood samples collected in a previous clinical trial (ClinicalTrials.gov: NCT01999309) before simvastatin add-on treatment was initiated. Analysed sample types included serum, plasma, resting-state peripheral blood mononuclear cells (PBMCs), as well as PBMC samples treated ex vivo with immune stimulants and simvastatin. Associations between the blood readouts and clinical endpoints were evaluated using multivariable linear regression. This revealed that changes in insulin receptor (IR) levels induced in B-cells by ex vivo simvastatin treatment inversely correlated with in vivo effects on cognition at the primary endpoint of 12 months, as measured using the Brief Assessment of Cognition in Schizophrenia scale total score (standardised ß ± SE = -0.75 ± 0.16, P = 2.2 × 10-4, Q = 0.029; n = 21 patients). This correlation was not observed in the placebo group (ß ± SE = 0.62 ± 0.39, P = 0.17, Q = 0.49; n = 14 patients). The candidate biomarker explained 53.4 % of the variation in cognitive outcomes after simvastatin supplementation. Despite the small sample size, these findings suggest a possible interaction between the insulin signalling pathway and cognitive effects during simvastatin therapy. They also point to opportunities for personalized schizophrenia treatment through patient stratification.

Cell-type deconvolution of bulk-blood RNA-seq reveals biological insights into neuropsychiatric disorders.

Genome-wide association studies (GWASs) have uncovered susceptibility loci associated with psychiatric disorders such as bipolar disorder (BP) and schizophrenia (SCZ). However, most of these loci are in non-coding regions of the genome, and the causal mechanisms of the link between genetic variation and disease risk is unknown. Expression quantitative trait locus (eQTL) analysis of bulk tissue is a common approach used for deciphering underlying mechanisms, although this can obscure cell-type-specific signals and thus mask trait-relevant mechanisms. Although single-cell sequencing can be prohibitively expensive in large cohorts, computationally inferred cell-type proportions and cell-type gene expression estimates have the potential to overcome these problems and advance mechanistic studies. Using bulk RNA-seq from 1,730 samples derived from whole blood in a cohort ascertained from individuals with BP and SCZ, this study estimated cell-type proportions and their relation with disease status and medication. For each cell type, we found between 2,875 and 4,629 eGenes (genes with an associated eQTL), including 1,211 that are not found on the basis of bulk expression alone. We performed a colocalization test between cell-type eQTLs and various traits and identified hundreds of associations that occur between cell-type eQTLs and GWASs but that are not detected in bulk eQTLs. Finally, we investigated the effects of lithium use on the regulation of cell-type expression loci and found examples of genes that are differentially regulated according to lithium use. Our study suggests that applying computational methods to large bulk RNA-seq datasets of non-brain tissue can identify disease-relevant, cell-type-specific biology of psychiatric disorders and psychiatric medication.

Familial risk of postpartum psychosis.

Objective: Postpartum psychosis, a mood disorder triggered by childbirth, is one of the most severe psychiatric conditions, with high risks of suicide and infanticide if untreated. While it is evident that genetic factors play a crucial role in disorder risk, the exact extent of their importance is yet to be determined. Methods: This cohort study consisted of 1,648,759 women from the Swedish nationwide registers, of whom 2,514 (0.15%) experienced postpartum psychosis within three months of their first-ever childbirth. We estimated the relative recurrence risk of postpartum psychosis for female full siblings and cousins as a measure of familial, genetic, and environmental risk. Results: Relative recurrence risk of postpartum psychosis in full siblings was 10.69 (95% CI=6.60-16.26) when adjusted for year of and age at childbirth. Although cousins showed an elevated relative recurrence risk, these results did not reach statistical significance (1.78, 95% CI=0.70-3.62). Despite the higher familial risk of postpartum psychosis among full siblings, the absolute risk for women with an affected sibling is relatively low, estimated at 1.55% within the entire population. Conclusions: The observed increased risk of postpartum psychosis in full siblings suggests both genetic and shared environmental influences. However, the lack of significant results in cousins hampers a definitive distinction between these factors. Furthermore, despite increased relative recurrence risk in siblings, their overall likelihood of developing postpartum psychosis remains low. Our study underscores the need for further research to better understand the intricate interplay of genetics and environment in the development of postpartum psychosis.

Relapse in clinically stable adult patients with schizophrenia or schizoaffective disorder: evidence-based criteria derived by equipercentile linking and diagnostic test accuracy meta-analysis.

BACKGROUND: There is no consensus on defining relapse in schizophrenia, and scale-derived criteria with unclear clinical relevance are widely used. We aimed to develop an evidence-based scale-derived set of criteria to define relapse in patients with schizophrenia or schizoaffective disorder. METHODS: We searched the Yale University Open Data Access (YODA) for randomised controlled trials (RCTs) in clinically stable adults with schizophrenia or schizoaffective disorder, and obtained individual participant data on Positive and Negative Syndrome Scale (PANSS), Clinical Global Impression Severity (CGI-S), Personal and Social Performance (PSP), and Social and Occupational Functioning Assessment Scale (SOFAS). Our main outcomes were PANSS-derived criteria based on worsening in PANSS total score. We examined their relevance using equipercentile linking with CGI-S and functioning scales, and their test-performance in defining relapse with diagnostic test accuracy meta-analysis against CGI-S worsening (≥1-point increase together with a score ≥4 points) and psychiatric hospitalisation. FINDINGS: Based on data from seven RCTs (2354 participants; 1348 men [57·3%] and 1006 women [42·7%], mean age of 39·5 years [SD 12·0, range 17-89]; 303 Asian [12.9%], 255 Black [10.8%], 1665 White [70.7%], and other or unspecified 131 [5.6%]), an increase of 12 points or more in PANSS total (range 30-210 points) corresponded to clinically important deterioration in global severity of illness (≥1 point increase in CGI-S, range 1-7) and functioning (≥10 points decline in PSP or SOFAS, range 1-100). The interpretation of percentage changes varied importantly across different baseline scores. An increase of 12 points or more in PANSS total had good sensitivity and specificity using CGI-S as reference standard (sensitivity 82·1% [95% CI 77·1-86·4], specificity 86·9% [82·9-90·3]), as well as good sensitivity but lower specificity compared to hospitalisation (sensitivity 81·7% [74·1-87·7], specificity 69·2% [60·5-76·9]). Requiring either an increase in PANSS total or in specific items for positive and disorganization symptoms further improved test-performance. Cutoffs for situations where high sensitivity or specificity is needed are presented. INTERPRETATION: An increase of either 12 points or more in the PANSS total score, or worsening of specific positive and disorganisation symptom items could be a reasonable evidence-based definition of relapse in schizophrenia, potentially linking symptoms used to define remission and relapse. Percentage changes should not be used to define relapse because their interpretation depends on baseline scores. FUNDING: German Research Foundation (grant number: 428509362).

Extreme deviations from the normative model reveal cortical heterogeneity and associations with negative symptom severity in first-episode psychosis from the OPTiMiSE and GAP studies.

There is currently no quantifiable method to predict long-term clinical outcomes in patients presenting with a first episode of psychosis. A major barrier to developing useful markers for this is biological heterogeneity, where many different pathological mechanisms may underly the same set of symptoms in different individuals. Normative modelling has been used to quantify this heterogeneity in established psychotic disorders by identifying regions of the cortex which are thinner than expected based on a normative healthy population range. These brain atypicalities are measured at the individual level and therefore potentially useful in a clinical setting. However, it is still unclear whether alterations in individual brain structure can be detected at the time of the first psychotic episode, and whether they are associated with subsequent clinical outcomes. We applied normative modelling of cortical thickness to a sample of first-episode psychosis patients, with the aim of quantifying heterogeneity and to use any pattern of cortical atypicality to predict symptoms and response to antipsychotic medication at timepoints from baseline up to 95 weeks (median follow-ups = 4). T1-weighted brain magnetic resonance images from the GAP and OPTiMiSE samples were processed with Freesurfer V6.0.0 yielding 148 cortical thickness features. An existing normative model of cortical thickness (n = 37,126) was adapted to integrate data from each clinical site and account for effects of gender and site. Our test sample consisted of control participants (n = 149, mean age = 26, SD = 6.7) and patient data (n = 295, mean age = 26, SD = 6.7), this sample was used for estimating deviations from the normative model and subsequent statistical analysis. For each individual, the 148 cortical thickness features were mapped to centiles of the normative distribution and converted to z-scores reflecting the distance from the population mean. Individual cortical thickness metrics of +/- 2.6 standard deviations from the mean were considered extreme deviations from the norm. We found that no more than 6.4% of psychosis patients had extreme deviations in a single brain region (regional overlap) demonstrating a high degree of heterogeneity. Mann-Whitney U tests were run on z-scores for each region and significantly lower z-scores were observed in FEP patients in the frontal, temporal, parietal and occipital lobes. Finally, linear mixed-effects modelling showed that negative deviations in cortical thickness in parietal and temporal regions at baseline are related to more severe negative symptoms over the medium-term. This study shows that even at the early stage of symptom onset normative modelling provides a framework to identify individualised cortical markers which can be used for early personalised intervention and stratification.

Genomic loci influence patterns of structural covariance in the human brain.

Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to patterns of structural covariance (PSC) across brain regions and individuals during brain aging and diseases. The genetic underpinnings of these patterns remain largely unknown. We apply a stochastic multivariate factorization method to a diverse population of 50,699 individuals (12 studies and 130 sites) and derive data-driven, multi-scale PSCs of regional brain size. PSCs were significantly correlated with 915 genomic loci in the discovery set, 617 of which are newly identified, and 72% were independently replicated. Key pathways influencing PSCs involve reelin signaling, apoptosis, neurogenesis, and appendage development, while pathways of breast cancer indicate potential interplays between brain metastasis and PSCs associated with neurodegeneration and dementia. Using support vector machines, multi-scale PSCs effectively derive imaging signatures of several brain diseases. Our results elucidate genetic and biological underpinnings that influence structural covariance patterns in the human brain.

Childhood abuse v. neglect and risk for major psychiatric disorders.

BACKGROUND: Childhood maltreatment (CM) is a strong risk factor for psychiatric disorders but serves in its current definitions as an umbrella for various fundamentally different childhood experiences. As first step toward a more refined analysis of the impact of CM, our objective is to revisit the relation of abuse and neglect, major subtypes of CM, with symptoms across disorders. METHODS: Three longitudinal studies of major depressive disorder (MDD, N = 1240), bipolar disorder (BD, N = 1339), and schizophrenia (SCZ, N = 577), each including controls (N = 881), were analyzed. Multivariate regression models were used to examine the relation between exposure to abuse, neglect, or their combination to the odds for MDD, BD, SCZ, and symptoms across disorders. Bidirectional Mendelian randomization (MR) was used to probe causality, using genetic instruments of abuse and neglect derived from UK Biobank data (N = 143 473). RESULTS: Abuse was the stronger risk factor for SCZ (OR 3.51, 95% CI 2.17-5.67) and neglect for BD (OR 2.69, 95% CI 2.09-3.46). Combined CM was related to increased risk exceeding additive effects of abuse and neglect for MDD (RERI = 1.4) and BD (RERI = 1.1). Across disorders, abuse was associated with hallucinations (OR 2.16, 95% CI 1.55-3.01) and suicide attempts (OR 2.16, 95% CI 1.55-3.01) whereas neglect was associated with agitation (OR 1.24, 95% CI 1.02-1.51) and reduced need for sleep (OR 1.64, 95% CI 1.08-2.48). MR analyses were consistent with a bidirectional causal effect of abuse with SCZ (IVWforward = 0.13, 95% CI 0.01-0.24). CONCLUSIONS: Childhood abuse and neglect are associated with different risks to psychiatric symptoms and disorders. Unraveling the origin of these differences may advance understanding of disease etiology and ultimately facilitate development of improved personalized treatment strategies.

Revealing the impact of psychiatric comorbidities on treatment outcome in early psychosis using counterfactual model explanation.

Introduction: Psychiatric comorbidities have a significant impact on the course of illness in patients with schizophrenia spectrum disorders. To accurately predict outcomes for individual patients using computerized prognostic models, it is essential to consider these comorbidities and their influence. Methods: In our study, we utilized a multi-modal deep learning architecture to forecast symptomatic remission, focusing on a multicenter sample of patients with first-episode psychosis from the OPTiMiSE study. Additionally, we introduced a counterfactual model explanation technique to examine how scores on the Mini International Neuropsychiatric Interview (MINI) affected the likelihood of remission, both at the group level and for individual patients. Results: Our findings at the group level revealed that most comorbidities had a negative association with remission. Among them, current and recurrent depressive disorders consistently exerted the greatest negative impact on the probability of remission across patients. However, we made an interesting observation: current suicidality within the past month and substance abuse within the past 12 months were associated with an increased chance of remission in patients. We found a high degree of variability among patients at the individual level. Through hierarchical clustering analysis, we identified two subgroups: one in which comorbidities had a relatively limited effect on remission (approximately 45% of patients), and another in which comorbidities more strongly influenced remission. By incorporating comorbidities into individualized prognostic prediction models, we determined which specific comorbidities had the greatest impact on remission at both the group level and for individual patients. Discussion: These results highlight the importance of identifying and including relevant comorbidities in prediction models, providing valuable insights for improving the treatment and prognosis of patients with psychotic disorders. Furthermore, they open avenues for further research into the efficacy of treating these comorbidities to enhance overall patient outcomes.

Psychosis Endophenotypes: A Gene-Set-Specific Polygenic Risk Score Analysis.

BACKGROUND AND HYPOTHESIS: Endophenotypes can help to bridge the gap between psychosis and its genetic predispositions, but their underlying mechanisms remain largely unknown. This study aims to identify biological mechanisms that are relevant to the endophenotypes for psychosis, by partitioning polygenic risk scores into specific gene sets and testing their associations with endophenotypes. STUDY DESIGN: We computed polygenic risk scores for schizophrenia and bipolar disorder restricted to brain-related gene sets retrieved from public databases and previous publications. Three hundred and seventy-eight gene-set-specific polygenic risk scores were generated for 4506 participants. Seven endophenotypes were also measured in the sample. Linear mixed-effects models were fitted to test associations between each endophenotype and each gene-set-specific polygenic risk score. STUDY RESULTS: After correction for multiple testing, we found that a reduced P300 amplitude was associated with a higher schizophrenia polygenic risk score of the forebrain regionalization gene set (mean difference per SD increase in the polygenic risk score: -1.15 µV; 95% CI: -1.70 to -0.59 µV; P = 6 × 10-5). The schizophrenia polygenic risk score of forebrain regionalization also explained more variance of the P300 amplitude (R2 = 0.032) than other polygenic risk scores, including the genome-wide polygenic risk scores. CONCLUSIONS: Our finding on reduced P300 amplitudes suggests that certain genetic variants alter early brain development thereby increasing schizophrenia risk years later. Gene-set-specific polygenic risk scores are a useful tool to elucidate biological mechanisms of psychosis and endophenotypes, offering leads for experimental validation in cellular and animal models.

Cell type deconvolution of bulk blood RNA-Seq to reveal biological insights of neuropsychiatric disorders.

Genome-wide association studies (GWAS) have uncovered susceptibility loci associated with psychiatric disorders like bipolar disorder (BP) and schizophrenia (SCZ). However, most of these loci are in non-coding regions of the genome with unknown causal mechanisms of the link between genetic variation and disease risk. Expression quantitative trait loci (eQTL) analysis of bulk tissue is a common approach to decipher underlying mechanisms, though this can obscure cell-type specific signals thus masking trait-relevant mechanisms. While single-cell sequencing can be prohibitively expensive in large cohorts, computationally inferred cell type proportions and cell type gene expression estimates have the potential to overcome these problems and advance mechanistic studies. Using bulk RNA-Seq from 1,730 samples derived from whole blood in a cohort ascertained for individuals with BP and SCZ this study estimated cell type proportions and their relation with disease status and medication. We found between 2,875 and 4,629 eGenes for each cell type, including 1,211 eGenes that are not found using bulk expression alone. We performed a colocalization test between cell type eQTLs and various traits and identified hundreds of associations between cell type eQTLs and GWAS loci that are not detected in bulk eQTLs. Finally, we investigated the effects of lithium use on cell type expression regulation and found examples of genes that are differentially regulated dependent on lithium use. Our study suggests that computational methods can be applied to large bulk RNA-Seq datasets of non-brain tissue to identify disease-relevant, cell type specific biology of psychiatric disorders and psychiatric medication.

Psychosis brain subtypes validated in first-episode cohorts and related to illness remission: results from the PHENOM consortium.

Using machine learning, we recently decomposed the neuroanatomical heterogeneity of established schizophrenia to discover two volumetric subgroups-a 'lower brain volume' subgroup (SG1) and an 'higher striatal volume' subgroup (SG2) with otherwise normal brain structure. In this study, we investigated whether the MRI signatures of these subgroups were also already present at the time of the first-episode of psychosis (FEP) and whether they were related to clinical presentation and clinical remission over 1-, 3-, and 5-years. We included 572 FEP and 424 healthy controls (HC) from 4 sites (Sao Paulo, Santander, London, Melbourne) of the PHENOM consortium. Our prior MRI subgrouping models (671 participants; USA, Germany, and China) were applied to both FEP and HC. Participants were assigned into 1 of 4 categories: subgroup 1 (SG1), subgroup 2 (SG2), no subgroup membership ('None'), and mixed SG1 + SG2 subgroups ('Mixed'). Voxel-wise analyses characterized SG1 and SG2 subgroups. Supervised machine learning analyses characterized baseline and remission signatures related to SG1 and SG2 membership. The two dominant patterns of 'lower brain volume' in SG1 and 'higher striatal volume' (with otherwise normal neuromorphology) in SG2 were identified already at the first episode of psychosis. SG1 had a significantly higher proportion of FEP (32%) vs. HC (19%) than SG2 (FEP, 21%; HC, 23%). Clinical multivariate signatures separated the SG1 and SG2 subgroups (balanced accuracy = 64%; p < 0.0001), with SG2 showing higher education but also greater positive psychosis symptoms at first presentation, and an association with symptom remission at 1-year, 5-year, and when timepoints were combined. Neuromorphological subtypes of schizophrenia are already evident at illness onset, separated by distinct clinical presentations, and differentially associated with subsequent remission. These results suggest that the subgroups may be underlying risk phenotypes that could be targeted in future treatment trials and are critical to consider when interpreting neuroimaging literature.

Gene expression profiling of monocytes in recent-onset schizophrenia.

Immune-related mechanisms have been suggested to be involved in schizophrenia. Various studies have shown changes in monocytes isolated from the blood of schizophrenia patients, including changes in monocyte numbers, as well as altered protein and transcript levels of important markers. However, validation of these findings and understanding how these results are related to immune-related changes in the brain and schizophrenia genetic risk factors, is limited. The goal of this study was to better understand changes observed in monocytes of patients with early-onset schizophrenia. Using RNA sequencing, we analyzed gene expression profiles of monocytes isolated from twenty patients with early-onset schizophrenia and seventeen healthy controls. We validated expression changes of 7 out of 29 genes that were differentially expressed in previous studies including TNFAIP3, DUSP2, and IL6. At a transcriptome-wide level, we found 99 differentially expressed genes. Effect sizes of differentially expressed genes were moderately correlated with differential expression in brain tissue (Pearson's r = 0.49). Upregulated genes were enriched for genes in NF-κB and LPS signaling pathways. Downregulated genes were enriched for glucocorticoid response pathways. These pathways have been implicated in schizophrenia before and play a role in regulating the activation of myeloid cells. Interestingly, they are also involved in several non-inflammatory processes in the central nervous system, such as neurogenesis and neurotransmission. Future studies are needed to better understand how dysregulation of the NF-κB and glucocorticoid pathways affects inflammatory and non-inflammatory processes in schizophrenia. The fact that dysregulation of these pathways is also seen in brain tissue, provides potential possibilities for biomarker development.

Development of the PSYCHS: Positive SYmptoms and Diagnostic Criteria for the CAARMS Harmonized with the SIPS.

Aim: To harmonize two ascertainment and severity rating instruments commonly used for the clinical high risk syndrome for psychosis (CHR-P): the Structured Interview for Psychosis-risk Syndromes (SIPS) and the Comprehensive Assessment of At-Risk Mental States (CAARMS). Methods: The initial workshop is described in the companion report from Addington et al. After the workshop, lead experts for each instrument continued harmonizing attenuated positive symptoms and criteria for psychosis and CHR-P through an intensive series of joint videoconferences. Results: Full harmonization was achieved for attenuated positive symptom ratings and psychosis criteria, and partial harmonization for CHR-P criteria. The semi-structured interview, named P ositive SY mptoms and Diagnostic Criteria for the C AARMS H armonized with the S IPS (PSYCHS), generates CHR-P criteria and severity scores for both CAARMS and SIPS. Conclusion: Using the PSYCHS for CHR-P ascertainment, conversion determination, and attenuated positive symptom severity rating will help in comparing findings across studies and in meta-analyses.