K. Oliver Schubert

The use of clinical and biological characteristics to predict outcome following First Episode Psychosis

Objective: Psychotic illnesses such as schizophrenia and other non-affective psychoses are heterogeneous in disease course and functional outcomes. We review evidence from investigations in clinical psychiatry, neuroimaging, neurocognition, and blood biomarker research suggesting that distinct bio-psycho-social patterns exist at the onset and during the early phase of a First Episode Psychosis (FEP), which can describe the risk of individual illness progression and functional trajectories. Method: A selective literature review was performed on articles drawn from Medline searches for relevant key words. A simulation model was constructed from data derived from two recent publications, selected as examples of studies that investigated multivariate predictors of long-term outcome following FEP. Results: We illustrate how illness trajectories following FEP could be described based on multimodal sociodemographic, clinical, psychological, and neurobiological information. A clinical modeling simulation shows thatrisk trajectories for achieving long-term favorable or unfavorable outcomes can differ significantly depending on baseline characteristics in combination with MRI and functional measurements within 6 months of disease onset. Conclusions: Multimodal trajectory modeling may be useful to describe longitudinal outcomes following FEP. Richlongitudinal data on predictors and outcomes, and better integration of multimodal (sociodemographic, clinical, psychological, biological) data, are required to operationalize this approach. This technique may improve our understanding of course of illness and help to provide a more personalized approach to the assessment and treatment of people presenting with FEP.

Peripheral blood gene expression analysis implicates B lymphocyte proliferation and ribosomal S26 transcripts in cognitive dysfunction in people with remitted Major Depression

Residual impairments in cognitive domains including attention, memory, executive function, and social perception occur in about 40% of patients with Major Depressive Disorder (MDD) following remission from acute episodes (Conradi et al., 2011), and are associated with reductions in pre-morbid functioning and higher unemployment rates (Baune et al., 2010; Evans et al., 2014). Cognitive impairment in MDD may represent a trait marker, trait-byillness interaction marker, or endophenotype of MDD that is independent of the acute depressive state and particularly active in a subgroup of MDD patients (Hasler et al., 2004; Iverson et al., 2011). We explored the potential molecular underpinnings of such a ‘cognitive’ MDD phenotype in a well-matched pilot sample of remitted MDD patients with poorer versus better performance on cognitive testing. Because the phenotype is likely highly complex, polygenic, and heterogeneous, we performed weighted gene coexpression network analysis (WGCNA), a hypothesis-free systems biology approach that identifies ‘modules’ of co-regulated – and therefore functionally related – genes in transcriptomic datasets. After constructing a gene co-expression network from samples of all patients, we determined whether modules were correlated with poorer versus better cognitive performance. We additionally explored whether modules were also correlated with a continuous measure of cognitive performance in both groups. We utilized whole blood transcriptomic data from remitted MDD patients from the Adelaide Cognitive Function and Mood Study (CoFaMS, HREC RAH protocol no. 111230g) matched for race, gender, age, Hamilton Depression Scale score, presence of psychotic features during any depressive episode, educational status, annual household income, current drugand alcohol use, and

Impact of a history of maternal depression and anxiety on asthma control during pregnancy

ABSTRACT Objective: To determine the impact of self-reported maternal depression/anxiety on asthma control during pregnancy. Method: Pregnant women with a doctor diagnosis of asthma (n = 189) were prospectively recruited at their antenatal booking visit, and the presence of maternal depression and anxiety was identified using self-report and routine questionnaire assessments. Data on exacerbations and asthma control were collected during gestation. Asthma control was assessed using the Juniper Asthma Control Questionnaire (ACQ) and women were classified as having recurrent uncontrolled asthma if their ACQ score was >1.5 during two or more consecutive study visits. Exacerbations were defined as events that led to increased treatment requirements, and doctor or hospital visits. Results: There were 85 women with self-reported depression/anxiety and 104 women without self-reported depression/anxiety. The presence of depression/anxiety was associated with an increased likelihood (adjusted hazard ratio (HR) 1.67: 95% confidence interval (CI) 1.03–2.72) and incidence (adjusted incidence rate ratio (IRR) 1.71: 95% CI 1.13–2.58) of uncontrolled asthma during pregnancy, as well as an increased risk of recurrent uncontrolled asthma during 2 or more study visits (adjusted relative risk (RR) 1.98: 95% CI 1.00–3.91). No impact of depression/anxiety was observed with respect to the likelihood (adjusted HR 0.70: 95% CI 0.35–1.41) or incidence of exacerbations during pregnancy (adjusted IRR 0.66: 95% CI 0.35–1.26). Conclusions: This study provides evidence that the presence of maternal depression/anxiety is associated with an increased likelihood and incidence of uncontrolled asthma during pregnancy. Given the high prevalence of co-morbid depression/anxiety among asthmatics, further research investigating such associations is urgently required.

Trajectories of anxiety and health related quality of life during pregnancy

Anxiety and health related Quality of Life (HRQoL) have emerged as important mental health measures in obstetric care. Few studies have systematically examined the longitudinal trajectories of anxiety and HRQoL in pregnancy. Using a linear growth modeling strategy, we analyzed the course of State-Trait Anxiety Inventory (STAI)- and Short Form (36) Health Survey (SF-36) scores between the 12th and the 36th week of gestation, in a sample of 355 women. We additionally analyzed the impact of depressive symptoms and a chronic medical condition (asthma), on STAI and SF-36 trajectory curves. STAI scores remained stable throughout pregnancy. A previous history of anxiety increased the overall STAI scores. Asthma and depressive symptoms scores had no impact on the STAI trajectory. Physical SF-36 scores decreased over the course of pregnancy, whereas mental SF-36 trended towards improvement. Asthma reduced physical SF-36 overall. While high depressive symptoms decreased the overall mental SF-36, they were also significantly associated with mental SF-36 improvements over time. Anxiety symptoms are stable during pregnancy and are not modulated by depressive symptoms or asthma. Physical HRQoL declines in pregnancy. In contrast, mental HRQoL appears to improve, particularly in women with high initial levels of depressive symptoms.

Targeted proteomic analysis of cognitive dysfunction in remitted major depressive disorder: Opportunities of multi-omics approaches towards predictive, preventive, and personalized psychiatry

In order to accelerate the understanding of pathophysiological mechanisms and clinical biomarker discovery and in psychiatry, approaches that integrate multiple -omics platforms are needed. We introduce a workflow that investigates a narrowly defined psychiatric phenotype, makes use of the potent and cost-effective discovery technology of gene expression microarrays, applies Weighted Gene Co-Expression Network Analysis (WGCNA) to better capture complex and polygenic traits, and finally explores gene expression findings on the proteomic level using targeted mass-spectrometry (MS) technologies. To illustrate the effectiveness of the workflow, we present a proteomic analysis of peripheral blood plasma from patients remitted major depressive disorder (MDD) who experience ongoing cognitive deficits. We show that co-expression patterns previous detected on the transcript level could be replicated for plasma proteins, as could the module eigengene correlation with cognitive performance. Further, we demonstrate that functional analysis of multi-omics data has the potential to point to cellular mechanisms and candidate biomarkers for cognitive dysfunction in MDD, implicating cell cycle regulation by cyclin D3 (CCND3), regulation of protein processing in the endoplasmatic reticulum by Thioredoxin domain-containing protein 5 (TXND5), and modulation of inflammatory cytokines by Tripartite Motif Containing 26 (TRI26). SIGNIFICANCE This paper discusses how data from multiple -omics platforms can be integrated to accelerate biomarker discovery in psychiatry. Using the phenotype of cognitive impairment in remitted major depressive disorder (MDD) as an example, we show that the application of a systems biology approach - weighted gene co-expression network analysis (WGCNA) - in the discovery phase, and targeted proteomic follow-up of results, provides a structured avenue towards uncovering novel candidate markers and pathways for personalized clinical psychiatry.

A gene co-expression module implicating the mitochondrial electron transport chain is associated with long-term response to lithium treatment in bipolar affective disorder

Lithium is the first-line treatment for bipolar affective disorder (BPAD) but two-thirds of patients respond only partially or not at all. The reasons for this high variability in lithium response are not well understood. Transcriptome-wide profiling, which tests the interface between genes and the environment, represents a viable means of exploring the molecular mechanisms underlying lithium response variability. Thus, in the present study we performed co-expression network analyses of whole-blood-derived RNA-seq data from n = 50 lithium-treated BPAD patients. Lithium response was assessed using the well-validated ALDA scale, which we used to define both a continuous and a dichotomous measure. We identified a nominally significant correlation between a co-expression module comprising 46 genes and lithium response represented as a continuous (i.e., scale ranging 0–10) phenotype (cor = −0.299, p = 0.035). Forty-three of these 46 genes had reduced mRNA expression levels in better lithium responders relative to poorer responders, and the central regulators of this module were all mitochondrially-encoded (MT-ND1, MT-ATP6, MT-CYB). Accordingly, enrichment analyses indicated that genes involved in mitochondrial functioning were heavily over-represented in this module, specifically highlighting the electron transport chain (ETC) and oxidative phosphorylation (OXPHOS) as affected processes. Disrupted ETC and OXPHOS activity have previously been implicated in the pathophysiology of BPAD. Our data adds to previous evidence suggesting that a normalisation of these processes could be central to lithium’s mode of action, and could underlie a favourable therapeutic response.

Co-expression network analysis of peripheral blood transcriptome identifies dysregulated protein processing in endoplasmic reticulum and immune response in recurrent MDD in older adults

The molecular factors involved in the pathophysiology of major depressive disorder (MDD) remain poorly understood. One approach to examine the molecular basis of MDD is co-expression network analysis, which facilitates the examination of complex interactions between expression levels of individual genes and how they influence biological pathways affected in MDD. Here, we applied an unsupervised gene-network based approach to a prospective experimental design using microarray genome-wide gene expression from the peripheral whole blood of older adults. We utilised the Sydney Memory and Ageing Study (sMAS, N = 521) and the Older Australian Twins Study (OATS, N = 186) as discovery and replication cohorts, respectively. We constructed networks using Weighted Gene Co-expression Network Analysis (WGCNA), and correlated identified modules with four subtypes of depression: single episode, current, recurrent, and lifetime MDD. Four modules of highly co-expressed genes were associated with recurrent MDD (N = 27) in our discovery cohort (FDR<0.2), with no significant findings for a single episode, current or lifetime MDD. Functional characterisation of these modules revealed a complex interplay between dysregulated protein processing in the endoplasmic reticulum (ER), and innate and adaptive immune response signalling, with possible involvement of pathogen-related pathways. We were underpowered to replicate findings at the network level in an independent cohort (OATS), however; we found a significant overlap for 9 individual genes with similar co-expression and dysregulation patterns associated with recurrent MDD in both cohorts. Overall, our findings support other reports on dysregulated immune response and protein processing in the ER in MDD and provide novel insights into the pathophysiology of depression.

Using a Correlational Systems Biology Approach in Gene Expression and Proteomic Discovery Platforms, to Characterize Cognitive Dysfunction in People with Remitted Major Depression

Abstract Cognitive impairments are observed in a substantial proportion of patients suffering from Major Depressive Disorder (MDD), significantly impacting on patients’ long term psychosocial functioning and quality of life. In order to characterize a potential ‘cognitive’ phenotype in MDD, we utilized whole-blood transcriptomic data from remitted MDD patients for weighted gene co-expression network analysis (WGCNA), and identified 16 transcriptomic modules. One module was significantly correlated with poor versus better cognitive performance, containing ribosomal genes and modulators of B cell biology. On the plasma protein level, SWATH-MS detected 43% of module gene products. The detected proteins were also significantly co-expressed as a module and correlated with the phenotype of interest. The experimental workflow may represent an effective approach to blood biomarker discovery for psychiatric phenotypes, and findings may inform research into novel pharmacological strategies in MDD treatment.