Novel open reading frames in human accelerated regions and transposable elements reveal new leads to understand schizophrenia and bipolar disorder.

Schizophrenia (SCZ) and bipolar disorder are debilitating neuropsychiatric disorders arising from a combination of environmental and genetic factors. Novel open reading frames (nORFs) are genomic loci that give rise to previously uncharacterized transcripts and protein products. In our previous work, we have shown that nORFs can be biologically regulated and that they may play a role in cancer and rare diseases. More importantly, we have shown that nORFs may emerge in accelerated regions of the genome giving rise to species-specific functions. We hypothesize that nORFs represent a potentially important group of biological factors that may contribute to SCZ and bipolar disorder pathophysiology. Human accelerated regions (HARs) are genomic features showing human-lineage-specific rapid evolution that may be involved in biological regulation and have additionally been found to associate with SCZ genes. Transposable elements (TEs) are another set of genomic features that have been shown to regulate gene expression. As with HARs, their relevance to SCZ has also been suggested. Here, nORFs are investigated in the context of HARs and TEs. This work shows that nORFs whose expression is disrupted in SCZ and bipolar disorder are in close proximity to HARs and TEs and that some of them are significantly associated with SCZ and bipolar disorder genomic hotspots. We also show that nORF encoded proteins can form structures and potentially constitute novel drug targets.

Spatial and temporal diversity of glycome expression in mammalian brain.

Mammalian brain glycome remains a relatively poorly understood area compared to other large-scale "omics" studies, such as genomics and transcriptomics due to the inherent complexity and heterogeneity of glycan structure and properties. Here, we first performed spatial and temporal analysis of glycome expression patterns in the mammalian brain using a cutting-edge experimental tool based on liquid chromatography-mass spectrometry, with the ultimate aim to yield valuable implications on molecular events regarding brain functions and development. We observed an apparent diversity in the glycome expression patterns, which is spatially well-preserved among nine different brain regions in mouse. Next, we explored whether the glycome expression pattern changes temporally during postnatal brain development by examining the prefrontal cortex (PFC) at different time point across six postnatal stages in mouse. We found that glycan expression profiles were dynamically regulated during postnatal developments. A similar result was obtained in PFC samples from humans ranging in age from 39 d to 49 y. Novel glycans unique to the brain were also identified. Interestingly, changes primarily attributed to sialylated and fucosylated glycans were extensively observed during PFC development. Finally, based on the vast heterogeneity of glycans, we constructed a core glyco-synthesis map to delineate the glycosylation pathway responsible for the glycan diversity during the PFC development. Our findings reveal high levels of diversity in a glycosylation program underlying brain region specificity and age dependency, and may lead to new studies exploring the role of glycans in spatiotemporally diverse brain functions.

Peripheral lymphocyte signaling pathway deficiencies predict treatment response in first-onset drug-naïve schizophrenia.

Despite being a major cause of disability worldwide, the pathophysiology of schizophrenia and molecular basis of treatment response heterogeneity continue to be unresolved. Recent evidence suggests that multiple aspects of pathophysiology, including genetic risk factors, converge on key cell signaling pathways and that exploration of peripheral blood cells might represent a practical window into cell signaling alterations in the disease state. We employed multiplexed phospho-specific flow cytometry to examine cell signaling epitope expression in peripheral blood mononuclear cell (PBMC) subtypes in drug-naïve schizophrenia patients (n = 49) relative to controls (n = 61) and relate these changes to serum immune response proteins, schizophrenia polygenic risk scores and clinical effects of treatment, including drug response and side effects, over the longitudinal course of antipsychotic treatment. This revealed both previously characterized (Akt1) and novel cell signaling epitopes (IRF-7 (pS477/pS479), CrkL (pY207), Stat3 (pS727), Stat3 (pY705) and Stat5 (pY694)) across PBMC subtypes which were associated with schizophrenia at disease onset, and correlated with type I interferon-related serum molecules CD40 and CXCL11. Alterations in Akt1 and IRF-7 (pS477/pS479) were additionally associated with polygenic risk of schizophrenia. Finally, changes in Akt1, IRF-7 (pS477/pS479) and Stat3 (pS727) predicted development of metabolic and cardiovascular side effects following antipsychotic treatment, while IRF-7 (pS477/pS479) and Stat3 (pS727) predicted early improvements in general psychopathology scores measured using the Brief Psychiatric Rating Scale (BPRS). These findings suggest that peripheral blood cells can provide an accessible surrogate model for intracellular signaling alterations in schizophrenia and have the potential to stratify subgroups of patients with different clinical outcomes or a greater risk of developing metabolic and cardiovascular side effects following antipsychotic therapy.

Exploring cellular markers of metabolic syndrome in peripheral blood mononuclear cells across the neuropsychiatric spectrum.

Recent evidence suggests that comorbidities between neuropsychiatric conditions and metabolic syndrome may precede and even exacerbate long-term side-effects of psychiatric medication, such as a higher risk of type 2 diabetes and cardiovascular disease, which result in increased mortality. In the present study we compare the expression of key metabolic proteins, including the insulin receptor (CD220), glucose transporter 1 (GLUT1) and fatty acid translocase (CD36), on peripheral blood mononuclear cell subtypes from patients across the neuropsychiatric spectrum, including schizophrenia, bipolar disorder, major depression and autism spectrum conditions (n = 25/condition), relative to typical controls (n = 100). This revealed alterations in the expression of these proteins that were specific to schizophrenia. Further characterization of metabolic alterations in an extended cohort of first-onset antipsychotic drug-naïve schizophrenia patients (n = 58) and controls (n = 63) revealed that the relationship between insulin receptor expression in monocytes and physiological insulin sensitivity was disrupted in schizophrenia and that altered expression of the insulin receptor was associated with whole genome polygenic risk scores for schizophrenia. Finally, longitudinal follow-up of the schizophrenia patients over the course of antipsychotic drug treatment revealed that peripheral metabolic markers predicted changes in psychopathology and the principal side effect of weight gain at clinically relevant time points. These findings suggest that peripheral blood cells can provide an accessible surrogate model for metabolic alterations in schizophrenia and have the potential to stratify subgroups of patients with different clinical outcomes or a greater risk of developing metabolic complications following antipsychotic therapy.

Exploring the neuropsychiatric spectrum using high-content functional analysis of single-cell signaling networks.

Neuropsychiatric disorders overlap in symptoms and share genetic risk factors, challenging their current classification into distinct diagnostic categories. Novel cross-disorder approaches are needed to improve our understanding of the heterogeneous nature of neuropsychiatric diseases and overcome existing bottlenecks in their diagnosis and treatment. Here we employ high-content multi-parameter phospho-specific flow cytometry, fluorescent cell barcoding and automated sample preparation to characterize ex vivo signaling network responses (n = 1764) measured at the single-cell level in B and T lymphocytes across patients diagnosed with four major neuropsychiatric disorders: autism spectrum condition (ASC), bipolar disorder (BD), major depressive disorder (MDD), and schizophrenia (SCZ; n = 25 each), alongside matched healthy controls (n = 100). We identified 25 nodes (individual cell subtype-epitope-ligand combinations) significantly altered relative to the control group, with variable overlap between different neuropsychiatric diseases and heterogeneously expressed at the level of each individual patient. Reconstruction of the diagnostic categories from the altered nodes revealed an overlapping neuropsychiatric spectrum extending from MDD on one end, through BD and SCZ, to ASC on the other end. Network analysis showed that although the pathway structure of the epitopes was broadly preserved across the clinical groups, there were multiple discrete alterations in network connectivity, such as disconnections within the antigen/integrin receptor pathway and increased negative regulation within the Akt1 pathway in CD4+ T cells from ASC and SCZ patients, in addition to increased correlation of Stat1 (pY701) and Stat5 (pY694) responses in B cells from BD and MDD patients. Our results support the "dimensional" approach to neuropsychiatric disease classification and suggest potential novel drug targets along the neuropsychiatric spectrum.

Building the Digital Mental Health Ecosystem: Opportunities and Challenges for Mobile Health Innovators.

Digital mental health technologies such as mobile health (mHealth) tools can offer innovative ways to help develop and facilitate mental health care provision, with the COVID-19 pandemic acting as a pivot point for digital health implementation. This viewpoint offers an overview of the opportunities and challenges mHealth innovators must navigate to create an integrated digital ecosystem for mental health care moving forward. Opportunities exist for innovators to develop tools that can collect a vast range of active and passive patient and transdiagnostic symptom data. Moving away from a symptom-count approach to a transdiagnostic view of psychopathology has the potential to facilitate early and accurate diagnosis, and can further enable personalized treatment strategies. However, the uptake of these technologies critically depends on the perceived relevance and engagement of end users. To this end, behavior theories and codesigning approaches offer opportunities to identify behavioral drivers and address barriers to uptake, while ensuring that products meet users' needs and preferences. The agenda for innovators should also include building strong evidence-based cases for digital mental health, moving away from a one-size-fits-all well-being approach to embrace the development of comprehensive digital diagnostics and validated digital tools. In particular, innovators have the opportunity to make their clinical evaluations more insightful by assessing effectiveness and feasibility in the intended context of use. Finally, innovators should adhere to standardized evaluation frameworks introduced by regulators and health care providers, as this can facilitate transparency and guide health care professionals toward clinically safe and effective technologies. By laying these foundations, digital services can become integrated into clinical practice, thus facilitating deeper technology-enabled changes.

Proof-of-Concept Support for the Development and Implementation of a Digital Assessment for Perinatal Mental Health: Mixed Methods Study.

BACKGROUND: Perinatal mental health symptoms commonly remain underdiagnosed and undertreated in maternity care settings in the United Kingdom, with outbreaks of disease, like the COVID-19 pandemic, further disrupting access to adequate mental health support. Digital technologies may offer an innovative way to support the mental health needs of women and their families throughout the perinatal period, as well as assist midwives in the recognition of perinatal mental health concerns. However, little is known about the acceptability and perceived benefits and barriers to using such technologies. OBJECTIVE: The aim of this study was to conduct a mixed methods evaluation of the current state of perinatal mental health care provision in the United Kingdom, as well as users' (women and partners) and midwives' interest in using a digital mental health assessment throughout the perinatal period. METHODS: Women, partners, and midwives were recruited to participate in the study, which entailed completing an online survey. Quantitative data were explored using descriptive statistics. Open-ended response data were first investigated using thematic analysis. Resultant themes were then mapped onto the components of the Capability, Opportunity, and Motivation Behavior model and summarized using descriptive statistics. RESULTS: A total of 829 women, 103 partners, and 90 midwives participated in the study. The provision of adequate perinatal mental health care support was limited, with experiences varying significantly across respondents. There was a strong interest in using a digital mental health assessment to screen, diagnose, and triage perinatal mental health concerns, particularly among women and midwives. The majority of respondents (n=781, 76.42%) expressed that they would feel comfortable or very comfortable using or recommending a digital mental health assessment. The majority of women and partners showed a preference for in-person consultations (n=417, 44.74%), followed by a blended care approach (ie, both in-person and online consultations) (n=362, 38.84%), with fewer participants preferring online-only consultations (n=120, 12.88%). Identified benefits and barriers mainly related to physical opportunity (eg, accessibility), psychological capability (eg, cognitive skills), and automatic motivation (eg, emotions). CONCLUSIONS: This study provides proof-of-concept support for the development and implementation of a digital mental health assessment to inform clinical decision making in the assessment of perinatal mental health concerns in the United Kingdom.

Diagnostic prediction model development using data from dried blood spot proteomics and a digital mental health assessment to identify major depressive disorder among individuals presenting with low mood.

With less than half of patients with major depressive disorder (MDD) correctly diagnosed within the primary care setting, there is a clinical need to develop an objective and readily accessible test to enable earlier and more accurate diagnosis. The aim of this study was to develop diagnostic prediction models to identify MDD patients among individuals presenting with subclinical low mood, based on data from dried blood spot (DBS) proteomics (194 peptides representing 115 proteins) and a novel digital mental health assessment (102 sociodemographic, clinical and personality characteristics). To this end, we investigated 130 low mood controls, 53 currently depressed individuals with an existing MDD diagnosis (established current MDD), 40 currently depressed individuals with a new MDD diagnosis (new current MDD), and 72 currently not depressed individuals with an existing MDD diagnosis (established non-current MDD). A repeated nested cross-validation approach was used to evaluate variation in model selection and ensure model reproducibility. Prediction models that were trained to differentiate between established current MDD patients and low mood controls (AUC = 0.94 ± 0.01) demonstrated a good predictive performance when extrapolated to differentiate between new current MDD patients and low mood controls (AUC = 0.80 ± 0.01), as well as between established non-current MDD patients and low mood controls (AUC = 0.79 ± 0.01). Importantly, we identified DBS proteins A1AG1, A2GL, AL1A1, APOE and CFAH as important predictors of MDD, indicative of immune system dysregulation; as well as poor self-rated mental health, BMI, reduced daily experiences of positive emotions, and tender-mindedness. Despite the need for further validation, our preliminary findings demonstrate the potential of such prediction models to be used as a diagnostic aid for detecting MDD in clinical practice.

Alteration of Neuronal Excitability and Short-Term Synaptic Plasticity in the Prefrontal Cortex of a Mouse Model of Mental Illness.

Using a genetic mouse model that faithfully recapitulates a DISC1 genetic alteration strongly associated with schizophrenia and other psychiatric disorders, we examined the impact of this mutation within the prefrontal cortex. Although cortical layering, cytoarchitecture, and proteome were found to be largely unaffected, electrophysiological examination of the mPFC revealed both neuronal hyperexcitability and alterations in short-term synaptic plasticity consistent with enhanced neurotransmitter release. Increased excitability of layer II/III pyramidal neurons was accompanied by consistent reductions in voltage-activated potassium currents near the action potential threshold as well as by enhanced recruitment of inputs arising from superficial layers to layer V. We further observed reductions in both the paired-pulse ratios and the enhanced short-term depression of layer V synapses arising from superficial layers consistent with enhanced neurotransmitter release at these synapses. Recordings from layer II/III pyramidal neurons revealed action potential widening that could account for enhanced neurotransmitter release. Significantly, we found that reduced functional expression of the voltage-dependent potassium channel subunit Kv1.1 substantially contributes to both the excitability and short-term plasticity alterations that we observed. The underlying dysregulation of Kv1.1 expression was attributable to cAMP elevations in the PFC secondary to reduced phosphodiesterase 4 activity present in Disc1 deficiency and was rescued by pharmacological blockade of adenylate cyclase. Our results demonstrate a potentially devastating impact of Disc1 deficiency on neural circuit function, partly due to Kv1.1 dysregulation that leads to a dual dysfunction consisting of enhanced neuronal excitability and altered short-term synaptic plasticity.SIGNIFICANCE STATEMENT Schizophrenia is a profoundly disabling psychiatric illness with a devastating impact not only upon the afflicted but also upon their families and the broader society. Although the underlying causes of schizophrenia remain poorly understood, a growing body of studies has identified and strongly implicated various specific risk genes in schizophrenia pathogenesis. Here, using a genetic mouse model, we explored the impact of one of the most highly penetrant schizophrenia risk genes, DISC1, upon the medial prefrontal cortex, the region believed to be most prominently dysfunctional in schizophrenia. We found substantial derangements in both neuronal excitability and short-term synaptic plasticity-parameters that critically govern neural circuit information processing-suggesting that similar changes may critically, and more broadly, underlie the neural computational dysfunction prototypical of schizophrenia.

Proteomic approaches to identify blood-based biomarkers for depression and bipolar disorders.

INTRODUCTION: Major depressive disorder (MDD) and bipolar disorder (BD) are leading causes of disability worldwide, yet many people remain undiagnosed, are misdiagnosed, and/or ineffectively treated. Diagnosis relies on the clinical assessment of symptoms, and there is currently no molecular or brain-imaging diagnostic test available. Identifying and validating protein biomarkers could provide a more accurate and objective means of diagnosis. Areas covered: Proteomics is a powerful tool that enables the identification and quantification of novel candidate biomarkers of disease. In this review, we discuss the role of proteomic technologies in biomarker discovery and validation, peripheral blood as a source of protein biomarkers, statistical methods for analyzing proteomic data, and some existing challenges in the field. We also review a selection of previously published studies focused on identifying blood-based diagnostic protein biomarkers of MDD and BD within a ten-year period. Expert commentary: Proteomic studies have led to the identification of numerous potential biomarkers of MDD and BD. However, clinical validation and translation into clinical practice have not yet been achieved. Conducting large-scale validation studies and addressing various factors that limit the reproducibility of the proteomic findings are key to ensure that robust and reliable biomarker tests are developed and clinically validated.

Evidence of microglial activation following exposure to serum from first-onset drug-naïve schizophrenia patients.

Abnormal activation of brain microglial cells is widely implicated in the pathogenesis of schizophrenia. Previously the pathophysiology of microglial activation was considered to be intrinsic to the central nervous system. We hypothesised that due to their perivascular localization, microglia can also be activated by factors present in circulating blood. Through application of high-content functional screening, we show that peripheral blood serum from first-onset drug-naïve schizophrenia patients is sufficient to provoke microglial cell signalling network responses in vitro which are indicative of proinflammatory activation. We further explore the composition of the serum for the presence of analytes, with the potential to activate microglia, and the utility of the resultant microglial cellular phenotype for novel drug discovery.

Oxidative stress in drug-naïve first episode patients with schizophrenia and major depression: effects of disease acuity and potential confounders.

Oxidative stress and immune dysregulation have been linked to schizophrenia and depression. However, it is unknown whether these factors are related to the pathophysiology or whether they are an epiphenomenon. Inconsistent oxidative stress-related findings in previous studies may have resulted from the use of different biomarkers which show disparate aspects of oxidative stress. Additionally, disease severity, medication, smoking, endocrine stress axis activation and obesity are potential confounders. In order to address some of these shortcomings, we have analyzed a broader set of oxidative stress biomarkers in our exploratory study, including urinary 8-iso-prostaglandin F2α (8-iso-PGF2α), 8-OH-2-deoyxguanosine (8-OH-2-dG), and blood levels of malondialdehyde (MDA), superoxide dismutase (SOD) and glutathione S-transferase (GST) in acutely ill drug-naïve first episode patients with schizophrenia (n = 22), major depression (n = 18), and controls (n = 43). Possible confounding factors were considered, and patients were followed-up after 6 weeks of treatment. No differences were observed regarding 8-OH-2-dG, MDA and GST. At baseline, 8-iso-PGF2α levels were higher in patients with schizophrenia (p = 0.004) and major depression (p = 0.037), with a trend toward higher SOD concentrations in schizophrenia (p = 0.053). After treatment, schizophrenia patients showed a further increase in 8-iso-PGF2α (p = 0.016). These results were not related to age, sex, disease severity, medication or adipose tissue mass. However, 8-iso-PGF2α was associated with smoking, endocrine stress axis activation, C-reactive protein levels and low plasma concentrations of brain-derived neurotrophic factor. This study suggests a role of lipid peroxidation particularly in drug-naïve acutely ill schizophrenia patients and highlights the importance of taking into account other confounding factors in biomarker studies.

Altered apolipoprotein C expression in association with cognition impairments and hippocampus volume in schizophrenia and bipolar disorder.

Proteomic analyses facilitate the interpretation of molecular biomarker probes which are very helpful in diagnosing schizophrenia (SZ). In the current study, we attempt to test whether potential differences in plasma protein expressions in SZ and bipolar disorder (BD) are associated with cognitive deficits and their underlying brain structures. Forty-two plasma proteins of 29 SZ patients, 25 BD patients and 93 non-clinical controls were quantified and analysed using multiple reaction monitoring-based triple quadrupole mass spectrometry approach. We also computed group comparisons of protein expressions between patients and controls, and between SZ and BD patients, as well. Potential associations of protein levels with cognitive functioning (psychomotor speed, executive functioning, crystallised intelligence) as well as underlying brain volume in the hippocampus were explored, using bivariate correlation analyses. The main finding of this study was that apolipoprotein expression differed between patients and controls and that these alterations in both disease groups were putatively related to cognitive impairments as well as to hippocampus volumes. However, none of the protein level differences were related to clinical symptom severity. In summary, altered apolipoprotein expression in BD and SZ was linked to cognitive decline and underlying morphological changes in both disorders. Our results suggest that the detection of molecular patterns in association with cognitive performance and its underlying brain morphology is of great importance for understanding of the pathological mechanisms of SZ and BD, as well as for supporting the diagnosis and treatment of both disorders.

Individual differences in the peripheral immune system promote resilience versus susceptibility to social stress.

Depression and anxiety disorders are associated with increased release of peripheral cytokines; however, their functional relevance remains unknown. Using a social stress model in mice, we find preexisting individual differences in the sensitivity of the peripheral immune system that predict and promote vulnerability to social stress. Cytokine profiles were obtained 20 min after the first social stress exposure. Of the cytokines regulated by stress, IL-6 was most highly up-regulated only in mice that ultimately developed a susceptible behavioral phenotype following a subsequent chronic stress, and levels remained elevated for at least 1 mo. We confirmed a similar elevation of serum IL-6 in two separate cohorts of patients with treatment-resistant major depressive disorder. Before any physical contact in mice, we observed individual differences in IL-6 levels from ex vivo stimulated leukocytes that predict susceptibility versus resilience to a subsequent stressor. To shift the sensitivity of the peripheral immune system to a pro- or antidepressant state, bone marrow (BM) chimeras were generated by transplanting hematopoietic progenitor cells from stress-susceptible mice releasing high IL-6 or from IL-6 knockout (IL-6(-/-)) mice. Stress-susceptible BM chimeras exhibited increased social avoidance behavior after exposure to either subthreshold repeated social defeat stress (RSDS) or a purely emotional stressor termed witness defeat. IL-6(-/-) BM chimeric and IL-6(-/-) mice, as well as those treated with a systemic IL-6 monoclonal antibody, were resilient to social stress. These data establish that preexisting differences in stress-responsive IL-6 release from BM-derived leukocytes functionally contribute to social stress-induced behavioral abnormalities.

Current concepts in chronic inflammatory diseases: Interactions between microbes, cellular metabolism, and inflammation.

Recent research indicates that chronic inflammatory diseases, including allergies and autoimmune and neuropsychiatric diseases, share common pathways of cellular and molecular dysregulation. It was the aim of the International von-Behring-Röntgen Symposium (October 16-18, 2014, in Marburg, Germany) to discuss recent developments in this field. These include a concept of biodiversity; the contribution of urbanization, lifestyle factors, and nutrition (eg, vitamin D); and new mechanisms of metabolic and immune dysregulation, such as extracellular and intracellular RNAs and cellular and mitochondrial stress. Epigenetic mechanisms contribute further to altered gene expression and therefore to the development of chronic inflammation. These novel findings provide the foundation for further development of preventive and therapeutic strategies.

Pretreatment levels of the fatty acid handling proteins H-FABP and CD36 predict response to olanzapine in recent-onset schizophrenia patients.

Traditional schizophrenia pharmacotherapy remains a subjective trial and error process involving administration, titration and switching of drugs multiple times until an adequate response is achieved. Despite this time-consuming and costly process, not all patients show an adequate response to treatment. As a consequence, relapse is a common occurrence and early intervention is hampered. Here, we have attempted to identify candidate blood biomarkers associated with drug response in 121 initially antipsychotic-free recent-onset schizophrenia patients treated with widely-used antipsychotics, namely olanzapine (n=40), quetiapine (n=23), risperidone (n=30) and a mixture of these drugs (n=28). Patients were recruited and investigated as two separate cohorts to allow biomarker validation. Data analysis showed the most significant relationship between pre-treatment levels of heart-type fatty acid binding protein (H-FABP) and response to olanzapine (p=0.008, F=8.6, ß=70.4 in the discovery cohort and p=0.003, F=15.2, ß=24.4 in the validation cohort, adjusted for relevant confounding variables). In a functional follow-up analysis of this finding, we tested an independent cohort of 10 patients treated with olanzapine and found that baseline levels of plasma H-FABP and expression of the binding partner for H-FABP, fatty acid translocase (CD36), on monocytes predicted the reduction of psychotic symptoms (p=0.040, F=6.0, ß=116.3 and p=0.012, F=11.9, ß=-0.0054, respectively). We also identified a set of serum molecules changed after treatment with antipsychotic medication, in particular olanzapine. These molecules are predominantly involved in cellular development and metabolism. Taken together, our findings suggest an association between biomarkers involved in fatty acid metabolism and response to olanzapine, while other proteins may serve as surrogate markers associated with drug efficacy and side effects.

Towards a blood-based diagnostic panel for bipolar disorder.

BACKGROUND: Bipolar disorder (BD) is a costly, devastating and life shortening mental disorder that is often misdiagnosed, especially on initial presentation. Misdiagnosis frequently results in ineffective treatment. We investigated the utility of a biomarker panel as a diagnostic test for BD. METHODS AND FINDINGS: We performed a meta-analysis of eight case-control studies to define a diagnostic biomarker panel for BD. After validating the panel on established BD patients, we applied it to undiagnosed BD patients. We analysed 249 BD, 122 pre-diagnostic BD, 75 pre-diagnostic schizophrenia and 90 first onset major depression disorder (MDD) patients and 371 controls. The biomarker panel was identified using ten-fold cross-validation with lasso regression applied to the 87 analytes available across the meta-analysis studies. We identified 20 protein analytes with excellent predictive performance [area under the curve (AUC)⩾0.90]. Importantly, the panel had a good predictive performance (AUC 0.84) to differentiate 12 misdiagnosed BD patients from 90 first onset MDD patients, and a fair to good predictive performance (AUC 0.79) to differentiate between 110 pre-diagnostic BD patients and 184 controls. We also demonstrated the disease specificity of the panel. CONCLUSIONS: An early and accurate diagnosis has the potential to delay or even prevent the onset of BD. This study demonstrates the potential utility of a biomarker panel as a diagnostic test for BD.

Identification of an Immune-Neuroendocrine Biomarker Panel for Detection of Depression: A Joint Effects Statistical Approach.

BACKGROUND/AIMS: Less than half of depression patients are correctly diagnosed within the primary care setting. Previous proteomic studies have identified numerous immune and neuroendocrine changes in patients. However, few studies have considered the joint effects of biological molecules and their diagnostic potential. Our aim was to develop and validate a diagnostic serum biomarker panel identified through joint effects analysis of multiplex immunoassay profiling data from 1,007 clinical samples. METHODS: In stage 1, we conducted a meta-analysis of two independent cohorts of 78 first-/recent-onset drug-naive/drug-free depression patients and 156 controls and applied the 10-fold cross-validation with least absolute shrinkage and selection operator regression to identify an optimal diagnostic prediction model (biomarker panel). In stage 2, we tested the discriminatory performance of this biomarker panel using the naturalistic Netherlands Study of Depression and Anxiety (NESDA) cohort of 468 depression patients and 305 controls. RESULTS: An optimal panel of 33 immune-neuroendocrine biomarkers and gender was selected in the meta-analysis. Testing this biomarker-gender panel using the NESDA cohort resulted in a moderate to good performance to differentiate patients from controls (0.69 < AUC < 0.86), particularly the first-episode patients free of chronic non-psychiatric diseases or medications and following incorporation of sociodemographic covariates (0.76 < AUC < 0.92). CONCLUSION: Despite the need for additional validation studies, we demonstrated that a blood-based biomarker-sociodemographic panel can detect depression in naturalistic healthcare settings with good discriminatory power. Further refinements of blood biomarker panels aiding in the diagnosis of depression may provide a cost-effective means to increase accuracy of clinical diagnosis within the primary care setting.

A targeted multiplexed proteomic investigation identifies ketamine-induced changes in immune markers in rat serum and expression changes in protein kinases/phosphatases in rat brain.

There is substantial interest in the N-methyl-d-aspartate (NMDA) receptor antagonist ketamine in psychiatric research because it exerts acute psychotomimetic and rapid antidepressant effects in rodents and humans. Here, we investigated proteomic changes in brain and serum after acute treatment of rats with ketamine using two targeted proteomic profiling methods. Multiplex immunoassay profiling of serum identified altered levels of interleukin 4, tumor necrosis factor alpha, and fibroblast growth factor 9, suggesting a link between ketamine exposure and peripheral inflammation and growth factor dysregulation. Selected reaction monitoring mass spectrometry profiling of rat brain tissue found that proteomic changes occurred in the frontal cortex and to a greater extent in the hippocampus. This involved changes in signaling kinases and proteases such as protein kinase C beta, neurochondrin (NCDN), calcineurin, extracellular signal-regulated kinsase 1 (ERK1), and mammalian target of rapamycin (MTOR). Furthermore, altered levels were found for proteins associated with neurotransmitter metabolism (mitochondrial aspartate aminotransferase, catechol O-methyl transferase, synaptic vesicle endo-/exocytosis (vesicle fusing ATPase (NSF), synapsin 1 (SYN1), syndapin-1 (PACN1)). Consistent with previous global proteomic studies, we confirmed known changes in mitochondrial complex I, prohibitin (PHB) and neurofilament proteins (neurofilament light chain and α-internexin (AINX)). Taken together, the proteomic changes parallel those described in human psychiatric pathology. The results will help to elucidate ketamine's mechanism of action, which will facilitate development of novel drugs for the treatment of schizophrenia and major depressive disorder.

Hippocampal Proteomic and Metabonomic Abnormalities in Neurotransmission, Oxidative Stress, and Apoptotic Pathways in a Chronic Phencyclidine Rat Model.

Schizophrenia is a neuropsychiatric disorder affecting 1% of the world's population. Due to both a broad range of symptoms and disease heterogeneity, current therapeutic approaches to treat schizophrenia fail to address all symptomatic manifestations of the disease. Therefore, disease models that reproduce core pathological features of schizophrenia are needed for the elucidation of pathological disease mechanisms. Here, we employ a comprehensive global label-free liquid chromatography-mass spectrometry proteomic (LC-MS(E)) and metabonomic (LC-MS) profiling analysis combined with the targeted proteomics (selected reaction monitoring and multiplex immunoassay) of serum and brain tissues to investigate a chronic phencyclidine (PCP) rat model in which glutamatergic hypofunction is induced through noncompetitive NMDAR-receptor antagonism. Using a multiplex immunoassay, we identified alterations in the levels of several cytokines (IL-5, IL-2, and IL-1ß) and fibroblast growth factor-2. Extensive proteomic and metabonomic brain tissue profiling revealed a more prominent effect of chronic PCP treatment on both the hippocampal proteome and metabonome compared to the effect on the frontal cortex. Bioinformatic pathway analysis confirmed prominent abnormalities in NMDA-receptor-associated pathways in both brain regions, as well as alterations in other neurotransmitter systems such as kainate, AMPA, and GABAergic signaling in the hippocampus and in proteins associated with neurodegeneration. We further identified abundance changes in the level of the superoxide dismutase enzyme (SODC) in both the frontal cortex and hippocampus, which indicates alterations in oxidative stress and substantiates the apoptotic pathway alterations. The present study could lead to an increased understanding of how perturbed glutamate receptor signaling affects other relevant biological pathways in schizophrenia and, therefore, support drug discovery efforts for the improved treatment of patients suffering from this debilitating psychiatric disorder.