Michael G. Gottschalk

Applications of blood-based protein biomarker strategies in the study of psychiatric disorders

Major psychiatric disorders such as schizophrenia, major depressive and bipolar disorders are severe, chronic and debilitating, and are associated with high disease burden and healthcare costs. Currently, diagnoses of these disorders rely on interview-based assessments of subjective self-reported symptoms. Early diagnosis is difficult, misdiagnosis is a frequent occurrence and there are no objective tests that aid in the prediction of individual responses to treatment. Consequently, validated biomarkers are urgently needed to help address these unmet clinical needs. Historically, psychiatric disorders are viewed as brain disorders and consequently only a few researchers have as yet evaluated systemic changes in psychiatric patients. However, promising research has begun to challenge this concept and there is an increasing awareness that disease-related changes can be traced in the peripheral system which may even be involved in the precipitation of disease onset and course. Converging evidence from molecular profiling analysis of blood serum/plasma have revealed robust molecular changes in psychiatric patients, suggesting that these disorders may be detectable in other systems of the body such as the circulating blood. In this review, we discuss the current clinical needs in psychiatry, highlight the importance of biomarkers in the field, and review a representative selection of biomarker studies to highlight opportunities for the implementation of personalized medicine approaches in the field of psychiatry. It is anticipated that the implementation of validated biomarker tests will not only improve the diagnosis and more effective treatment of psychiatric patients, but also improve prognosis and disease outcome.

Proteomic Enrichment Analysis of Psychotic and Affective Disorders Reveals Common Signatures in Presynaptic Glutamatergic Signaling and Energy Metabolism

Background: Although genetic studies suggest an overlap in risk alleles across the major psychiatric disorders, disease signatures reflecting overlapping symptoms have not been found. Profiling studies have identified candidate protein markers associated with specific disorders of the psychoaffective spectrum, but this has always been done in a selective fashion without accounting for the entire proteome composition of the system under investigation. Methods: Employing an orthogonal system-based proteomic enrichment approach based on label-free liquid chromatography mass spectrometry, we analyzed anterior prefrontal human post-mortem brain tissue of patients affected by schizophrenia (n = 23), bipolar disorder (n = 23), major depressive disorder with (n = 12) and without psychotic features (n = 11), and healthy controls (n = 23). Labeled selected reaction monitoring (SRM) was used to validate these findings on a pathway level. Independent in silico analyses of biological annotations revealed common pathways across the diseases, associated with presynaptic glutamatergic neurotransmission and energy metabolism. We validated the proteomic findings using SRM and confirmed that there were no effects of post-mortem confounders. Results: Schizophrenia and affective psychosis were linked to a hypoglutamatergic state and hypofunction of energy metabolism, while bipolar disorder and major depressive disorder were linked to a hyperglutamatergic state and hyperfunction of energy metabolism. Conclusions: These findings support recent investigations, which have focused on the therapeutic potential of glutamatergic modulation in psychotic and affective disorders. We suggest a disease model in which disturbances of the glutamatergic system and ensuing adaptations of neuronal energy metabolism are linked to distinct psychiatric symptom dimensions, delivering novel evidence for targeted treatment approaches.

Targeted Multiplexed Selected Reaction Monitoring Analysis Evaluates Protein Expression Changes of Molecular Risk Factors for Major Psychiatric Disorders

Background: Extensive research efforts have generated genomic, transcriptomic, proteomic, and functional data hoping to elucidate psychiatric pathophysiology. Selected reaction monitoring, a recently developed targeted proteomic mass spectrometric approach, has made it possible to evaluate previous findings and hypotheses with high sensitivity, reproducibility, and quantitative accuracy. Methods: Here, we have developed a labelled multiplexed selected reaction monitoring assay, comprising 56 proteins previously implicated in the aetiology of major psychiatric disorders, including cell type markers or targets and effectors of known psychopharmacological interventions. We analyzed postmortem anterior prefrontal cortex (Brodmann area 10) tissue of patients diagnosed with schizophrenia (n=22), bipolar disorder (n=23), and major depressive disorder with (n=11) and without (n=11) psychotic features compared with healthy controls (n=22). Results: Results agreed with several previous studies, with the finding of alterations of Wnt-signalling and glutamate receptor abundance predominately in bipolar disorder and abnormalities in energy metabolism across the neuropsychiatric disease spectrum. Calcium signalling was predominantly affected in schizophrenia and affective psychosis. Interestingly, we were able to show a decrease of all 4 tested oligodendrocyte specific proteins (MOG, MBP, MYPR, CNPase) in bipolar disorder and to a lesser extent in schizophrenia and affective psychosis. Finally, we provide new evidence linking ankyrin 3 specifically to affective psychosis and the 22q11.2 deletion syndrome-associated protein septin 5 to schizophrenia. Conclusions: Our study highlights the potential of selected reaction monitoring to evaluate the protein abundance levels of candidate markers of neuropsychiatric spectrum disorders, providing a high throughput multiplex platform for validation of putative disease markers and drug targets.

Proteomics: improving biomarker translation to modern medicine?

Biomarkers are defi ned as ‘measurable characteristics that refl ect physiological, pharmacological, or disease processes’ according to the European Medicines Agency [1]. Th e ideal platforms for biomarker discovery include genomic, transcriptomic, proteomic, metabonomic and imaging analyses. However, most biomarkers used in clinical studies are based on proteomic applications as the majority of current drug targets are proteins, such as G protein-coupled receptors, ion channels, enzymes and components of hormone signaling pathways [2]. Furthermore, linking the results of biomarker studies using protein-protein interaction approaches can assist in systems biology approaches and could lead to hypo thesis generation and identifi cation of new drug targets [3]. Proteomic-based approaches for biomarker investigation can be employed in diff erent aspects of medicine, such as elucidation of pathways aff ected in disease, identifi cation of individuals who are at a high risk of developing disease for prognosis and prediction of response, identifi cation of individuals who are most likely to respond to specifi c therapeutic interventions, and

The use of proteomic biomarkers for improved diagnosis and stratification of schizophrenia patients

Schizophrenia is characterized by a wide spectrum of clinical manifestations, including strong effects on mood and behavior. Patients can also suffer from serious comorbidities including immune system or metabolic abnormalities. Recent advances using proteomic profiling approaches have increased our understanding of these molecular effects and have laid the groundwork for unraveling the heterogeneity of this broadly defined disease. These findings could lead to improved diagnosis and stratification of patients through identification of biochemically different disease subtypes and personalized medicine approaches. The inclusion of molecular signatures in psychiatry will be an important leap forward in providing more effective treatment of patients suffering from this debilitating disorder.

Lithium reverses behavioral and axonal transport-related changes associated with ANK3 bipolar disorder gene disruption

Ankyrin 3 (ANK3) has been implicated as a genetic risk factor for bipolar disorder (BD), however the resulting pathophysiological and treatment implications remain elusive. In a preclinical systems biological approach, we aimed to characterize the behavioral and proteomic effects of Ank3 haploinsufficiency and chronic mood-stabilizer treatment in mice. Psychiatric-related behavior was evaluated with the novelty-suppressed feeding (NSF) paradigm, elevated plus maze (EPM) and a passive avoidance task (PAT). Tandem mass spectrometry (MSE) was employed for hippocampal proteome profiling. A functional enrichment approach based on protein-protein interactions (PPIs) was performed to outline which biological processes in the hippocampus were affected by Ank3 haploinsufficiency and lithium treatment. Proteomic abundance changes as detected by MSE or highlighted by PPI network modelling were followed up by targeted selected reaction monitoring (SRM). Increased psychiatric-related behavior in Ank3+/- mice was ameliorated by lithium in all assessments (NSF, EPM, PAT). MSE followed by modular PPI clustering and functional annotation enrichment pointed towards kinesin-related axonal transport and glutamate signaling as mediators of Ank3+/- pathophysiology and lithium treatment. SRM validated this hypothesis and further confirmed abundance changes of ANK3 interaction partners. We propose that psychiatric-related behavior in Ank3+/- mice is connected to a disturbance of the kinesin cargo system, resulting in a dysfunction of neuronal ion channel and glutamate receptor transport. Lithium reverses this molecular signature, suggesting the promotion of anterograde kinesin transport as part of its mechanism of action in ameliorating Ank3-related psychiatric-related behavior.

Evaluation of molecular brain changes associated with environmental stress in rodent models compared to human major depressive disorder: A proteomic systems approach.

Abstract Objectives: Rodent models of major depressive disorder (MDD) are indispensable when screening for novel treatments, but assessing their translational relevance with human brain pathology has proved difficult. Methods: Using a novel systems approach, proteomics data obtained from post-mortem MDD anterior prefrontal cortex tissue (n = 12) and matched controls (n = 23) were compared with equivalent data from three commonly used preclinical models exposed to environmental stressors (chronic mild stress, prenatal stress and social defeat). Functional pathophysiological features associated with depression-like behaviour were identified in these models through enrichment of protein–protein interaction networks. A cross-species comparison evaluated which model(s) represent human MDD pathology most closely. Results: Seven functional domains associated with MDD and represented across at least two models such as “carbohydrate metabolism and cellular respiration” were identified. Through statistical evaluation using kernel-based machine learning techniques, the social defeat model was found to represent MDD brain changes most closely for four of the seven domains. Conclusions: This is the first study to apply a method for directly evaluating the relevance of the molecular pathology of multiple animal models to human MDD on the functional level. The methodology and findings outlined here could help to overcome translational obstacles of preclinical psychiatric research.

Serum biomarkers predictive of depressive episodes in panic disorder

Panic disorder with or without comorbid agoraphobia (PD/PDA) has been linked to an increased risk to develop subsequent depressive episodes, yet the underlying pathophysiology of these disorders remains poorly understood. We aimed to identify a biomarker panel predictive for the development of a depressive disorder (major depressive disorder and/or dysthymia) within a 2-year-follow-up period. Blood serum concentrations of 165 analytes were evaluated in 120 PD/PDA patients without depressive disorder baseline diagnosis (6-month-recency) in the Netherlands Study of Depression and Anxiety (NESDA). We assessed the predictive performance of serum biomarkers, clinical, and self-report variables using receiver operating characteristics curves (ROC) and the area under the ROC curve (AUC). False-discovery-rate corrected logistic regression model selection of serum analytes and covariates identified an optimal predictive panel comprised of tetranectin and creatine kinase MB along with patient gender and scores from the Inventory of Depressive Symptomatology (IDS) rating scale. Combined, an AUC of 0.87 was reached for identifying the PD/PDA patients who developed a depressive disorder within 2 years (n = 44). The addition of biomarkers represented a significant (p = 0.010) improvement over using gender and IDS alone as predictors (AUC = 0.78). For the first time, we report on a combination of biological serum markers, clinical variables and self-report inventories that can detect PD/PDA patients at increased risk of developing subsequent depressive disorders with good predictive performance in a naturalistic cohort design. After an independent validation our proposed biomarkers could prove useful in the detection of at-risk PD/PDA patients, allowing for early therapeutic interventions and improving clinical outcome.

Discovery of serum biomarkers predicting development of a subsequent depressive episode in social anxiety disorder.

Although social anxiety disorder (SAD) is strongly associated with the subsequent development of a depressive disorder (major depressive disorder or dysthymia), no underlying biological risk factors are known. We aimed to identify biomarkers which predict depressive episodes in SAD patients over a 2-year follow-up period. One hundred sixty-five multiplexed immunoassay analytes were investigated in blood serum of 143 SAD patients without co-morbid depressive disorders, recruited within the Netherlands Study of Depression and Anxiety (NESDA). Predictive performance of identified biomarkers, clinical variables and self-report inventories was assessed using receiver operating characteristics curves (ROC) and represented by the area under the ROC curve (AUC). Stepwise logistic regression resulted in the selection of four serum analytes (AXL receptor tyrosine kinase, vascular cell adhesion molecule 1, vitronectin, collagen IV) and four additional variables (Inventory of Depressive Symptomatology, Beck Anxiety Inventory somatic subscale, depressive disorder lifetime diagnosis, BMI) as optimal set of patient parameters. When combined, an AUC of 0.86 was achieved for the identification of SAD individuals who later developed a depressive disorder. Throughout our analyses, biomarkers yielded superior discriminative performance compared to clinical variables and self-report inventories alone. We report the discovery of a serum marker panel with good predictive performance to identify SAD individuals prone to develop subsequent depressive episodes in a naturalistic cohort design. Furthermore, we emphasise the importance to combine biological markers, clinical variables and self-report inventories for disease course predictions in psychiatry. Following replication in independent cohorts, validated biomarkers could help to identify SAD patients at risk of developing a depressive disorder, thus facilitating early intervention.

Translational neuropsychiatry of genetic and neurodevelopmental animal models of schizophrenia

Psychiatric symptoms are subjective by nature and tend to overlap between different disorders. The modelling of a neuropsychiatric disorder therefore faces challenges because of missing knowledge of the fundamental pathophysiology and a lack of accurate diagnostics. Animal models are used to test hypotheses of aetiology and to represent the human condition as close as possible to increase our understanding of the disease and to evaluate new targets for drug discovery. In this review, genetic and neurodevelopmental animal models of schizophrenia are discussed with respect to behavioural and neurophysiological findings and their association with the clinical condition. Only specific animal models of schizophrenia may ultimately lead to novel diagnostic approaches and drug discovery. We argue that molecular biomarkers are important to improve animal to human translation since behavioural readouts lack the necessary specificity and reliability to assess human psychiatric symptoms.