J Neural Transm DOI 10.1007/s00702-013-1134-6
PSYCHIATRY AND PRECLINICAL PSYCHIATRIC STUDIES - REVIEW ARTICLE
The potential of biomarkers in psychiatry: focus on proteomics Izabela Sokolowska • Armand G. Ngounou Wetie Kelly Wormwood • Johannes Thome • Costel C. Darie • Alisa G. Woods
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Received: 17 October 2013 / Accepted: 2 December 2013 Ó Springer-Verlag Wien 2013
Abstract The etiology and pathogenesis of many psychiatric disorders are unclear with many signaling pathways and complex interactions still unknown. Primary information provided from gene expression or brain activity imaging experiments is useful, but can have limitations. There is a current effort focusing on the discovery of diagnostic and prognostic proteomic potential biomarkers for psychiatric disorders. Despite this work, there is still no biological diagnostic test available for any mental disorder. Biomarkers may advance the care of psychiatric illnesses and have great potential to knowledge of psychiatric disorders but several drawbacks must be considered. Here, we describe the potential of proteomic biomarkers for better understanding and diagnosis of psychiatric disorders and current putative biomarkers for schizophrenia, depression, autism spectrum disorder and attention deficit/hyperactivity disorder. Keywords Biomarkers Psychiatry Mass spectrometry Proteomics Abbreviations ASD Autism spectrum disorder ADHD Attention-deficit/hyperactivity disorder EEG Electroencephalography MRI Magnetic resonance imaging
I. Sokolowska A. G. Ngounou Wetie K. Wormwood C. C. Darie A. G. Woods (&) Biochemistry and Proteomics Group, Department of Chemistry and Biomolecular Science, Clarkson University, 8 Clarkson Avenue, Potsdam, NY 13699-5810, USA e-mail: [email protected] J. Thome Department of Psychiatry, University of Rostock, Gehlsheimerstraße 20, 18147 Rostock, Germany
SPECT CSF MDD MS 2-DE-PAGE LC–MS/MS MALDI SELDI DIGE ICAT AQUA ELISA WB Q-PCR NRG-1 SPEM1 NADPH VGF GRP78 DPYSL2 ALDOC CPX-2 NPY DHEA HDL
Single photon emission computed tomography Cerebral spinal fluid Major depressive disorders Mass spectrometry 2-dimensional gel electrophoresis Liquid chromatography–mass spectrometry Matrix assisted laser desorption ionization Surface enhanced laser/desorption ionization Differential gel electrophoresis Isotope-coded affinity tag Absolute quantitation Enzyme-linked immunoabsorbent assay Western blotting Quantitative polymerase chain reaction Neuregulin-1 Smooth-pursuit eye movement Nicotinamide adenine dinucleotide phosphate Nerve growth factor inducible Glucose-regulated 78 kDa protein Dihydropyrimidinase-related protein 2 Aldolase C Cyclooxygenase-2 Neuropeptide Y Manganese, dehydroepiandrosterone High-density lipoprotein
Introduction: what are biomarkers and why do we search for them? Psychiatric disorders such as schizophrenia, mood disorders, autism spectrum disorder (ASD) or attention-
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deficit/hyperactivity disorder (ADHD) can be disabling conditions with incompletely understood etiology and biological mechanisms. Psychiatric disorders are extremely challenging to understand in terms of their pathology and physiology, making them difficult to accurately diagnose. Clinicians could greatly benefit from sensitive and specific biomarkers to supplement subjective and variable behavioral and purely symptomatic measurements. Although biomarkers are successfully used in predicting diseases such as cancer, to date there is no laboratory test that is used clinically for diagnosis of psychiatric disorders (Lakhan et al. 2010; Pallis et al. 2011; Ross 2011). What is a biomarker? The United States food and drug administration (FDA) defines a biomarker as an objective measurement of normal processes, pathological processes or pharmacological response to a therapeutic (Lesko and Atkinson 2001; Biomarkers Definitions Working Group 2001; Darie et al. 2008). A biomarker is a potential parameter/parameters measured and clearly differentiated between normal versus pathological states. This includes genes, their products such as RNA, proteins and post-translational modification of those proteins, lipids or even biomolecular complexes. Therefore, virtually any molecule present in human body is a potential biomarker candidate if its level can be associated with a disorder (Woods et al. 2012a). Electroencephalography (EEG), magnetic resonance imaging (MRI) and single photon emission computed tomography (SPECT) are frequently used clinically to assess neurological conditions. Brain activity patterns using measured by these instruments have also been proposed as biomarkers for specific disorders (Whalley et al. 2012; Muller et al. 1997; Bremner et al. 1995; WintonBrown and Kapur 2009; Howes et al. 2009). Genetic biomarkers for several psychiatric disorders have been studied extensively (Le-Niculescu et al. 2009; Poelmans et al. 2011; Barnett and Smoller 2009; Kvajo et al. 2010). However, genetic information, although useful, does not represent actual function. Current biomarker discovery progression may, therefore, shift towards the true effectors of physiological functions: proteins. An expressed set of proteins and their interaction with other molecules varies between tissue and cells in a given time, space and under different physiological conditions, especially when normal and pathological states are compared. Therefore, proteomic biomarker discovery is an area with enormous potential. Because of easy accessibility, fluids such as blood, urine and saliva are great source for putative proteomic biomarker candidate discovery. Cerebral spinal fluid (CSF) and brain tissue can also potentially be used.
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Characteristics of a good biomarker Diagnostic specificity, reliability and reproducibility of a putative psychiatric biomarker should ideally be high. Confirmation by at least two independent published studies is optimal. Obtaining biological samples for a diagnostic test should be ideally non-invasive, which is especially important in psychiatric disorders, since patients often have conditions that should typically not be exacerbated by additional stress. Many of the studies to be discussed utilize blood for analysis, which is an invasive procedure. Naturally, brain tissue or cerebrospinal fluid analysis is also invasive. Samples requiring non-invasive procedures are possible for use, however, and studies of saliva, urine, or cheek swabs could be of interest for future study. A desirable diagnostic biomarker should meet several characteristics: (1) reliability, accuracy and the limitation of a test have to be well characterized; (2) the development process for biomarkers should be clear and disclosed; (3) results should be reproducible with an independent replication; (4) biomarker interpretation should allow comparison with other neurological observations; (5) information provided by a biomarker should be timely and cost-effective with significant clinical usefulness; (6) technology for a test should be available and tolerated by the general target population; (7) methodology should be cheap, simple and easily integrated into a clinical care practice [adapted from (Cook 2008)]. A single molecule as a biomarker candidate may not be sufficient for accurate and reliable diagnosis; therefore, the current trend in psychiatry has shifted towards identification of signature set of biomarkers (Lakhan et al. 2010; Singh and Rose 2009). After obtaining a clinical biomarker signature set for a particular disorder in humans, it would be optimal to compare it with those obtained from animal models for the same disorder. Proteomic biomarker analysis integrated across species could help to create a comprehensive model, which ideally would also include genetics, imaging, electrophysical and behavioral measures. Animal models could potentially be used to test the effect of existing and novel therapeutic drugs on the biomarker fingerprint and to identify additional biomarkers and drug targets. Why do we need biomarkers for psychiatric disorders? Psychiatric diagnosis currently relies on purely behavioral symptoms as defined by the DSM-5 or ICD-10 (APA 2013; World Health Organization 1992). These guides change with additional revisions and also do not always have the same criteria for diagnosis as one another (Woods et al. 2013a). This attests to the possible subjectivity of these manuals. Due to very high heterogeneity of psychiatric diseases, diagnosis is often challenging. With the use of
The potential of biomarkers in psychiatry
biomarkers, clinicians may be able to differentiate between disorders or distinguish between disorders subtypes which frequently can have different underlying pathophysiology. Putative biomarkers would allow better monitoring of treatment response or prediction of disorder progression, and help to determine treatment strategy. Biomarkers could aid with the prediction of the susceptibility for a disorder and possibly promote the use of preventive treatments. Biomarkers in psychiatry: potential drawbacks Establishment of routine diagnostic screening can raise ethical concerns, especially concerning the use of collected information. Known predisposition of individuals to certain diseases may cause discrimination. One of the biggest concerns is potential discrimination by health insurance companies or employers, which could deny individual health coverage or employment basing their decision solely on genetic predisposition to a psychiatric disorder. In the USA, the genetic information non-discrimination act was legislated in 2008, preventing such practices; unfortunately it is strictly limited to DNA, RNA and chromosomal changes (Tan 2009). This act does not cover non-genetic psychiatric biomarkers such as brain activity measurement or protein profiling. There is also the potential for misusing predictive biomarkers in reproductive health in practices such as selective abortion. As an example of ethical concerns that can arise from biomarker identification, the FDA issued a recent warning to the company, 23 and Me, which has marketed a kit to be used for personalized biomarker identification and healthcare advice. The FDA indicated the following problems in their letter: lack of reliability of the tests included, lack of test assessment, possibility for false positive/false negative results and lack of approval for use of the kit by the FDA inspections (FDA Warning Letter 2013). In December, 2013, 23 and me discontinued providing their interpretation customers’ test results, although the test itself and genetic information provided by it is still being sold. Another major issue is cost. Any new technology can increase the price of medical treatment. Diagnostic tests can be relatively expensive. None of the currently investigated biomarkers is definitively predictive for the progression of psychiatric disorders; therefore, investments in such a costly screening procedure may be considered by many not worth its price. It should also be considered, in the case of MS-based proteomics, that this is currently an expensive technique. Proteomics may be developing into a more cost effective method over time. However, it is also important to note that the diagnostic potential of proteomics needs to be considered. If the cost savings over a lifetime gained by effective treatment/prevention of a disorder are greater than an initial
costly test, then proteomics are indeed cost effective. It may be the case in some instances that proteomics is an effective method for biomarker identification, but actual clinical tests should be based on ELISA or some other less costly method, once biomarkers have been identified. Several companies are already working on first screening platforms, focusing mostly on treatment selection for people already diagnosed. Brain resources started iSpot studies, employing various diagnostic tools to predict optimized depression treatment (Williams et al. 2011). A different company, Genomind, developed the genecept assay which evaluates genetic markers from saliva such as the serotonin transporter, glutamate or dopamine to predict psychiatric treatment response and aids clinicians in selecting the right treatments for their patients initially, rather than through trial and error (Genomind 2012).
Proteomics in psychiatric biomarkers discovery When addressing extensive biological mechanisms involved in psychiatric disorders, data obtained from differential gene expression experiments are useful but incomplete. Gene expression patterns do not consistently and entirely correlate with protein expression patterns and cannot precisely identify and quantify essential functional modulators (Neale et al. 2012)—proteins, which play the central functional role in disorders, making their analysis as biomarkers appealing. Proteins can change in several ways, including by changes in protein levels, but also by changes in conformation or activity (Ideker et al. 2001; Gygi et al. 1999b). Proteomics is the study of any protein expressed by a given cell, tissue or organism at a particular time under known and determined conditions (Wilkins et al. 1996; Darie et al. 2005a, 2008a, 2011; Ngounou Wetie et al. 2012; Sokolowska et al. 2011, 2012c, d; Woods et al. 2011). It includes the modification of a protein or set of proteins by cellular mechanisms varying under the influence of certain internal and environmental factors (Darie et al. 2004, 2005a, b, 2008a; Sokolowska et al. 2011, 2012b, c; Spellman et al. 2008; Woods et al. 2012b). Proteomic approaches can identify post-translational modifications of proteins which are essentially a quick and dynamic method protein function control (Darie et al. 2004, 2005a, 2008; Sokolowska et al. 2011, 2012c, d; Woods et al. 2011, 2012a), as well as new proteins (Sokolowska et al. 2012a, d; Woods et al. 2012a; Roy et al. 2012). Therefore, qualitative and quantitative protein analysis and identification from body fluids (serum/plasma, saliva, urine, CSF) and tissues (cell lines, biopsies, and human post-mortem tissues) provide an important biomarker detection tool. Typically, these analyses are performed using mass spectrometry (MS), a critical
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tool for proteomic studies (Darie 2013; Ngounou Wetie et al. 2013a, b, c, d, e; Sokolowska et al. 2013a, b; Woods et al. 2013b). Proteomic analysis for biomarker discovery consists of several steps: (1) isolation of proteins from different tissues or body fluids (for example under pathological versus normal conditions); (2) fractionation and separation of a complex mixture of proteins; (3) analysis of separated fractions by MS and (4) use of bioinformatics’ tools and specific databases for data processing. To reduce sample complexity, fractionation and enrichment procedures are performed mostly using various chromatographic and electrophoresis techniques to separate proteins based on their characteristics such as molecular mass, isoelectric point, hydrophobicity or charge. Biochemically fractionated proteins are analyzed by a mass spectrometer (MS) which is the primary method for proteomic studies. MS and bioinformatics technologies allow identification and characterization of protein patterns differentially expressed in pathological states versus controls. Several methods for proteins profiling such as 2-dimensional gel electrophoresis (2-DE-PAGE) with subsequent LC–MS/MS identification, matrix assisted laser desorption ionization (MALDI) or fingerprinting profiles using surface enhanced laser/ desorption ionization (SELDI) have emerged recently (Lakhan 2006). MS-based techniques can provide quantitative or qualitative data. Various quantification methods, such as differential gel electrophoresis (DIGE) (Viswanathan et al. 2006), isotope-coded affinity tag (ICAT) (Gygi et al. 1999a), absolute quantitation (AQUA) or label free methods such as peak intensities or spectra counting allow accurate proteins level estimation in samples (Zhu et al. 2010). It is necessary to validate results with different protein detection methods such as ELISA or Western Blot (WB) and very often on the mRNA level (Northern Blot, Q-PCR). Although MS is a comprehensive protein measurement technique, directed protein detection methods also exist.
Current trends and putative proteomics biomarkers in psychiatric disorders Many factors including genes, proteins, their interactions and environmental influences likely contribute to the development of a mental disorder. Reliance on only one single biomarker rather than a signature set could potentially lead to misdiagnosis, particularly since many disorders likely have overlapping underlying mechanisms. Development of a signature set of biomarker for particular disorder is more realistic. One good example was described by Pies, who defined a signature set of biomarkers for schizophrenia which includes: (1) genetic marker—
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neuregulin-1 mutations increase susceptibility to schizophrenia; (2) brain activity marker—abnormal smooth-pursuit eye movement observed people with schizophrenia; (3) brain changes markers—schizophrenic individuals showed reduced anterior cingulate volumes, enlarged lateral and third ventricular volumes and white matters abnormalities (Pies 2008). This is a comprehensive signature including more than one measurement as well as measurements obtained via different methodologies. A simple search in PubMed using ‘‘biomarker’’ and the disorder reveals almost 3,500 results for depression, around 2,000 for schizophrenia but only around 250 for ADHD, primarily published in last 10 years. Increases in the search for biomarkers for psychiatric disorders are very promising but it is important to note that most published findings are initial and largely unconfirmed by additional independent studies. The human brain proteome project by the human proteome organization endeavors to assist in more effective communication and exchange of proteomic data regarding brain research (Hamacher and Meyer 2005; Stephan et al. 2005). The project’s goal is to characterize and compare data from the human and mouse brain proteome to aid in faster development of reliable and accurate biomarkers (Turck and Iris 2011). Schizophrenia biomarkers Schizophrenia is an incompletely understood psychiatric condition affecting around 2 million people in the US alone and almost 1 % of the world population (Lakhan 2006; van Os and Kapur 2009). No laboratory screening test is currently available and diagnosis is based largely on behavior, past history and duration of symptoms, both positive and negative (World Health Organization 1992; American Psychiatric Association 2000). The availability of objective and reliable markers could allow for more precise schizophrenia detection, early therapeutic intervention and could also point towards new therapeutic strategies (Martins-DeSouza 2010). Numerous genetic studies have identified specific markers or chromosomal regions associated with schizophrenia but none showed an acceptable genome-wide significance (Purcell et al. 2009; Ng et al. 2009; Glessner and Hakonarson 2009). Analysis of thalamus proteins of schizophrenia patients and control non-schizophrenia individuals (who never suffered from psychiatric or neurological disorders) revealed 50 proteins with significant differential expression (Martins-de-Souza et al. 2010c). The most common alternations were related to oligodendrocyte (myelin basic protein and myelin oligodendrocyte protein, glial fibrillary acidic protein) and energy metabolism (pyruvate, NADPH) (Martins-de-Souza et al. 2010b). Additional analysis of metabolites in CSF showed
The potential of biomarkers in psychiatry Table 1 Characteristics of a good marker, advantages and disadvantages of biomarkers Characteristics of a good biomarker
Diagnostic specificity/accuracy Reliability Reproducibility Non-invasive test needed Test limitations well characterized Development process clear and disclosed Should allow comparison with other neurological observations Clinical usefulness
characterization and understanding of the disorder (Lakhan 2006; Martins-de-Souza 2010; Lakhan and Kramer 2009; Falkai and Moller 2012). It should also be noted that brain and CSF-derived biomarkers described by this study would not necessarily meet ‘‘good’’ clinical biomarker criteria (Table 1), since these are invasive procedures, although biomarkers obtained using these procedures may be informative for research purposes and may be reflected in other more attainable bodily fluids. Major depressive disorder (biomarkers)
Technology available Test to obtain tolerated by the general target population Inexpensive Simple Easily integrated into clinical practice Comparable to animal model observations Comparable to other biomarkers Advantages/uses of proteomic biomarkers in psychiatric disorders
Objective diagnosis Supplement to behavioral assessments Potential to differentiate between disorders with similar symptoms Distinguish between disorder subtypes Monitoring treatment response Prediction of disorder progression Prediction of disorder susceptibility Aid in treatment selection/strategy Promotion of preventative treatments Indicate treatment targets Elucidate disorder etiology
Disadvantages of proteomic biomarkers in psychiatric disorders
Ethical misuse/discrimination Cost Lack of predictive value Further complicates diagnosis Increases time/labor
alternations in glutamatergic neurotransmission and cannabinoid metabolism (Vasic et al. 2012). Profiling of phosphorylation patterns (such as protein phosphorylation) in serum proteins in individuals with schizophrenia versus controls showed 72 phosphoproteins with altered phosphorylation pattern (Jaros et al. 2012). Several molecules are currently under investigation such as hypothalamic VGF (nerve growth factor inducible) expression (Busse et al. 2012) and apolipoprotein A1—apoA1 (Yang et al. 2006; Claassen et al. 2007). Apolipoprotein A2 and A4 and transferrin were also identified as differentially expressed proteins in serum from people with schizophrenia (Levin et al. 2010). Proteomic investigations in schizophrenia are yet to discover biomarker with accurate diagnostic specificity, but they have the potential to advance our better
Major depressive disorder is a serious and prevalent psychiatric problem affecting around 10 % of the world population (Saraceno 2002). When selecting antidepressant treatment, clinicians typically try a series of medication, and/ or psychotherapy and potentially other non-pharmacological medical treatments (for example, electroconvulsive therapy or transcranial magnetic stimulation). Through essentially trial and error they come up with a treatment for that individual. Biomarkers directing the optimal treatments for an individual could reduce the time it takes for an antidepressant treatment to work (Martins-De-Souza et al. 2010a; Krishnan and Nestler 2010). In genetic studies, polymorphisms in the promoter region (5HT-T) of the serotonin transporter (known as SLC6A4) increase the susceptibility to developing depression as a consequence of stressful life events, such as childhood abuse (Caspi et al. 2003). Glial marker S100B is elevated in mood disorders, and this is especially strong in depression (Schroeter et al. 2010, 2011). Most proteomic studies examining depression have focused on response to therapeutic treatment, which is currently based on increasing synaptic levels of serotonin and norepinephrine (Skolnick et al. 2009; To et al. 2005). In vivo studies using animal rat models assessed differential protein expression after exposure to stress. Identified hippocampal proteins included those playing roles in protein folding, signal transduction, neuronal plasticity and metabolism (Carboni et al. 2006). In vitro experiments assessed exposure of rat cortical neurons to long-term antidepressant (fluoxetine) treatment and identified proteins involved in neuroprotection (cyclophilin A), serotonin biosynthesis (14-3-3 protein z/delta) and axonal transport (glucose regulated protein 78 kDa, GRP78) (Cecconi et al. 2007). 2-DE-PAGE analysis of two brain regions, the frontal cortex and accumbens, showed altered expression of DPYSL2 (plays a role in neuronal development), carbonic anhydrase and ALDOC (both play role in energy metabolism) (Beasley et al. 2006; Johnston-Wilson et al. 2000). A proteomic study of hippocampus in animal models of depression showed differentially expressed proteins with a role in oxidative stress and neurogenesis and maintenance
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of neuronal processes (Mu et al. 2007; Khawaja et al. 2004). Elevated levels of peripheral inflammatory biomarkers (e.g., interleukin 6, tumor necrosis factor alpha) have been suggested to be associated with depression (Krishnadas and Cavanagh 2012). Many currently used antidepressants have an additional anti-inflammatory function (Kubera et al. 2001, 2004; Maes 2001). New potential anti-inflammatory molecules, cyclooxygenase-2 (COX-2) inhibitors, were tested in animal models and showed favorable effects in depression treatment versus placebo (Muller et al. 2011). Therefore, identification of inflammatory biomarkers such as cytokines may be possible to be applied to diagnosis and prediction of prognosis of depression as well as to monitoring treatment response (Li et al. 2011). To serve a clinical purpose, however, these depression biomarkers identified in animal models would need to be validated in humans and in accessible biomaterials. Autism spectrum disorder (ASD) biomarkers ASDs are complex disabilities characterized by problems with communication and social interaction with prevalence of 1 in 88 children in the USA (Autism and Developmental Disabilities Monitoring Network Surveillance Year 2008 Principal Investigators 2012). A recent survey of parents suggests that the incidence may be as high as 1 in 50 children (Blumberg 2013). Discovery of biomarkers for ASD is important, especially because children affected with ASDs cannot easily communicate their symptoms. Fragile X syndrome is the best-understood genetic cause of autism (Hagerman et al. 2012); however, the cause of the majority of forms of autism is unknown. New evidence reveals the association of ASD with events like oxidative stress and mitochondrial dysfunction (Junaid and Pullarkat 2001). Analysis of potential peripheral markers using MSbased methods revealed immune system alterations in cerebrospinal fluid and blood taken from people with ASD (Zimmerman et al. 2005; Molloy et al. 2006). Results were confirmed by LC–MS/MS showing apolipoprotein and complement is differentially expressed in serum from children with ASD versus controls (Woods et al. 2012b; Corbett et al. 2007). Proteomic analysis can reveal posttranslational modifications, for example increased polarity of glyoxalase 1 which was increased in the gray matter of ASD brains (Junaid et al. 2004). Serum protein profiling experiments suggested that altered protein levels in peripheral blood might represent useful biomarkers in future diagnosis (Taurines et al. 2010). HPLC-ESI-Ion Trap-MS analysis of saliva samples revealed a significant difference in protein phosphorylation in ASD patients versus unaffected controls (Castagnola et al. 2008). Correlation of genetic information, proteomics data and
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behavioral analysis will undoubtedly advance the field in finding predictive protein markers in ASD. Accessible fluids, such as blood and saliva, would be most amenable to a diagnostic test based on biomarkers, although in disorders with a central pathogenesis it is also important to consider whether peripheral fluids reflect brain protein changes. Direct comparisons of cerebrospinal fluid, blood and saliva contents in the same individuals may help to address this. According to one study, hyperphosphorylated tau, a brain marker of Alzheimer’s disease, was detected in saliva of individuals with Alzheimer’s disease although beta-amyloid protein (another marker) was not. This study illustrates that bodily fluids can reflect central nervous system changes, although not in all instances (Shi et al. 2011). ADHD biomarkers ADHD affects 4–12 % of school-age children (Brown et al. 2001). It is characterized by inattention and/hyperactive impulsivity, either alone or in combination (Brown et al. 2001). Children diagnosed with ADHD represent a heterogeneous group with a high variation in their symptoms. The diagnostic process is still somewhat unstructured and can be relatively easily biased (Berger 2011). A metaanalysis of genetic information showed several implicated genes, although none of them can be used as a true predictive marker (Gizer et al. 2009; Faraone and Mick 2010). Several genetic studies identified the main contributing factors as prefrontal dopamine deficiency and central dopaminergic dysfunction, but changes in oxidative metabolism and immunity were also suggested (Ceylan et al. 2012; Swanson et al. 2007). Polymorphisms in the dopamine transporter and D4 receptor were also suggested as biomarkers (Froehlich et al. 2010; McGough 2012). Proteomic approaches have been explored in the search for ADHD biomarkers. Urinary serotonin levels have been studied in ADHD using proteomic studies involving LC–MS/MS (Moriarty et al. 2011, 2012). A case studies series spanning 40 years revealed significant heterogeneity of size effects across studies for several potential biomarkers and confirmed association between studies for neuropeptide Y (NPY), dehydroepiandrosterone (DHEA) (Scassellati et al. 2012). Additionally, free cholesterol, HDL and ApoA1 were found to be higher in children with ADHD versus controls (Spahis et al. 2008). While the search for biomarkers has improved knowledge about the molecular biology and pathology of ADHD, further information is needed. The current state of ADHD biomarker identification has been recently summarized in a comprehensive review article. Unfortunately, reliable ADHD biomarkers still remain unidentified (Thome et al. 2012).
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Discussion The study of putative psychiatric biomarkers is very complex because of the heterogeneous nature of psychiatric disorders which are often referred to as syndromes with several subtypes, not uniform problems. Therefore, a single biomarker is very unlikely to provide enough information to identify cellular and metabolic pathways involved in a particular individual. Identification of a signature set of biomarkers for disorder subtypes each based on their underlying biological pathways will be the most effective for diagnosis and treatment selection. It may become more powerful to predict the course and outcome of these disorders. As new neuroscience and proteomic discoveries have started to revolutionize the science of psychiatric disorders, biomarkers have come to be quite possible diagnostic tools in the near future. We are yet to see them being used in routine screening tests. Psychiatric disorders are very often overlapping creating greater challenge in accurate and specific diagnosis. Even though progress in proteomics technologies is encouraging, those approaches are still in development with drawbacks to overcome. Generally, any given tissue or body fluid would have millions of proteins expected but using current methods only a small fraction can be detected. Insoluble membraneassociated proteins (such as receptor, ion channels, G proteins) or very hydrophobic, acidic, basic and small proteins are very often omitted in traditional 2DE-PAGE experiments. Moreover, low-abundance of many essential proteins and small amount of available material (especially brain tissue) create further limitations. Therefore, those difficulties have to be addressed by development of more efficient sample prefractionation and separation methods prior to mass spectrometry analysis. Additional characteristics of protein biomarkers, such as expression levels, post-translational modifications, protein interactions, lead to better understanding of nervous system physiology under normal and pathological conditions and greater insight into the pathophysiology of psychiatric disorders (Taurines et al. 2011). Considering the high occurrence and important effect on society of psychiatric disorders, more broad and systematic studies in human fluids and tissues from affected patients are required. These types of experiments would create a large amount of data. Subsequently, there is need for establishing of multiinstitutional collaborations for obtaining large-scale experimental data which should be a properly integrated, comprehensive analysis and public database. Cooperation between clinicians, chemists, biochemists, neuroscientist, bioinformatics and statisticians is needed. Assessment of patients’ individual protein profiles for diagnosis and monitoring of therapeutic treatment is a first step in personalized medicine in psychiatry. Together with
understanding of molecular mechanisms, signaling pathways and protein–protein interactions provided by proteomics approaches supported with genomics and metabolomics data, personalized medicine will likely facilitate development of future diagnostic and prognostic biomarkers as well as novel therapeutic drugs. Acknowledgments This work was supported in part by Clarkson University (start-up grant to CCD), private donations (Mary Stewart Joyce, Ken Sandler, Bob Matloff and the SciFund Challenge Donors), the Redcay Foundation (SUNY Plattsburgh), the Alexander von Humboldt Foundation, the University of Rostock, Germany and by the U.S. Army research office through the Defense University Research Instrumentation Program (DURIP grant #W911NF-11-10304). The authors declare that they have no competing and/or financial interests.
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PSYCHIATRY AND PRECLINICAL PSYCHIATRIC STUDIES - REVIEW ARTICLE
The potential of biomarkers in psychiatry: focus on proteomics Izabela Sokolowska • Armand G. Ngounou Wetie Kelly Wormwood • Johannes Thome • Costel C. Darie • Alisa G. Woods
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Received: 17 October 2013 / Accepted: 2 December 2013 Ó Springer-Verlag Wien 2013
Abstract The etiology and pathogenesis of many psychiatric disorders are unclear with many signaling pathways and complex interactions still unknown. Primary information provided from gene expression or brain activity imaging experiments is useful, but can have limitations. There is a current effort focusing on the discovery of diagnostic and prognostic proteomic potential biomarkers for psychiatric disorders. Despite this work, there is still no biological diagnostic test available for any mental disorder. Biomarkers may advance the care of psychiatric illnesses and have great potential to knowledge of psychiatric disorders but several drawbacks must be considered. Here, we describe the potential of proteomic biomarkers for better understanding and diagnosis of psychiatric disorders and current putative biomarkers for schizophrenia, depression, autism spectrum disorder and attention deficit/hyperactivity disorder. Keywords Biomarkers Psychiatry Mass spectrometry Proteomics Abbreviations ASD Autism spectrum disorder ADHD Attention-deficit/hyperactivity disorder EEG Electroencephalography MRI Magnetic resonance imaging
I. Sokolowska A. G. Ngounou Wetie K. Wormwood C. C. Darie A. G. Woods (&) Biochemistry and Proteomics Group, Department of Chemistry and Biomolecular Science, Clarkson University, 8 Clarkson Avenue, Potsdam, NY 13699-5810, USA e-mail: [email protected] J. Thome Department of Psychiatry, University of Rostock, Gehlsheimerstraße 20, 18147 Rostock, Germany
SPECT CSF MDD MS 2-DE-PAGE LC–MS/MS MALDI SELDI DIGE ICAT AQUA ELISA WB Q-PCR NRG-1 SPEM1 NADPH VGF GRP78 DPYSL2 ALDOC CPX-2 NPY DHEA HDL
Single photon emission computed tomography Cerebral spinal fluid Major depressive disorders Mass spectrometry 2-dimensional gel electrophoresis Liquid chromatography–mass spectrometry Matrix assisted laser desorption ionization Surface enhanced laser/desorption ionization Differential gel electrophoresis Isotope-coded affinity tag Absolute quantitation Enzyme-linked immunoabsorbent assay Western blotting Quantitative polymerase chain reaction Neuregulin-1 Smooth-pursuit eye movement Nicotinamide adenine dinucleotide phosphate Nerve growth factor inducible Glucose-regulated 78 kDa protein Dihydropyrimidinase-related protein 2 Aldolase C Cyclooxygenase-2 Neuropeptide Y Manganese, dehydroepiandrosterone High-density lipoprotein
Introduction: what are biomarkers and why do we search for them? Psychiatric disorders such as schizophrenia, mood disorders, autism spectrum disorder (ASD) or attention-
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deficit/hyperactivity disorder (ADHD) can be disabling conditions with incompletely understood etiology and biological mechanisms. Psychiatric disorders are extremely challenging to understand in terms of their pathology and physiology, making them difficult to accurately diagnose. Clinicians could greatly benefit from sensitive and specific biomarkers to supplement subjective and variable behavioral and purely symptomatic measurements. Although biomarkers are successfully used in predicting diseases such as cancer, to date there is no laboratory test that is used clinically for diagnosis of psychiatric disorders (Lakhan et al. 2010; Pallis et al. 2011; Ross 2011). What is a biomarker? The United States food and drug administration (FDA) defines a biomarker as an objective measurement of normal processes, pathological processes or pharmacological response to a therapeutic (Lesko and Atkinson 2001; Biomarkers Definitions Working Group 2001; Darie et al. 2008). A biomarker is a potential parameter/parameters measured and clearly differentiated between normal versus pathological states. This includes genes, their products such as RNA, proteins and post-translational modification of those proteins, lipids or even biomolecular complexes. Therefore, virtually any molecule present in human body is a potential biomarker candidate if its level can be associated with a disorder (Woods et al. 2012a). Electroencephalography (EEG), magnetic resonance imaging (MRI) and single photon emission computed tomography (SPECT) are frequently used clinically to assess neurological conditions. Brain activity patterns using measured by these instruments have also been proposed as biomarkers for specific disorders (Whalley et al. 2012; Muller et al. 1997; Bremner et al. 1995; WintonBrown and Kapur 2009; Howes et al. 2009). Genetic biomarkers for several psychiatric disorders have been studied extensively (Le-Niculescu et al. 2009; Poelmans et al. 2011; Barnett and Smoller 2009; Kvajo et al. 2010). However, genetic information, although useful, does not represent actual function. Current biomarker discovery progression may, therefore, shift towards the true effectors of physiological functions: proteins. An expressed set of proteins and their interaction with other molecules varies between tissue and cells in a given time, space and under different physiological conditions, especially when normal and pathological states are compared. Therefore, proteomic biomarker discovery is an area with enormous potential. Because of easy accessibility, fluids such as blood, urine and saliva are great source for putative proteomic biomarker candidate discovery. Cerebral spinal fluid (CSF) and brain tissue can also potentially be used.
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Characteristics of a good biomarker Diagnostic specificity, reliability and reproducibility of a putative psychiatric biomarker should ideally be high. Confirmation by at least two independent published studies is optimal. Obtaining biological samples for a diagnostic test should be ideally non-invasive, which is especially important in psychiatric disorders, since patients often have conditions that should typically not be exacerbated by additional stress. Many of the studies to be discussed utilize blood for analysis, which is an invasive procedure. Naturally, brain tissue or cerebrospinal fluid analysis is also invasive. Samples requiring non-invasive procedures are possible for use, however, and studies of saliva, urine, or cheek swabs could be of interest for future study. A desirable diagnostic biomarker should meet several characteristics: (1) reliability, accuracy and the limitation of a test have to be well characterized; (2) the development process for biomarkers should be clear and disclosed; (3) results should be reproducible with an independent replication; (4) biomarker interpretation should allow comparison with other neurological observations; (5) information provided by a biomarker should be timely and cost-effective with significant clinical usefulness; (6) technology for a test should be available and tolerated by the general target population; (7) methodology should be cheap, simple and easily integrated into a clinical care practice [adapted from (Cook 2008)]. A single molecule as a biomarker candidate may not be sufficient for accurate and reliable diagnosis; therefore, the current trend in psychiatry has shifted towards identification of signature set of biomarkers (Lakhan et al. 2010; Singh and Rose 2009). After obtaining a clinical biomarker signature set for a particular disorder in humans, it would be optimal to compare it with those obtained from animal models for the same disorder. Proteomic biomarker analysis integrated across species could help to create a comprehensive model, which ideally would also include genetics, imaging, electrophysical and behavioral measures. Animal models could potentially be used to test the effect of existing and novel therapeutic drugs on the biomarker fingerprint and to identify additional biomarkers and drug targets. Why do we need biomarkers for psychiatric disorders? Psychiatric diagnosis currently relies on purely behavioral symptoms as defined by the DSM-5 or ICD-10 (APA 2013; World Health Organization 1992). These guides change with additional revisions and also do not always have the same criteria for diagnosis as one another (Woods et al. 2013a). This attests to the possible subjectivity of these manuals. Due to very high heterogeneity of psychiatric diseases, diagnosis is often challenging. With the use of
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biomarkers, clinicians may be able to differentiate between disorders or distinguish between disorders subtypes which frequently can have different underlying pathophysiology. Putative biomarkers would allow better monitoring of treatment response or prediction of disorder progression, and help to determine treatment strategy. Biomarkers could aid with the prediction of the susceptibility for a disorder and possibly promote the use of preventive treatments. Biomarkers in psychiatry: potential drawbacks Establishment of routine diagnostic screening can raise ethical concerns, especially concerning the use of collected information. Known predisposition of individuals to certain diseases may cause discrimination. One of the biggest concerns is potential discrimination by health insurance companies or employers, which could deny individual health coverage or employment basing their decision solely on genetic predisposition to a psychiatric disorder. In the USA, the genetic information non-discrimination act was legislated in 2008, preventing such practices; unfortunately it is strictly limited to DNA, RNA and chromosomal changes (Tan 2009). This act does not cover non-genetic psychiatric biomarkers such as brain activity measurement or protein profiling. There is also the potential for misusing predictive biomarkers in reproductive health in practices such as selective abortion. As an example of ethical concerns that can arise from biomarker identification, the FDA issued a recent warning to the company, 23 and Me, which has marketed a kit to be used for personalized biomarker identification and healthcare advice. The FDA indicated the following problems in their letter: lack of reliability of the tests included, lack of test assessment, possibility for false positive/false negative results and lack of approval for use of the kit by the FDA inspections (FDA Warning Letter 2013). In December, 2013, 23 and me discontinued providing their interpretation customers’ test results, although the test itself and genetic information provided by it is still being sold. Another major issue is cost. Any new technology can increase the price of medical treatment. Diagnostic tests can be relatively expensive. None of the currently investigated biomarkers is definitively predictive for the progression of psychiatric disorders; therefore, investments in such a costly screening procedure may be considered by many not worth its price. It should also be considered, in the case of MS-based proteomics, that this is currently an expensive technique. Proteomics may be developing into a more cost effective method over time. However, it is also important to note that the diagnostic potential of proteomics needs to be considered. If the cost savings over a lifetime gained by effective treatment/prevention of a disorder are greater than an initial
costly test, then proteomics are indeed cost effective. It may be the case in some instances that proteomics is an effective method for biomarker identification, but actual clinical tests should be based on ELISA or some other less costly method, once biomarkers have been identified. Several companies are already working on first screening platforms, focusing mostly on treatment selection for people already diagnosed. Brain resources started iSpot studies, employing various diagnostic tools to predict optimized depression treatment (Williams et al. 2011). A different company, Genomind, developed the genecept assay which evaluates genetic markers from saliva such as the serotonin transporter, glutamate or dopamine to predict psychiatric treatment response and aids clinicians in selecting the right treatments for their patients initially, rather than through trial and error (Genomind 2012).
Proteomics in psychiatric biomarkers discovery When addressing extensive biological mechanisms involved in psychiatric disorders, data obtained from differential gene expression experiments are useful but incomplete. Gene expression patterns do not consistently and entirely correlate with protein expression patterns and cannot precisely identify and quantify essential functional modulators (Neale et al. 2012)—proteins, which play the central functional role in disorders, making their analysis as biomarkers appealing. Proteins can change in several ways, including by changes in protein levels, but also by changes in conformation or activity (Ideker et al. 2001; Gygi et al. 1999b). Proteomics is the study of any protein expressed by a given cell, tissue or organism at a particular time under known and determined conditions (Wilkins et al. 1996; Darie et al. 2005a, 2008a, 2011; Ngounou Wetie et al. 2012; Sokolowska et al. 2011, 2012c, d; Woods et al. 2011). It includes the modification of a protein or set of proteins by cellular mechanisms varying under the influence of certain internal and environmental factors (Darie et al. 2004, 2005a, b, 2008a; Sokolowska et al. 2011, 2012b, c; Spellman et al. 2008; Woods et al. 2012b). Proteomic approaches can identify post-translational modifications of proteins which are essentially a quick and dynamic method protein function control (Darie et al. 2004, 2005a, 2008; Sokolowska et al. 2011, 2012c, d; Woods et al. 2011, 2012a), as well as new proteins (Sokolowska et al. 2012a, d; Woods et al. 2012a; Roy et al. 2012). Therefore, qualitative and quantitative protein analysis and identification from body fluids (serum/plasma, saliva, urine, CSF) and tissues (cell lines, biopsies, and human post-mortem tissues) provide an important biomarker detection tool. Typically, these analyses are performed using mass spectrometry (MS), a critical
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tool for proteomic studies (Darie 2013; Ngounou Wetie et al. 2013a, b, c, d, e; Sokolowska et al. 2013a, b; Woods et al. 2013b). Proteomic analysis for biomarker discovery consists of several steps: (1) isolation of proteins from different tissues or body fluids (for example under pathological versus normal conditions); (2) fractionation and separation of a complex mixture of proteins; (3) analysis of separated fractions by MS and (4) use of bioinformatics’ tools and specific databases for data processing. To reduce sample complexity, fractionation and enrichment procedures are performed mostly using various chromatographic and electrophoresis techniques to separate proteins based on their characteristics such as molecular mass, isoelectric point, hydrophobicity or charge. Biochemically fractionated proteins are analyzed by a mass spectrometer (MS) which is the primary method for proteomic studies. MS and bioinformatics technologies allow identification and characterization of protein patterns differentially expressed in pathological states versus controls. Several methods for proteins profiling such as 2-dimensional gel electrophoresis (2-DE-PAGE) with subsequent LC–MS/MS identification, matrix assisted laser desorption ionization (MALDI) or fingerprinting profiles using surface enhanced laser/ desorption ionization (SELDI) have emerged recently (Lakhan 2006). MS-based techniques can provide quantitative or qualitative data. Various quantification methods, such as differential gel electrophoresis (DIGE) (Viswanathan et al. 2006), isotope-coded affinity tag (ICAT) (Gygi et al. 1999a), absolute quantitation (AQUA) or label free methods such as peak intensities or spectra counting allow accurate proteins level estimation in samples (Zhu et al. 2010). It is necessary to validate results with different protein detection methods such as ELISA or Western Blot (WB) and very often on the mRNA level (Northern Blot, Q-PCR). Although MS is a comprehensive protein measurement technique, directed protein detection methods also exist.
Current trends and putative proteomics biomarkers in psychiatric disorders Many factors including genes, proteins, their interactions and environmental influences likely contribute to the development of a mental disorder. Reliance on only one single biomarker rather than a signature set could potentially lead to misdiagnosis, particularly since many disorders likely have overlapping underlying mechanisms. Development of a signature set of biomarker for particular disorder is more realistic. One good example was described by Pies, who defined a signature set of biomarkers for schizophrenia which includes: (1) genetic marker—
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neuregulin-1 mutations increase susceptibility to schizophrenia; (2) brain activity marker—abnormal smooth-pursuit eye movement observed people with schizophrenia; (3) brain changes markers—schizophrenic individuals showed reduced anterior cingulate volumes, enlarged lateral and third ventricular volumes and white matters abnormalities (Pies 2008). This is a comprehensive signature including more than one measurement as well as measurements obtained via different methodologies. A simple search in PubMed using ‘‘biomarker’’ and the disorder reveals almost 3,500 results for depression, around 2,000 for schizophrenia but only around 250 for ADHD, primarily published in last 10 years. Increases in the search for biomarkers for psychiatric disorders are very promising but it is important to note that most published findings are initial and largely unconfirmed by additional independent studies. The human brain proteome project by the human proteome organization endeavors to assist in more effective communication and exchange of proteomic data regarding brain research (Hamacher and Meyer 2005; Stephan et al. 2005). The project’s goal is to characterize and compare data from the human and mouse brain proteome to aid in faster development of reliable and accurate biomarkers (Turck and Iris 2011). Schizophrenia biomarkers Schizophrenia is an incompletely understood psychiatric condition affecting around 2 million people in the US alone and almost 1 % of the world population (Lakhan 2006; van Os and Kapur 2009). No laboratory screening test is currently available and diagnosis is based largely on behavior, past history and duration of symptoms, both positive and negative (World Health Organization 1992; American Psychiatric Association 2000). The availability of objective and reliable markers could allow for more precise schizophrenia detection, early therapeutic intervention and could also point towards new therapeutic strategies (Martins-DeSouza 2010). Numerous genetic studies have identified specific markers or chromosomal regions associated with schizophrenia but none showed an acceptable genome-wide significance (Purcell et al. 2009; Ng et al. 2009; Glessner and Hakonarson 2009). Analysis of thalamus proteins of schizophrenia patients and control non-schizophrenia individuals (who never suffered from psychiatric or neurological disorders) revealed 50 proteins with significant differential expression (Martins-de-Souza et al. 2010c). The most common alternations were related to oligodendrocyte (myelin basic protein and myelin oligodendrocyte protein, glial fibrillary acidic protein) and energy metabolism (pyruvate, NADPH) (Martins-de-Souza et al. 2010b). Additional analysis of metabolites in CSF showed
The potential of biomarkers in psychiatry Table 1 Characteristics of a good marker, advantages and disadvantages of biomarkers Characteristics of a good biomarker
Diagnostic specificity/accuracy Reliability Reproducibility Non-invasive test needed Test limitations well characterized Development process clear and disclosed Should allow comparison with other neurological observations Clinical usefulness
characterization and understanding of the disorder (Lakhan 2006; Martins-de-Souza 2010; Lakhan and Kramer 2009; Falkai and Moller 2012). It should also be noted that brain and CSF-derived biomarkers described by this study would not necessarily meet ‘‘good’’ clinical biomarker criteria (Table 1), since these are invasive procedures, although biomarkers obtained using these procedures may be informative for research purposes and may be reflected in other more attainable bodily fluids. Major depressive disorder (biomarkers)
Technology available Test to obtain tolerated by the general target population Inexpensive Simple Easily integrated into clinical practice Comparable to animal model observations Comparable to other biomarkers Advantages/uses of proteomic biomarkers in psychiatric disorders
Objective diagnosis Supplement to behavioral assessments Potential to differentiate between disorders with similar symptoms Distinguish between disorder subtypes Monitoring treatment response Prediction of disorder progression Prediction of disorder susceptibility Aid in treatment selection/strategy Promotion of preventative treatments Indicate treatment targets Elucidate disorder etiology
Disadvantages of proteomic biomarkers in psychiatric disorders
Ethical misuse/discrimination Cost Lack of predictive value Further complicates diagnosis Increases time/labor
alternations in glutamatergic neurotransmission and cannabinoid metabolism (Vasic et al. 2012). Profiling of phosphorylation patterns (such as protein phosphorylation) in serum proteins in individuals with schizophrenia versus controls showed 72 phosphoproteins with altered phosphorylation pattern (Jaros et al. 2012). Several molecules are currently under investigation such as hypothalamic VGF (nerve growth factor inducible) expression (Busse et al. 2012) and apolipoprotein A1—apoA1 (Yang et al. 2006; Claassen et al. 2007). Apolipoprotein A2 and A4 and transferrin were also identified as differentially expressed proteins in serum from people with schizophrenia (Levin et al. 2010). Proteomic investigations in schizophrenia are yet to discover biomarker with accurate diagnostic specificity, but they have the potential to advance our better
Major depressive disorder is a serious and prevalent psychiatric problem affecting around 10 % of the world population (Saraceno 2002). When selecting antidepressant treatment, clinicians typically try a series of medication, and/ or psychotherapy and potentially other non-pharmacological medical treatments (for example, electroconvulsive therapy or transcranial magnetic stimulation). Through essentially trial and error they come up with a treatment for that individual. Biomarkers directing the optimal treatments for an individual could reduce the time it takes for an antidepressant treatment to work (Martins-De-Souza et al. 2010a; Krishnan and Nestler 2010). In genetic studies, polymorphisms in the promoter region (5HT-T) of the serotonin transporter (known as SLC6A4) increase the susceptibility to developing depression as a consequence of stressful life events, such as childhood abuse (Caspi et al. 2003). Glial marker S100B is elevated in mood disorders, and this is especially strong in depression (Schroeter et al. 2010, 2011). Most proteomic studies examining depression have focused on response to therapeutic treatment, which is currently based on increasing synaptic levels of serotonin and norepinephrine (Skolnick et al. 2009; To et al. 2005). In vivo studies using animal rat models assessed differential protein expression after exposure to stress. Identified hippocampal proteins included those playing roles in protein folding, signal transduction, neuronal plasticity and metabolism (Carboni et al. 2006). In vitro experiments assessed exposure of rat cortical neurons to long-term antidepressant (fluoxetine) treatment and identified proteins involved in neuroprotection (cyclophilin A), serotonin biosynthesis (14-3-3 protein z/delta) and axonal transport (glucose regulated protein 78 kDa, GRP78) (Cecconi et al. 2007). 2-DE-PAGE analysis of two brain regions, the frontal cortex and accumbens, showed altered expression of DPYSL2 (plays a role in neuronal development), carbonic anhydrase and ALDOC (both play role in energy metabolism) (Beasley et al. 2006; Johnston-Wilson et al. 2000). A proteomic study of hippocampus in animal models of depression showed differentially expressed proteins with a role in oxidative stress and neurogenesis and maintenance
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of neuronal processes (Mu et al. 2007; Khawaja et al. 2004). Elevated levels of peripheral inflammatory biomarkers (e.g., interleukin 6, tumor necrosis factor alpha) have been suggested to be associated with depression (Krishnadas and Cavanagh 2012). Many currently used antidepressants have an additional anti-inflammatory function (Kubera et al. 2001, 2004; Maes 2001). New potential anti-inflammatory molecules, cyclooxygenase-2 (COX-2) inhibitors, were tested in animal models and showed favorable effects in depression treatment versus placebo (Muller et al. 2011). Therefore, identification of inflammatory biomarkers such as cytokines may be possible to be applied to diagnosis and prediction of prognosis of depression as well as to monitoring treatment response (Li et al. 2011). To serve a clinical purpose, however, these depression biomarkers identified in animal models would need to be validated in humans and in accessible biomaterials. Autism spectrum disorder (ASD) biomarkers ASDs are complex disabilities characterized by problems with communication and social interaction with prevalence of 1 in 88 children in the USA (Autism and Developmental Disabilities Monitoring Network Surveillance Year 2008 Principal Investigators 2012). A recent survey of parents suggests that the incidence may be as high as 1 in 50 children (Blumberg 2013). Discovery of biomarkers for ASD is important, especially because children affected with ASDs cannot easily communicate their symptoms. Fragile X syndrome is the best-understood genetic cause of autism (Hagerman et al. 2012); however, the cause of the majority of forms of autism is unknown. New evidence reveals the association of ASD with events like oxidative stress and mitochondrial dysfunction (Junaid and Pullarkat 2001). Analysis of potential peripheral markers using MSbased methods revealed immune system alterations in cerebrospinal fluid and blood taken from people with ASD (Zimmerman et al. 2005; Molloy et al. 2006). Results were confirmed by LC–MS/MS showing apolipoprotein and complement is differentially expressed in serum from children with ASD versus controls (Woods et al. 2012b; Corbett et al. 2007). Proteomic analysis can reveal posttranslational modifications, for example increased polarity of glyoxalase 1 which was increased in the gray matter of ASD brains (Junaid et al. 2004). Serum protein profiling experiments suggested that altered protein levels in peripheral blood might represent useful biomarkers in future diagnosis (Taurines et al. 2010). HPLC-ESI-Ion Trap-MS analysis of saliva samples revealed a significant difference in protein phosphorylation in ASD patients versus unaffected controls (Castagnola et al. 2008). Correlation of genetic information, proteomics data and
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behavioral analysis will undoubtedly advance the field in finding predictive protein markers in ASD. Accessible fluids, such as blood and saliva, would be most amenable to a diagnostic test based on biomarkers, although in disorders with a central pathogenesis it is also important to consider whether peripheral fluids reflect brain protein changes. Direct comparisons of cerebrospinal fluid, blood and saliva contents in the same individuals may help to address this. According to one study, hyperphosphorylated tau, a brain marker of Alzheimer’s disease, was detected in saliva of individuals with Alzheimer’s disease although beta-amyloid protein (another marker) was not. This study illustrates that bodily fluids can reflect central nervous system changes, although not in all instances (Shi et al. 2011). ADHD biomarkers ADHD affects 4–12 % of school-age children (Brown et al. 2001). It is characterized by inattention and/hyperactive impulsivity, either alone or in combination (Brown et al. 2001). Children diagnosed with ADHD represent a heterogeneous group with a high variation in their symptoms. The diagnostic process is still somewhat unstructured and can be relatively easily biased (Berger 2011). A metaanalysis of genetic information showed several implicated genes, although none of them can be used as a true predictive marker (Gizer et al. 2009; Faraone and Mick 2010). Several genetic studies identified the main contributing factors as prefrontal dopamine deficiency and central dopaminergic dysfunction, but changes in oxidative metabolism and immunity were also suggested (Ceylan et al. 2012; Swanson et al. 2007). Polymorphisms in the dopamine transporter and D4 receptor were also suggested as biomarkers (Froehlich et al. 2010; McGough 2012). Proteomic approaches have been explored in the search for ADHD biomarkers. Urinary serotonin levels have been studied in ADHD using proteomic studies involving LC–MS/MS (Moriarty et al. 2011, 2012). A case studies series spanning 40 years revealed significant heterogeneity of size effects across studies for several potential biomarkers and confirmed association between studies for neuropeptide Y (NPY), dehydroepiandrosterone (DHEA) (Scassellati et al. 2012). Additionally, free cholesterol, HDL and ApoA1 were found to be higher in children with ADHD versus controls (Spahis et al. 2008). While the search for biomarkers has improved knowledge about the molecular biology and pathology of ADHD, further information is needed. The current state of ADHD biomarker identification has been recently summarized in a comprehensive review article. Unfortunately, reliable ADHD biomarkers still remain unidentified (Thome et al. 2012).
The potential of biomarkers in psychiatry
Discussion The study of putative psychiatric biomarkers is very complex because of the heterogeneous nature of psychiatric disorders which are often referred to as syndromes with several subtypes, not uniform problems. Therefore, a single biomarker is very unlikely to provide enough information to identify cellular and metabolic pathways involved in a particular individual. Identification of a signature set of biomarkers for disorder subtypes each based on their underlying biological pathways will be the most effective for diagnosis and treatment selection. It may become more powerful to predict the course and outcome of these disorders. As new neuroscience and proteomic discoveries have started to revolutionize the science of psychiatric disorders, biomarkers have come to be quite possible diagnostic tools in the near future. We are yet to see them being used in routine screening tests. Psychiatric disorders are very often overlapping creating greater challenge in accurate and specific diagnosis. Even though progress in proteomics technologies is encouraging, those approaches are still in development with drawbacks to overcome. Generally, any given tissue or body fluid would have millions of proteins expected but using current methods only a small fraction can be detected. Insoluble membraneassociated proteins (such as receptor, ion channels, G proteins) or very hydrophobic, acidic, basic and small proteins are very often omitted in traditional 2DE-PAGE experiments. Moreover, low-abundance of many essential proteins and small amount of available material (especially brain tissue) create further limitations. Therefore, those difficulties have to be addressed by development of more efficient sample prefractionation and separation methods prior to mass spectrometry analysis. Additional characteristics of protein biomarkers, such as expression levels, post-translational modifications, protein interactions, lead to better understanding of nervous system physiology under normal and pathological conditions and greater insight into the pathophysiology of psychiatric disorders (Taurines et al. 2011). Considering the high occurrence and important effect on society of psychiatric disorders, more broad and systematic studies in human fluids and tissues from affected patients are required. These types of experiments would create a large amount of data. Subsequently, there is need for establishing of multiinstitutional collaborations for obtaining large-scale experimental data which should be a properly integrated, comprehensive analysis and public database. Cooperation between clinicians, chemists, biochemists, neuroscientist, bioinformatics and statisticians is needed. Assessment of patients’ individual protein profiles for diagnosis and monitoring of therapeutic treatment is a first step in personalized medicine in psychiatry. Together with
understanding of molecular mechanisms, signaling pathways and protein–protein interactions provided by proteomics approaches supported with genomics and metabolomics data, personalized medicine will likely facilitate development of future diagnostic and prognostic biomarkers as well as novel therapeutic drugs. Acknowledgments This work was supported in part by Clarkson University (start-up grant to CCD), private donations (Mary Stewart Joyce, Ken Sandler, Bob Matloff and the SciFund Challenge Donors), the Redcay Foundation (SUNY Plattsburgh), the Alexander von Humboldt Foundation, the University of Rostock, Germany and by the U.S. Army research office through the Defense University Research Instrumentation Program (DURIP grant #W911NF-11-10304). The authors declare that they have no competing and/or financial interests.
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