1. INTRODUCTION
Bipolar disorder and severe clinical depression are a group of mental diseases, which often manifest themselves with dramatic changes in mood, energy, cognition, and behaviour that fluctuate over time. Psychotic states characterized by auditory or visual hallucinations, paranoid or delusional beliefs can also be present in the acute phase. The aetiology of these disorders is multifactorial including a complex interaction between genetic predisposition, with many genes each accounting for a small effect, and environmental factors, such as developmental abnormalities, trauma, substance abuse and social factors.
Despite the abundance of research, data on neurobiological mechanisms underlying mood disorders are elusive, and diagnoses rely on clinical assessment of these dynamic symptoms mainly through interview-based methodology. Currently most used classification systems (Diagnostic and Statistical Manual of Mental Disorders 4th Edition; International Statistical Classification of Diseases and Related Health Problems 10th Revision) are not evidence-based and many diagnostic criteria overlap with many
other diseases with different aetiologies, such as neurological or organic conditions.
Not surprisingly, the disease course remains not predictable and treatment options are unspecific leaving practice mostly to clinician’s personal experience.
In order to be clinically useful, a diagnosis should be able to predict prognosis, course of illness and eventually guide treatment choice and such aims are better achieved when the classification of illnesses moves closer to the underlying biological mechanisms.
Identification of biological markers could improve the diagnosis and classification of mood disorders subtypes, as well as stratify patients into more homogeneous, clinically useful subpopulations.
1.1 Biomarkers in Psychiatry
According to the official definition by the National Institutes of Health (NIH), ‘‘a biomarker is a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacological responses to a therapeutic intervention’’ (Biomarkers Definitions Working Group et al., 2001).
The utilization of biomarkers for brain disorders is not a recent concept. In the nineteenth century, Kraepelin established a writing scale to stratify patients suffering from psychiatric disorders by measuring their writing pressure curves (Kraepelin, 1899). Due to
the phenotypic heterogeneity and the lack of quantitative measures for disease symptoms, biomarker discovery in the field of neuroscience has been confronted with considerable challenges.
This holds true especially for neuropsychiatric disorders where, despite tremendous progress in understanding brain function, the exact molecular underpinnings of mental dysfunction remain elusive. Because biomarkers can differentiate between distinct biological states, their availability is critical in clinical settings for premorbid diagnosis, patient stratification, and monitoring of disease progression and treatment.
The use of biological markers in other areas of medicine has come a long way with advances in the fields of pathology, biochemistry and most notably genetics. For example, prevalent and debilitating diseases such as heart failure can be diagnosed with high sensitivity and specificity by measurement of levels of B-natriuretic peptide (BNP). Certain types of cancer can be screened for and monitored by specific tumor markers.
The search for biological markers for psychiatric diseases has been going on for decades; however, previous experimental attempts have selected candidate biomarkers based on current models of disease pathogenesis. Due to the presumed high level of etiologic heterogeneity and the overlap of dimensions across mood disorders, standalone markers proved neither specific nor applicable. Recent technological advances made it possible to leave
hypothesis-based approaches of biomarker discovery behind in favour of non-hypothesis-driven profiling experiments.
Biomarkers may be in the form of genes, proteins and other molecules, or morphological characteristics, in any case they should relate closely to the neurobiology representing the molecular correlate of the disorder, not merely the symptom or disease consequences. Diagnostic biomarkers, depending on the information they can provide, be used as prediction tools (e.g.
subclinical markers, risk or vulnerability markers), this is of high importance in psychiatry as there is evidence that delays in diagnosis and intervention lead to poorer prognosis. Biomarkers could also be identified as diseases signatures (e.g. disease markers, stage or progression markers) (Perlis, 2011), adding information on prognosis and course of illness. Moreover, in order to be translated into clinical practice biomarkers should also be measurable and reproducible over time, non invasive, easily available, cost-effective and provide with high sensitivity and specificity for the disease (Lakhan and Kramer, 2009; Schwarz and Bahn, 2008).
1.2 Proteomics
Proteomics was a term first coined in the mid 1990s in the context of the field of “genomics” (Wilkins et al., 1996). The term proteomics was originally defined as ‘‘the study of the total set of expressed proteins by a cell, tissue or organism at a given time
under a determined condition’’ (Choudhary and Grant, 2004). Like genomics (global DNA expression) and transcriptonomics (global mRNA expression), proteomics is viewed as a high throughput hypothesis-generating research method.
Genomic analyses have provided useful insights into genes conferring susceptibility to complex neuropsychiatric diseases.
Given that multiple genetic lesions, which may additionally vary among individuals, can cause psychiatric disorders, many disease- related genes have a low penetrance and do not exhibit an effect on the phenotype in a predictable and quantifiable manner (Schwarz and Bahn, 2008). On the other hand it is increasingly evident that mRNA correlate poorly with observed biological phenotype.
Proteomic studies have many advantages over transcriptonomic or genomic studies as they focus on the protein as the ‘biological effectors’ molecule. Indeed, cell function relies on complex molecular mechanisms such as alternative promoters, alternative splicing, RNA editing and post-translational modification which produce multiple isoforms of proteins from single genes. (Godovac- Zimmermann et al., 2005). Moreover, the proteome also includes the modifications made to a particular set of proteins produced by an organism and this can vary with time and distinct requirements, stresses or other environmental factors that a cell or organism is subject to.
Therefore, proteomics profiling experiments appear to be more suitable to describe the final pathway of all complex interactions between genes and environment in the human cell.
It is not the presence of certain proteins per se that make them markers but rather their expression, the shifts in expression as well as their state. In many cases, however, it is not just one protein but a set of proteins that is indicative of a disease. Over the past decade, proteomic technologies have matured and now enable the measurements of hundreds or even thousands of proteins in one experiment. Thus, researchers can monitor the global expression of proteins and protein groups in search for disease- related differences, which can provide an insight into the aetiology of diseases and the ability to find disease-specific markers. (Gao et al., 2005).
Procedures
Most proteomic analyses are performed using a combination of two-dimensional electrophoresis (2-DE) for protein separation and mass spectrometry (MS) for protein identification. Direct comparison of 2DE profiles from different samples combined with unambiguous identification of the separated proteins by MS is the typical format followed by most researchers.
2-DE
2-dimensional electrophoresis separates proteins based on size, as
in regular electrophoresis, but also based on charge, or isoelectric point (pI). The first step is isoelectric focusing (IEF). The mixed protein sample is run on an immobilized pH gradient; the range of the gradient used depends on the expected proteins in the sample.
The sample is added to the gradient and an electric current is applied. Proteins will be positively charged at pHs below their pI and negatively charged at pH’s above their pI. When the protein is at the point in the gradient where the surrounding pH is equal to its pI, there will be no charge on the protein and it will stop moving.
Once enough time has passed for the proteins to settle in the gradient, the current is removed and the gradient is laid horizontally along an SDS-PAGE gel. An electric current is then applied and the proteins move horizontally out of the IEF gradient and into the polyacrylamide gel where they are separated based on molecular weight. This method can reproducibly separate mixtures of proteins. Once the proteins have been separated, they can be analyzed quantitatively as long as there is a reference sample. The amount of protein in cells under two conditions (e.g. aerobic and anaerobic) can be measured by staining with a fluorescent dye. The brighter the fluorescence, the more protein is present. Proteins that are expressed at different levels are then taken for further analysis and identification. Procedure is shown in Figure 1
Mass Spectrometry
Mass spectrometry (MS) is an important emerging method for the characterization of proteins. The incorporation of MS to the 2DE workflow allows high throughput and more sensitive identification of differentially expressed proteins. For this, the spots of interest can be excised from gels and the proteins digested enzymatically, most commonly using trypsin in order to produce peptides. The masses of these peptides are then measured by MS and the combination of peptide masses provides a peptide mass fingerprint specific to each protein. The identity of the protein is then determined by comparison of this experimentally determined fingerprint with fingerprints derived from protein sequences in an in silico-digested database. MS/MS instruments, such as Matrix- assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF-MS) allow the fragmentation of single peptides, thereby providing the sequence of each analyzed peptide and facilitating protein identification. MALDI-TOF MS is a relatively novel technique in which a co-precipitate of an UV-light absorbing matrix and a biomolecule is irradiated by a nanosecond laser pulse. Most of the laser energy is absorbed by the matrix, which prevents unwanted fragmentation of the biomolecule. The ionized biomolecules are accelerated in an electric field and enter the flight tube. During the flight in this tube, different molecules are separated according to their mass to charge ratio and reach the detector at different times. In this way each molecule yields a distinct signal. (Figure 2) The method is used for detection and
characterization of biomolecules, such as proteins, peptides, oligosaccharides and oligonucleotides, with molecular masses between 400 and 350,000 Da. It is a very sensitive method, which allows the detection of low (10-15 to 10-18 mole) quantities of sample
with an accuracy of 0.1 - 0.01 %.
Protein identification by this technique has the advantage of short measuring time (few minutes) and negligible sample consumption (less than 1 pmol) together with additional information on microheterogeneity (e.g. glycosylation) and presence of by- products.
1.3 Proteomics in psychiatric disorders
The first proteomics paper in psychiatric disease was published in 2000 (Johnston-Wilson et al., 2000) and a current PubMed search for the appropriate terms returns 200 relevant articles.
Biomarker studies on psychiatric disorders present peculiar hurdles such as the inherent difficulties in accessing relevant biological materials, since the main manifestations appear to be in the brain. Almost all large-scale proteome studies in psychiatry have aimed to profile protein expression differences compared to control samples in a hypothesis-free manner. Proteomic profiling for psychiatric disorders has been initially carried out in brain tissue. Studies in brain tissues, however, carry some limitations and the reasons are both methodological and theoretical. Indeed, brain samples need to be analyzed carefully to avoid post-mortem
artefacts, they are a very valuable source for the generation of hypotheses with regard to the aetiology of a disorder; however, they can only give molecular information from the time of death. One additional limitation of post-mortem research is that results cannot be exported into animal models. Sampling peripheral tissues is important for diagnostic purposes as well as for investigating treatment effect, therefore proteome of blood or cerebrospinal fluid is necessary to integrate findings between brain and periphery.
Results from the main studies on human brain tissue, cerebrospinal fluid and serum as peripheral tissue on relevant psychiatric patients’ populations are reported below.
Post mortem studies
In schizophrenia brain tissue, dysregulation of pathways associated with reactive oxygen species has been observed [Yao et al., 2001] as well as alterations in mitochondrial oxidative phosphorylation and glucose metabolism. Proteome analyses have identified differential expression of proteins involved in those processes, such as key enzymes associated with glucose metabolism including aldolase C (ALDOC), gamma enolase (ENO2), aconitase (ACO2), hexokinase (HK1), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), a number of subunits of mitochondrial ATPase and proteins associated to oxidative stress [Martins-de- Souza et al., 2012]. Oligodendrocytes are responsible for myelination of axons in the central nervous system. Reduction or
malformation of the myelin sheath can result in leakage and reduced propagation of nerve impulses. In addition, proteome analyses have confirmed this through identification of differentially expressed proteins such as Myelin basic protein (MBP), myelin oligodendrocyte glycoprotein (MOG) and 20,30-cyclic nucleotide,30-phosphodiesterase (CNP) [Martins-de-Souza et al., 2012].
Proteome analyses of human brain tissue of patients with bipolar disorder have also been performed. Dihydropyrimidinase-related protein-2 (DPYSL2) and glial fibrillary acid protein (GFAP) were found to be decreased in the frontal cortex (FC-BA10) suggestive of effects on brain development. The differential expression of several tubulin subunits suggested that cytoskeletal dysfunction may be an important component of bipolar disorder through an analysis of the anterior cingulate cortex (ACC-BA24) [Beasley et al., 2006].
One common finding in proteomic studies of distinct brain regions from bipolar disorder patients is suggestive of a dysfunction in energy metabolism [Beasley et al., 2006; Johnston-Wilson et al, 2000], more prominently in the dorsolateral prefrontal cortex (DLPFC-BA9) where half of the identified differentially expressed proteins were involved in these pathways.
At present, only two brain regions, the frontal cortex (FC) and accumbens (ACC), from depressed patients have been subjected to proteomic analyses, revealing differentially expressed proteins [Beasley et al., 2006; Johnston-Wilson et al, 2000]. Altered
expression of DPYSL2 was common to both studies, although this was downregulated in FC and upregulated in ACC. DPYSL2 plays a role in nervous system development and cell differentiation by regulating axonal guidance, neuronal growth cone collapse and cell migration, suggestive of alterations in brain development in depressed patients. The differential expression of carbonic anhydrase (CA2) and ALDOC was also common to both studies, implicating effects on energy metabolism. Both of these studies used brain tissue samples from the Stanley Neuropathology Consortium and analysed samples from major depression, schizophrenia, bipolar disorder and control subjects simultaneously.
Cerebrospinal fluid (CSF)
Mass spectrometry was employed to profile proteins and peptides in a total of 179 cerebrospinal fluid samples (58 schizophrenia patients, 16 patients with depression, five patients with obsessive- compulsive disorder, ten patients with Alzheimer disease, and 90 controls). Results showed a highly significant differential distribution of samples from healthy volunteers away from drug- naïve patients with first-onset paranoid schizophrenia. The key alterations were the up-regulation of a 40-amino acid VGF-derived peptide, the down-regulation of transthyretin at approximately 4 kDa, and a peptide cluster at approximately 6,800-7,300 Da.
(Huang et al., 2006). In a second study from the same group of researchers, a label-free LC-MS strategy was used to investigate the proteomic profile of 20 CSF samples (ten samples from healthy volunteers and ten from schizophrenia patients). A clear difference was found between healthy volunteers and schizophrenia patients.
77 proteins were detected (Huang et al., 2007).
Moreover, metabolic and proteomic profiles were investigated in CSF of Patients Prodromal for Psychosis (PPP), showing that from 36% to 29% of PPP patients displayed proteomic/metabolic specific profiles. However, the biochemical dysregulation identified in PPP patients does not yet predict clinical outcome. Apolipoprotein A1 (apoA1) was found decreased in CSF from schizophrenia patients was also found downregulated in liver and RBCs; was also significantly reduced in sera of first-onset drug-naïve schizophrenia patients using enzyme-linked immunosorbent assay and consistently downregulated in post-mortem brain tissue. In addition, studies using CSF from depressed patients have been carried out, but only in a validation context for schizophrenia findings [Huang et al., 2008]. Thus far, no systematic proteomic study has been carried out on brain tissues from depressed patients.
Sampling peripheral fluids such as CSF, although promising carries some limitations: it can be collected in small amount, it may be contaminated with proteins coming from serum for a leak in the blood brain barrier and lumbar puncture carries some risks
for the patient therefore it could not be applied to develop large scale diagnostic tests.
Serum
Serum can be obtained in relatively large quantities, it can be sampled with minimal discomfort to the patient and therefore remains one of the most suitable methods to investigate diagnostic biomarkers and findings can be easily exported and used to develop large scale tests. Moreover, there is now an increasing body of evidence pointing to a close integration between the central nervous system and immunological functions with lymphocytes playing therein a central role. Numerous studies showed similarities between receptor expression and mechanisms of transduction processes of cells in the nervous system (e.g. neurons and glia) and lymphocytes. In several neuropsychiatric disorders, alteration of metabolism and cellular functions in the CNS, as well as disturbances in the main neurotransmitter and hormonal systems are concomitant with altered function and metabolism of blood lymphocytes (Gladkevich et al., 2004). While there is yet no clear biomarker, there is increasing evidence to suggest that disease related changes can be detected outside the brain. Multiple contributing factors including growth factors and/or pro- inflammatory cytokines are dysregulated in psychotic disorders.
And also multiple endocrine (Thyroid, sex steroids, HPA axis) and
metabolic (insulin resistance) function appear altered in mood disorders.
Serum levels of the inflammatory markers sTNFR1 and sTNFR2, were found to be higher in chronic institutionalized patients with schizophrenia than in controls, however no correlation with symptom severity was found (Coelho et al., 2008). Dietrich- Muszalska and Olas (2007) showed that collagen-stimulated platelet aggregation was significantly lower in the schizophrenic patients group compared to healthy controls.
In another study the activity of the platelet antioxidative enzyme superoxide dismutase (SOD), and the levels of thiobarbituric acid reactive species (TBARS) were measured as oxidative stress indicators. Results suggested an enhanced generation of reactive oxygen species and significantly lower SOD activity in schizophrenia patients compared to healthy controls (Dietrich- Muszalska et al., 2005).
One protein corresponding to the group of alpha-defensins was able to discriminate minimally medicated patients with schizophrenia from controls in T-cells lisates. Moreover, plasma from 21 monozygotic twins discordant for schizophrenia and 8 healthy unaffected twin pairs was also analyzed for the expression of alpha-defensins by ELISA. Both affected and unaffected twins were found to have significantly elevated alpha-defensin levels compared to healthy twin pairs (Craddock RM et al., 2008).
A recent review of 185 publications described a total of 273 schizophrenia biomarkers identified in serum and/or plasma. The findings suggested an ongoing immunological and inflammatory process in schizophrenia, accompanied by altered cortisol levels which suggested activated stress response and altered hypothalamic-pituitary-adrenal axis function in these patients (Chan et al., 2011).
One study by Domenici et al. reported a proteomic investigation carried out in a large clinical population based on the profiling, through a multiplexed immunoassay, of 79 analytes belonging to pathways previously shown to be involved in the pathophysiology of either depression or schizophrenia (such as growth factors and cytokines) or previously untested. Results showed a clear increase of insulin and matrix metallo-proteinase 9 (MMP9) in depression with respect to controls, while in schizophrenia the levels of circulating growth factors such as BDNF and EGF drew a separation from controls and to a lesser extent also proteins such as chemokines and cytokines (Domenici et al, 2010). It is worth to underlie that these results were found to be independent of treatment and also that same results were obtained in other studies carried out with different methodologies (Futamura et al., 2002; Ikeda et al., 2008; Hashimoto et al., 2005; Gama et al., 2007).
Other Authors measured serum levels of BDNF, neurotrophin 3, tumor necrosis factor α, interleukin 6, interleukin 10, total reactive
antioxidant potential, thiobarbituric acid reactive substances and protein carbonyl content in patients with Bipolar disorders, controls and a group of positive controls (patients with sepsis).
Several of the markers discriminated between the bipolar and control groups, especially when patients were in acute episodes. In some cases, toxicity was as high in bipolar disorder as that seen in patients with sepsis (Kapczinski et al., 2011).
An interesting study has been carried out using serum obtained from two case-control studies of 75 patients with schizophrenia and 110 patients with bipolar disorder and their matched controls.
The samples were drawn within 1 month before estimated onset of illness. Multiplex immunoassay analyses led to identification of 20 molecules which were altered in pre-schizophrenia and 14 molecules in pre-bipolar disorder subjects compared to controls.
Only two of these molecular changes were identical in both data sets and predictive testing confirmed that the biomarker signatures for pre-schizophrenia and pre-bipolar disorder were dissimilar (Schwarz et al., 2011). The same research group carried out a multiplex molecular profiling approach to measure serum concentrations of 181 proteins and small molecules in 250 first and recent onset patients with schizophrenia, 35 patients with major depressive disorder, 32 patients with euthymic bipolar disorder, 45 patients with Asperger syndrome and 280 control subjects. Only preliminary results are available for this study showing a signature comprised of 34 analytes in a cohort of closely
matched schizophrenia patients (n=71) and control (n=59) subjects with a separation of 60-75% of schizophrenia subjects from controls. The same analysis also gave a separation of 50% of patients with major depression and 10-20% of patients with bipolar disorder and Asperger syndrome subjects from controls (Schwarz et al., Molecular Psychiatry Epub ahead of print).
A small study on 15 patients affected with bipolar disorder identified apolipoprotein A-I as a candidate serum biomarker whose levels in serum were restored with lithium therapy (Sussulini et al., 2011).
Another study focused on patients with bipolar disorder in remission phase. Proteins identified from mononuclear cells and serum using liquid chromatography-mass spectrometry (LC-MS) and a Multi-Analyte Profiling platforms revealed 60 differentially expressed molecules involved predominantly in cell death/survival pathways, compared with controls, suggesting that patients with bipolar disorder carry a peripheral fingerprint that has detrimental effects on cell function and that could be used to distinguish BD patients from healthy controls despite being in a remission phase (Herberth et al., 2011).
2. STUDY OBJECTIVES
The primary objective of the present study is to carry out a non- hypothesis-based proteomic investigation across 3 groups of subjects in order to identify potential specific disease-related biomarkers.
1) Un-medicated or minimally medicated patients with bipolar disorder experiencing acute psychotic symptoms.
2) Un-medicated or minimally medicated patients with a diagnosis of depression with no history of psychosis
3) Healthy volunteers matched for age range and sex.
Secondary objective is to correlate laboratory data and clinical data, in order to provide hypothesis on the relationship between the shift in protein expression and the observed phenomenology.
3. MATERIALS AND METHODS
3.1 Recruitment
Patients
The two groups of patients were recruited from the Day Hospital and inpatient services of the U.O. Psychiatria II, AOUP:
All subjects were given an in-house case report form (CRF), for socio-demographic assessment; record of current pharmacological treatment at the time of blood taking and basic medical information.
Inclusion criteria
1- age range between 20 and 50 years, 3- fasting at the time of blood taking.
4- Signed consent form
Exclusion criteria
1- metabolic diseases (obesity, metabolic syndrome, diabetes, hormonal disorders),
2-current fever or infectious disease,
3- current substance abuse or dependence, 4- pregnancy
5- refusal to sign informed consent
Psychopathological Assessment
A trained registered psychiatrist assessed diagnosis using the Structured Clinical Interview for DSM-IV-Patient version (SCID-I- P), according to DSM-IV criteria.
Patients were also assessed using the following rating scales:
The Positive and Negative Symptoms Scale (PANSS),
Hamilton Depression Rating Scale (HDRS-17),
Hamilton Anxiety Rating Scale (HAM-A),
Young Mania Rating Scale (YMRS).
Description of Instruments
SCID, Structured Clinical Interview for DSM IV ( American Psychiatric Association, 1994) will be used to determine the presence or absence of DSM IV Axis I disorders.
The PANSS or the Positive and Negative Syndrome Scale is a medical scale used for measuring symptom severity of patients with schizophrenia. The 30-item PANSS is an operationalized, drug-sensitive instrument that provides representation of positive symptoms, negative symptoms and general psychopathology.
Hamilton Depression rating Scale (HDRS) is the most widely used clinician-administered depression assessment scale. It contains 17 items pertaining to symptoms of depression experienced over the week prior to administration.
The Hamilton Anxiety Scale (HAS or HAMA) is a 14-item test Items score ranges from zero to four. The HAS is used to assess the severity of anxiety symptoms over the week prior to administration.
Young Man60%ia Rating Scale (YMRS) contains 11 items pertaining to symptoms of mania experienced over the days before assessment.
Healthy volunteers
Subjects were recruited among Hospital staff. All subjects were screened for absence of metabolic diseases (obesity, metabolic syndrome, diabetes, hormonal disorders), current fever or infectious disease and pregnancy. All subjects signed an informed consent to the study.
Ethical considerations:
The Study has been approved by the local ethical committee of the AOPU and University of Pisa. (n^ 23853)
3.2 Laboratory
Sample preparation
From all subjects, a peripheral venous blood sample of 25ml was obtained and placed in EDTA tubes. The blood samples were immediately processed to obtain lymphocytes fraction as described: blood was centrifuged for 15 min at 300g to obtain a platelet-rich plasma (PRP) and a pellet containing red cells, platelets, granulocytes, lymphocytes (P1). Platelets were precipitated from PRP by centrifugation at 30,000g for 10 min to obtain platelet-free plasma. P1 was diluted 1:1 with Emagel (Behring AG, Marburg, Germany) and centrifuged for 30 min at 550g at room temperature over a density gradient of Lymphoprep (Nycomed, Oslo, Norway). A lympho-monocyte ring has been thus obtained at the interface between the plasma and the Lymphoprep.
The granulocytes formed a pellet on the bottom of the test tube.
Lympho-monocytes were harvested and washed in phosphate- buffered saline (PBS) at 300g for 15 min. The resulting pellet was diluted with 1 ml PBS, stratified on 7 ml of platelet-free plasma and centrifuged at 60g for 15 min. Lymphocytes were washed twice in platelet-free plasma to obtain a sample completely separated from platelets. In order to eliminate any possible erythrocyte contamination, the granulocyte pellet was treated with hemolyzing
solution and washed twice at 600g for 15 min. The granulocytes formed a pellet on the bottom of the container.
The pellets of cells and plasma were marked with a unique patient number, stocked, and stored at -80°C freezer located at laboratory of Department of Psychiatry, via Bonanno 6.
Lymphocytes have been used for the proteomic analysis using two different but complementary technologies: two dimensional gel electrophoresis (2-DE) combined with mass spectrometry (MALDI- TOF-TOF).
2-DE analysis
200 μg of proteins, for each sample, were filled up to 450 µl in rehydration solution (supplemented with 1 % (v/v) pharmalytes, pH 3-10 NL). 2-DE was carried out by using 18 cm Immobiline Dry-Strips (GE Healthcare) with a non linear, pH 3-10 gradient (IPG strips). IPG strips were rehydrate with sample overnight at room temperature using the Immobiline DryStrip Reswelling tray.
Isoelectrofocusing (IEF) was performed at 16°C on an Ettan IPGphor II apparatus (Amersham Biosciences) placing strips on the Ettan IPGphor Cup Loading Manifold. To prepare the IPG strips for the second dimension, the strips were first equilibrated 15 min at room temperature in a buffer containing 50 mM Tris-HCl, pH 8.8, 6 M Urea, 30% glycerol, 2% SDS, 0.002% bromophenol blue, 1%
DTT, followed by a second equilibration for 10 min in the same buffer except that DTT was replaced by 2.5% IAA. Subsequently, the IPG strips were applied horizontally on top of 12.5% SDS- polyacrylamide gels (20x18x0.15 cm) and electrophoresis was performed using the PROTEAN Plus Dodeca Cell (Bio-Rad) with constant amperage (16mA/gel) at 10 °C until the dye front reached the bottom of the gel (about 15 h) applying a continuous buffer system.
At the end of the second dimension run, the gels were removed from the glass plates and soaked for 1 hour in a solution with ethanol (30%) and phosphoric acid (1%). Then the gels are stained overnight in a solution with ethanol (30%), phosphoric acid (1%) and Ruthenium II tris(bathophenanthroline disulfonate) tetrasodium (1μM). After staining, gels were washed 5 h with ethanol (30%) and phosphoric acid (1%). Images were acquired with IMAGEQUANT LAS 4000 (Ge Healthcare). These images were analyzed with Progenesis Same Spot (Nonlinear Dynamics) software. This software generates 2DE analyses which are robust and accurate. The gels were aligned to place all spots in exactly the same location, and then spot detection produced a complete data set since all gels contain the same number of spots, each matched to its corresponding spot on all gels. The software included statistical analysis calculations such as Anova p-value and False Discovery Rate (q-values). The protein spots with a 2-fold spot quantity change, p < 0.05 and q value < 0.05 were selected .
Mass Spectrometry
Identification of differentially expressed proteins were performed by MALDI-TOF/TOF mass spectrometer (Applied Biosystems, Framingham, MA, USA) as described.
Peak lists are generated with the Launch peak to MASCOT tools with the following settings: for the MS data, mass range 850– 4000, peak density of maximum 20 peaks per 100 Da, minimal S/N ratio of 15, minimal area of 250, maximum number of peak seat at 50;
for the MS/MS data, mass range 60–2000, peak density of maximum 50 peaks per 200 Da, minimal S/N ratio of 5, minimal area of 20, and maximum number of peak set at 200. Such acquired MS and MS/MS data were compared to the database using MASCOT search engine (http://www.matrixscience). In MASCOT, the combined PMF and MS/MS search were performed on uniprot_sptr_14.9-03-Mar-2009 database (selected for Homo sapiens, 87868 entries). Search settings allow one missed cleavage with the trypsin enzyme selected, one fixed modification (carboxymethylated cysteine) and a variable modification (oxidation of methionine).
Scaffold (version Scaffold_3_00_03, Proteome Software Inc., Portland, OR) were used to validate MS/MS based peptide and protein identifications. Peptide identifications are accepted if they can establish at greater than 95.0 % probability as specified by the
Peptide Prophet algorithm. Proteins that contain similar peptides and could not be differentiated based on MS/MS analysis alone will be grouped to satisfy the principles of parsimony.
Statistical analysis
Statistical calculations were performed by SPSS statistical software (Version 17.0; SPSS,Inc., USA). All data are expressed as mean ± SD.
The significance of the differences between groups (p-value <0.05) was calculated using Mann -Whitney test for non parametric variables or Chi-square Test (demographic variables) and T-test for independent samples (protein spots).
Images from protein separation and identification as well as Principal Component Analysis were performed by a dedicated software (Progenesis Same Spot). Protein probability was determined by the algorithm Protein Prophet.
4. RESULTS
4.1 Sample
Group 1- Patients with acute psychosic bipolar disorder
12 patients have been recruited in this group, 3 males and 9 females, mean age 41,5 years (SD=9,13). Mean age at illness onset was 28.08 years (SD= 11.23) and mean illness duration was 12.50 anni (SD= 9.49).
All patients were admitted to hospital acutely after a psychotic recurrence and after having discontinued their prescribed treatment. At time of blood taking they were on medication (a combination of mood stabilizers, benzodiazepines and antipsychotics) since 2 days (SD=1), with the exception of one patient who was totally unmedicated.
In terms of diagnoses, all patients belonging to this group presented with acute full blowing psychotic symptoms in the context of a bipolar disorder. Specifically, 5 patients had a diagnosis of bipolar disorder, most recent episode manic, 4 had a
diagnosis of bipolar disorder most recent episode depressive and 2 had a diagnosis of most recent episode mixed.
None of these patients had family history of psychosis, however, 8 patients out of 12 had a family history for mood disorders.
Group 2 – Patients with d epression with no hystory of psychotic symptoms
8 patients have been recruited in this group, one male and 7 females, mean age 36,5 years (SD=9,666). Mean age at onset was 27 years (SD=10,954) and mean illness duration 9,38 years (SD=7,249).
All patients included in this group had a diagosis of Major Depressive Episode, half (4 patients) had a second Axis I diagnosis of panic disorder, 3 patients had family history for mood disorders, however none of these patients had family history of psychosis.
In terms of medications, 4 patients were on a monotherapy with SSRIs, 1 patient was on a combination of SSRIs and BDZ, 1 patient on a combination of Bupropione and Gabapentin and 2 patients on a acombination of SSRIs and mood stabilizers.
Group 1 vs Group 2
Mean age, gender and age at onset and duration of illness did not differ significantly between the two groups.
A number of variables which could have possibly influenced proteomic prifiling have been checked and compared between the 2 groups.
Women in both groups did not differ in the average day of menstrual cycle and also they did not differ in terms on number of previous pregnancies.
Smoking as a dicothomous variable does not differ in the 2 groups and also the number of daily smoked cigarettes did not differ between the patients groups.
Alcohol drinking as well as units of acohol/day did not differ in the two groups.
Mean PANSS total score for group 1 was 83.5 (SD=24,4) and for group 2 was 40,8750 (SD=5,91457); mean score for the positive symptoms subscale was 26.1 (SD=5,7) for group 1 and 8,3750 (SD=3,88) for group 2; for the negative symptoms subscale mean score for group 1 was 12.5 (SD=7.98) and 7,0000 (SD=4,62910) for group 2; and general psychopathology subscale mean score was 44.75 (SD=15,85) for group 1 and 25,5 (SD=4,62) for group 2.
The two groups differ significantly in PANSS Total score (p<0.001), positive symptoms subscale (p<0.001); negative symptoms subscale (p=0.005) and general psychopathology subscale (p=0.001).
HAM-D-17 mean score for group 1 was 15,7 (SD=16,7) and 10,8750 (SD=5,54) for group 2, with no statistical difference.
Mean score for HAM-A was 12,1 (SD=6,27) for group 1 and 11,6250 (SD= 3,15) for group 2 with no statistical difference.
YMRS mean total score was 22.3 (SD=13,09) for group 1 and 1,8750 (SD= 3,18198) for group 2 reaching statistical difference (p<0.001).
Group 3 - Healthy volunteers
10 healthy volunteers were recruited in this group, 3 male and 9 females; mean age was 38,9 (SD= 12,142).
Demographic information for the groups are described in Table 1
4.2 Laboratory
In the Two-dimensional gel electrophoresis (2DE), the protein patterns of samples from patients with acute psychotic bipolar disorder were compared with respective samples of patients with depression and samples from healthy controls. The typical 2-DE gel image of protein extracts is shown in Figure 3.
In order to identify patterns in the peripheral lymphocytes’ protein profiles of the different groups, and to express the data in such a way as to highlight their similarities and differences, a mathematical procedure, the principal component analysis (PCA), was applied to the entire data of the match sets (Figure 4). PCA
underlies a clear separation between controls and patients groups, with a t1 value for the first component of 63,96% and a t2 value for the second component of 11,76%.
By computational gel image comparison, a total of 48 protein spots were found to be differentially expressed in the three groups, exhibiting ≥2 fold-change of mean value spot intensities.
T-Test for independent samples showed that 48 protein spots were found to be differentially expressed in patients compared to controls (p<0.001), with an increase in expression for most spots in patients and a decrease in expression for 6 spots (1261, 1904, 1481, 1742, 1586, 1241).
T-test also compared patients groups reporting 6 protein spots differentially expressed with statistical significance (p <0.001) in acute bipolar disorder vs depression. Spot numbers and analyses are shown in Table 3 and 4.
Figure 5, 6, 7 show values of optical density for 3 selected spots.
Proteins whose expression showed over 2-fold statistical significant spot quantity change were selected and identified.
5. DISCUSSION
The study reported here represents a proteomic investigation carried out on a clinical population of psychiatric patients diagnosed with bipolar disorder, major depression and healthy individuals, based on the profiling of peripheral serum lymphocytes extracts. Most of proteome studies reported in the literature, have been carried out on samples from patients with schizophrenia, while mood disorders samples have only been used in a validation context for schizophrenia. This study focuses entirely on mood disorders in search for distinct diagnostic or state signatures as well as common pathways. Protein expression differences were compared in an hypothesis-free manner and 2DE was used as a separation technique.
Results using PCA showed a clear separation between patients and controls. 48 protein spots were found to be differentially expressed with an over than 2 fold change. These results are in line with findings from previous studies which detected a number between 34 to 79 analytes differentially expressed in psychosis vs depression (Domenici et al., 2010; Schwarz et al., 2012).
The protein spots found to be differentially expressed, were compared between samples from patients with acute bipolar disorder and patients with major depression and statistical significance was reached for 6 of the 48 spots. Moreover, mean
values of optical density show that there is a gradient of expression of some proteins among patients groups. The limited number of significant differences among patients groups could be probably due to the small number and the large variability within the patients samples. However, it is can be hypothesized that the differences could stand for diagnostic specific signatures for the disorder or disease state and the similarities could represent common biological pathways involved in the pathophysiology of mood disorders. Protein spots are now under identification using MS techniques; the nature of the analytes will clarify the molecular underpinnings of such results. Several peripheral pathways are involved in the neurobiology of mood disorders, previous studies suggested that growth factors and inflammatory chemokines and cytokines are commonly altered across mood disorder spectrum (Kapczinski et al., 2011; Domenici et al., 2010).
Despite the separation power of the 2DE-MS-based proteomics, this approach presents some limitations such as a difficulty in detecting low abundance, acidic or basic proteins as well as proteins with extremely high or low molecular weight.
In conclusion, we found different protein patterns between patients and controls, while, we found limited differences between patients with acute psychotic bipolar disorder and patients with mild major depression. Despite, not yet confirmed by identification analysis, mood disorders may share common neurobiological pathways with distinctive features associated with the disease state.
Acknowledgements
I would like to thank Prof. Antonio Lucacchini for his supervision and support to the project. This work is the result of collaboration between the Department of Psychiatry, Neurobiology, Pharmacology and Biotechnology, University of Pisa and the AOUP.
A special thank to Dr. Laura Giusti who coordinated the lab activities. I would also like to acknowledge all the participants in the study for their collaboration. Data collection: Ginevra Orsolini, Michela Giorgi Mariani, Agnese Ciberti. Data analysis: Simona Calugi, Laura Giusti.
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