Protein expression profiling of postmortem brain in schizophrenia

Protein Biomarkers in Serum of Patients with Schizophrenia

This study was devised to identify potential biomarkers of schizophrenia (SP) using proteomics techniques. We obtained 44 serum specimens from patients with SP, 26 specimens from patients with depression, and 40 specimens from healthy controls. Immobilized metal affinity capture protein chips (IMAC30) and surface-enhanced laser desorption-ionization time-of-flight mass spectrometry were used to isolate and obtain mass spectrometric data of differentially expressed serum proteins. The sequences of the peaks discrepant among the study groups were obtained using matrix-assisted laser desorption/ionization mass spectrometry and proteins identified using Mascot database. In the SP group, there were 91 protein peaks that were different from other study groups at the p value of <0.05 and 54 peaks different at the p value of <0.01. Two protein peaks at the mass-to-charge ratio of 1,207.41 and 1,466.78 were markedly different among the study groups, with the lowest expression in specimens from patients with SP. The amino acid sequences were, respectively, Glu-Gly-Asp-Phe-Leu-Ala-Glu-Gly-Gly-Gly-Val-Arg (EGDFLAEGGGVR) and Asp-Ser-Gly-Glu-Gly-Asp-Phe-Leu-Ala-Glu-Gly-Gly-Gly-Val-Arg (DSGEGDFLAEGGGVR). These proteins were identified as the N-terminal fragments of fibrinogen. In conclusion, these biomarker proteins may be useful for molecular diagnosis of SP.

Proteomics Research in Schizophrenia

Despite intense scientific efforts, the neuropathology and pathophysiology of schizophrenia are poorly understood. Proteomic studies, by testing large numbers of proteins for associations with disease, may contribute to the understanding of the molecular mechanisms of schizophrenia. They may also indicate the types and locations of cells most likely to harbor pathological alterations. Investigations using proteomic approaches have already provided much information on quantitative and qualitative protein patterns in postmortem brain tissue, peripheral tissues and body fluids. Different proteomic technologies such as 2-D PAGE, 2-D DIGE, SELDI-TOF, shotgun proteomics with label-based (ICAT), and label-free (MS^E) quantification have been applied to the study of schizophrenia for the past 15 years. This review summarizes the results, mostly from brain but also from other tissues and bodily fluids, of proteomics studies in schizophrenia. Emphasis is given to proteomics platforms, varying sources of material, proposed candidate biomarkers emerging from comparative proteomics studies, and the specificity of the putative markers in terms of other mental illnesses. We also compare proteins altered in schizophrenia with reports of protein or mRNA sequences that are relatively enriched in specific cell types. While proteomic studies of schizophrenia find abnormalities in the expression of many proteins that are not cell type-specific, there appears to be a disproportionate representation of proteins whose synthesis and localization are highly enriched in one or more brain cell type compared with other types of brain cells. Two of the three proteins most commonly altered in schizophrenia are aldolase C and glial fibrillary acidic protein, astrocytic proteins with entirely different functions, but the studies are approximately evenly divided with regard to the direction of the differences and the concordance or discordance between the two proteins. Alterations of common myelin-associated proteins were also frequently observed, and in four studies that identified alterations in at least two, all differences were downwards in schizophrenia, consistent with earlier studies examining RNA or targeting myelin-associated proteins.

APOA-I: a possible novel biomarker for metabolic side effects in first episode schizophrenia

The purpose of this study was to investigate the change in plasma protein expression in first episode schizophrenia after an 8-week treatment with risperidone, and to explore potential biomarkers for metabolic side effects associated with risperidone treatment. Eighty first-episode schizophrenia patientswere enrolled in the study. Fifteen of the 80 patients were randomly selected to undergo proteomic analysis. Plasma proteins were obtained before and after the 8-week risperidone treatment, and measured using two-dimensional gel electrophoresis (2-DE), Matrix-Assisted Laser Desorption/Ionization Time of Flight Mass Spectrometry(MALDI-TOF/TOF) and peptide mass fingerprinting.Proteins with the highest fold changes after risperidone treatment were then measured for all 80 patients using enzyme linked immunosorbent assay (ELISA). The relationship between changes in plasma protein levels and changes in metabolic parameters after risperidone treatment was examined. In 15 randomly selected patients, approximately 1,500 protein spots were detected in each gel by 2-DE. Of those proteins, 22 spots showed significant difference in abundance after risperidone treatment (p's<0.05). After MALDI-TOF peptide mass fingerprinting, apolipoprotein A-I (APOA-I) and Guanine Nucleotide Binding Protein, Alpha Stimulating (GNAS), were found to have the highest fold changes.The content of APOA-I was significantly increased, and the content of GNAS was significantly decreased after risperidone treatment (p's<0.05). The analysis in the entire study sample showed similar findings in changes of APOA-I and GNAS after risperidone treatment. Further analysis showed significant relationships between changesin APOA-1 and changes in triglyceride, total cholesterol, and body mass index after controlling for age, gender and family history of diabetes. Similar analysis showed a trend positive relationship between changes in GNAS and changes in BMI. Using proteomic analysis, the study suggested that APOA-I might be a novel biomarkers related to metabolic side effects in first episode schizophrenia treated with risperidone.

CNS proteomes in alcohol and drug abuse and dependence

Drugs of abuse, including alcohol, can induce dependency formation and/or brain damage in brain regions important for cognition. 'High-throughput' approaches, such as cDNA microarray and proteomics, allow the analysis of global expression profiles of genes and proteins. These technologies have recently been applied to human brain tissue from patients with psychiatric illnesses, including substance abuse/dependence and appropriate animal models to help understand the causes and secondary effects of these complex disorders. Although these types of studies have been limited in number and by proteomics techniques that are still in their infancy, several interesting hypotheses have been proposed. Focusing on CNS proteomics, we aim to review and update current knowledge in this rapidly advancing area.

Dissecting the Syndrome of Schizophrenia: Progress toward Clinically Useful Biomarkers

The search for clinically useful biomarkers has been one of the holy grails of schizophrenia research. This paper will outline the evolving notion of biomarkers and then outline outcomes from a variety of biomarkers discovery strategies. In particular, the impact of high-throughput screening technologies on biomarker discovery will be highlighted and how new or improved technologies may allow the discovery of either diagnostic biomarkers for schizophrenia or biomarkers that will be useful in determining appropriate treatments for people with the disorder. History tells those involved in biomarker research that the discovery and validation of useful biomarkers is a long process and current progress must always be viewed in that light. However, the approval of the first biomarker screen with some value in predicting responsiveness to antipsychotic drugs suggests that biomarkers can be identified and that these biomarkers that will be useful in diagnosing and treating people with schizophrenia.

Proteomic research in psychiatry

Psychiatric disorders such as Alzheimer's disease, schizophrenia and mood disorders are severe and disabling conditions of largely unknown origin and poorly understood pathophysiology. An accurate diagnosis and treatment of these disorders is often complicated by their aetiological and clinical heterogeneity. In recent years proteomic technologies based on mass spectrometry have been increasingly used, especially in the search for diagnostic and prognostic biomarkers in neuropsychiatric disorders. Proteomics enable an automated high-throughput protein determination revealing expression levels, post-translational modifications and complex protein-interaction networks. In contrast to other methods such as molecular genetics, proteomics provide the opportunity to determine modifications at the protein level thereby possibly being more closely related to pathophysiological processes underlying the clinical phenomenology of specific psychiatric conditions. In this article we review the theoretical background of proteomics and its most commonly utilized techniques. Furthermore the current impact of proteomic research on diverse psychiatric diseases, such as Alzheimer's disease, schizophrenia, mood and anxiety disorders, drug abuse and autism, is discussed. Proteomic methods are expected to gain crucial significance in psychiatric research and neuropharmacology over the coming decade.

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry combined with magnetic beads for detecting serum protein biomarkers in parkinson's disease

BACKGROUND

Biomarkers for neurodegenerative diseases are essential to facilitate disease diagnosis. Application of proteomics has greatly hastened the search for novel biomarkers. In this study, new potential biomarkers were discovered, and a diagnostic pattern was established for idiopathic Parkinson's disease (PD) by using proteomic technology.

METHODS

Serum proteins from PD patients and controls were captured by magnetic bead-based weak cation exchange. The molecular weight of the proteins in bead-binding fraction was detected by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). Biomarker Wizard 3.1 and Biomarker Patterns Software were used for data analysis and constructing a model of biomarkers. A blinded testing set was used to validate the model.

RESULTS

A total of 17 discriminating m/z peaks related to PD were identified. The model based on the 5 biomarkers generated an excellent separation between PD and healthy controls with 98.36% for the sensitivity and 83.05% for the specificity. Blind test data demonstrated the model could recognize patients with PD with a sensitivity of 85.0% and a specificity of 70.0%.

CONCLUSIONS

The preliminary data suggested a potential application of MALDI-TOF-MS combined with magnetic beads. The model comprising 5 promising biomarkers can differentiate individuals with PD and the healthy subjects precisely.

Identify biomarkers of neuropsychiatric systemic lupus erythematosus by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry combined with weak cation magnetic beads

OBJECTIVE

To identify proteomic biomarkers in cerebrospinal fluid (CSF) and develop a diagnostic proteomic model for neuropsychiatric systemic lupus erythematosus (NPSLE).

METHODS

CSF proteomic spectra were generated by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) combined with weak cation exchange (WCX) magnetic beads. The spectra were taken from 27 patients with NPSLE before and after treatment, and 27 controls including 17 patients with scoliosis and 10 patients with SLE but without neuropsychiatric manifestation. Discriminating peaks were processed by Biomarker Patterns Software to build a decision tree model for NPSLE classification. In addition, CSF samples of 12 patients with NPSLE, 12 patients with lumbar disc herniation, and 9 patients with other neurological conditions were used as a blind test group to verify the accuracy of the model.

RESULTS

Twelve discriminating mass-to-charge (m/z) peaks were identified between NPSLE and controls: m/z peaks 7740, 11962, 8065, 7661, 6637, 5978, 11384, 11744, 8595, 10848, 7170, and 5806. The diagnostic decision tree model, built with a panel of m/z peaks 8595, 7170, 7661, 7740, and 5806, recognized NPSLE with both sensitivity and specificity of 92.6%, based on training group samples, and sensitivity and specificity of 91.7% and 85.7%, respectively, based on the blind test group. In addition, the root node m/z peak 8595 protein, which was downregulated in the CSF of patients with NPSLE after treatment, was identified and confirmed as ubiquitin by immunoprecipitation and ELISA.

CONCLUSION

Potential CSF biomarkers for NPSLE are identified by MALDI-TOF-MS combined with WCX magnetic beads. The novel diagnostic proteomic model with m/z peaks 8595, 7170, 7661, 7740, and 5806 is highly sensitive and relatively specific for NPSLE diagnosis. The level of ubiquitin in CSF is a promising biomarker for active NPSLE.

Effects of post-mortem intervals on regional brain protein profiles in rats using SELDI-TOF-MS analysis

Identification of disease-associated proteins is critical for elucidating CNS disease mechanisms and elaborating novel treatment strategies. It requires post-mortem tissue analysis which can be significantly affected by the collection process, post-mortem intervals (PMIs), and storage conditions. To assess the effect of time and storage conditions on brain protein stability, SELDI-TOF-MS protein profiles were assessed in rat frontal cortex, caudate-putamen, hippocampus and medulla samples collected after various PMIs (0, 6, 12, 24, 48, and 72 h) at 4 °C or at room temperature (RT) storage. Regions of interest were isolated from cryosections (tissue apposition, TA), or micropunched from cryosections apposed on filter paper (paper apposition, PA), and applied onto an NP20 ProteinChip array. Protein alterations, while greater at RT than at 4 °C, were detected at 6h then differentially evolved in the various brain regions, with greater alterations in the caudate-putamen (60%) and the cortex (48%). Overall, our sensitive analytical method allowed unveiling of different patterns of protein susceptibility to PMI and to storage temperature in the various brain regions. Some protein peaks were altered in all brain regions and may potentially serve as markers of the PMI status of the brain, or for reference values when studying new proteins. Changes in disease-related proteins within post-mortem samples can be greatly affected by PMI and storage conditions, particularly when studying fragile and/or low abundant protein/peptides in tissues sampled from the caudate-putamen and neocortex.

Specific serum protein biomarkers of rheumatoid arthritis detected by MALDI-TOF-MS combined with magnetic beads

OBJECTIVES

To identify novel serum protein biomarkers and establish diagnostic pattern for rheumatoid arthritis (RA) by using proteomic technology.

METHODS

Serum proteomic spectra were generated by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) combined with weak cationic exchange magnetic beads. A training set of spectra, derived from analyzing sera from 22 patients with RA, 26 patients with other autoimmune diseases and 25 age- and sex-matched healthy volunteers, was used to train and develop a decision tree model with a machine learning algorithm called decision boosting. A blinded testing set, including 21 patients with RA, 24 patients with other autoimmune diseases and 25 healthy people, was used to examine the accuracy of the model.

RESULTS

A decision tree model was established, consisting of four potential protein biomarkers whose m/z values were 4966.88, 5065.3, 5636.97 and 7766.87, respectively. In validation test, the decision tree model could differentiate RA from other autoimmune diseases and healthy people with the sensitivity of 85.71% and specificity of 87.76%, respectively.

CONCLUSIONS

The present data suggested that MALDI-TOF-MS combined with magnetic beads could screen and identify some novel serum protein biomarkers related to RA. The proteomic pattern based on the four candidate biomarkers is of value for laboratory diagnosis of RA.

MALDI-TOF MS combined with magnetic beads for detecting serum protein biomarkers and establishment of boosting decision tree model for diagnosis of systemic lupus erythematosus

OBJECTIVES

To discover novel potential biomarkers and establish a diagnostic pattern for SLE by using proteomic technology.

METHODS

Serum proteomic spectra were generated by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) combined with weak cationic exchange magnetic beads. A training set of spectra, derived from analysing sera from 32 patients with SLE, 43 patients with other autoimmune diseases and 43 age- and sex-matched healthy volunteers, was used to train and develop a decision tree model with a machine learning algorithm called decision boosting. A blinded testing set, including 32 patients with SLE, 42 patients with other autoimmune diseases and 40 healthy people, was used to determine the accuracy of the model.

RESULTS

The diagnostic pattern with a panel of four potential protein biomarkers of mass-to-charge (m/z) ratio 4070.09, 7770.45, 28 045.1 and 3376.02 could accurately recognize 25 of 32 patients with SLE, 36 of 42 patients with other autoimmune diseases and 36 of 40 healthy people.

CONCLUSIONS

The preliminary data suggested a potential application of MALDI-TOF MS combined with magnetic beads as an effective technology to profile serum proteome, and with pattern analysis, a diagnostic model comprising four potential biomarkers was indicated to differentiate individuals with SLE from RA, SS, SSc and healthy controls rapidly and precisely.

A comparison of the synaptic proteome in human chronic schizophrenia and rat ketamine psychosis suggest that prohibitin is involved in the synaptic pathology of schizophrenia

Many studies in recent years suggest that schizophrenia is a synaptic disease that crucially involves a hypofunction of N-methyl-D-aspartate receptor-mediated signaling. However, at present it is unclear how these pathological processes are reflected in the protein content of the synapse. We have employed two-dimensional gel electrophoresis in conjunction with mass spectrometry to characterize and compare the synaptic proteomes of the human left dorsolateral prefrontal cortex in chronic schizophrenia and of the cerebral cortex of rats treated subchronically with ketamine. We found consistent changes in the synaptic proteomes of human schizophrenics and in rats with induced ketamine psychosis compared to controls. However, commonly regulated proteins between both groups were very limited and only prohibitin was found upregulated in both chronic schizophrenia and the rat ketamine model. Prohibitin, however, could be a new potential marker for the synaptic pathology of schizophrenia and might be causally involved in the disease process.

Screening for specific biomarkers in serum for diagnosis of primary biliary cirrhosis using proteomic fingerprint technology

AIM: To screen for the potential protein biomarkers in serum for the diagnosis of primary biliary cirrhosis (PBC) using proteomic fingerprint technology.

METHODS: Proteomic fingerprint technology combining magnetic beads with MALDI-TOF-MS was used to profile and compare the serum proteins from 44 patients with PBC, 32 patients with other hepatic diseases and 43 healthy blood donors. Proteomic patterns associated with PBC were identified by Biomarker Patterns Software. Model of biomarkers was constructed and evaluated using the Biomarker Patterns Software.

RESULTS: A total of 69 discriminating m/z peaks were identified that were related to PBC (P < 0.05). The model of biomarkers constructed by the Biomarker Patterns Software based on the four biomarkers (3445, 4260, 8133 and 16290) generated excellent separation between the PBC and control groups. The sensitivity was 93.3% and the specificity was 95.1%. Blind test data indicated a sensitivity of 92.9% and a specificity of 82.4%.

CONCLUSION: Biomarkers for PBC can be discovered in serum by MALDI-TOF-MS combining the use of magnetic beads. The pattern of combined markers provides a powerful and reliable diagnostic method for PBC with a high sensitivity and specificity.

Schizophrenia proteomics: biomarkers on the path to laboratory medicine?

Over two million Americans are afflicted with schizophrenia, a debilitating mental health disorder with a unique symptomatic and epidemiological profile. Genomics studies have hinted towards candidate schizophrenia susceptibility chromosomal loci and genes. Modern proteomic tools, particularly mass spectrometry and expression scanning, aim to identify both pathogenic-revealing and diagnostically significant biomarkers. Only a few studies on basic proteomics have been conducted for psychiatric disorders relative to the plethora of cancer specific experiments. One such proteomic utility enables the discovery of proteins and biological marker fingerprinting profiling techniques (SELDI-TOF-MS), and then subjects them to tandem mass spectrometric fragmentation and de novo protein sequencing (MALDI-TOF/TOF-MS) for the accurate identification and characterization of the proteins. Such utilities can explain the pathogenesis of neuro-psychiatric disease, provide more objective testing methods, and further demonstrate a biological basis to mental illness. Although clinical proteomics in schizophrenia have yet to reveal a biomarker with diagnostic specificity, methods that better characterize the disorder using endophenotypes can advance findings. Schizophrenia biomarkers could potentially revolutionize its psychopharmacology, changing it into a more hypothesis and genomic/proteomic-driven science.