Proteomics for diagnostic and therapeutic blood biomarker discovery in schizophrenia and other psychotic disorders

Chapter 25

Proteomics for diagnostic and therapeutic blood biomarker discovery in schizophrenia and other psychotic disorders David R. Cottera, Sophie Sabherwala and Klaus Oliver Schubertb,c a

Psychiatry, RCSI, Dublin, Ireland, b Discipline of Psychiatry, University of Adelaide, Adelaide, SA, Australia, c Northern Adelaide Mental

Health Services, Northern Adelaide Local Health Network, Lyell McEwin Hospital, Elizabeth Vale, SA, Australia

1 Proteomics for complex psychiatric disorders Proteomics is defined as the large-scale qualitative and quantitative study of proteomes. Proteomic investigations offer unique biological insights. The expression and function of proteins can be modulated at the DNA transcription stage, or posttranslational-modification stage. In addition, many different proteins can arise as the products of a single gene by alternative splicing and alternative posttranslational modifications of gene transcripts. Neither of these modifications can be accurately predicted by data on nucleic acids, as produced by genomic investigations. In psychiatric disorders such as schizophrenia, where no known Mendelian variants have been identified, but instead variations of many genes confer subtle biological effects in addition to gene-environment interactions and purely environmental factors, the potential value of proteomics is therefore evident. Here, the functional end products of genetic and environmental variation can be directly assessed, and proteomic technologies are now capable of characterizing and identifying posttranslational modifications, protein-protein interactions, and protein turnover.

2 Proteomics and personalized psychiatry Psychiatric diagnoses are based on clinical symptoms and biological investigations, including neuroimaging investigations and blood tests, which have their main value in terms of out-ruling organic causes for symptoms. These biological tests do not currently contribute in a positive manner to diagnosis within most clinical situations. This is both a clear weakness and an opportunity. As in all medicine, in psychiatry, early identification and treatment are associated with better outcomes. This has become increasingly clear in psychosis research over the past decade, with good evidence that early identification and treatment of subjects with psychiatric disorders, both psychotic and affective, significantly improves their clinical outcome (Larsen et al., 2011). Consequently, over the past decade, there has been a shift in research focus from first onset with psychosis to the so-called “at risk mental state” (ARMS) (Rutigliano et al., 2016) with the aim of identifying vulnerable subjects and offering early treatment to prevent psychosis (Amminger et al., 2010; Clark et al., 2016). However, only 16%–35% UHR subjects go on to convert to psychosis (Cannon et al., 2016; Fusar-Poli et al., 2012), with 50%–65% of these subsequently experiencing nonpsychotic mental disorders, such as depression and anxiety (Kelleher et al., 2012; Rutigliano et al., 2016). Consequently, there is now an increasing focus, not just on the vulnerability to psychotic disorder represented by psychotic experiences (PEs), but on help-seeking, functional impairment (Yung & Lin, 2016), and vulnerability to major psychiatric disorders generally (Rutigliano et al., 2016). The identification of an early biological fingerprint of all of these psychiatric illnesses is theoretically possible, and proteomic methods can contribute to that (Oresˇi
c et al., 2011). Specifically, plasma proteomic methods have been applied to age 12 plasma samples and identified a pattern of altered complement proteins among subjects who went on to develop psychotic disorders at age 18 (English et al., 2018). Biological markers of other clinically important measures should become the focus of the next generation of translational studies. Chief among these, perhaps, is the need to identify biomarkers of clinical outcomes following antipsychotic Personalized Psychiatry. https://doi.org/10.1016/B978-0-12-813176-3.00025-0 Copyright © 2020 Elsevier Inc. All rights reserved.

307

308 Personalized psychiatry

drug treatment. While few studies have addressed this issue, there is preliminary evidence from a single mass spectrometrybased proteomic investigation for elevated expression of complement pathway proteins among first episode psychosis subjects who had a poor outcomes following amisulpride treatment (Focking et al., 2018). While preliminary, the study demonstrates the power of discovery proteomic methods for personalized psychiatry. Similar methods should now be used to identify proteomic biomarkers of cognitive outcome and treatment response among subjects with, for example, predominantly positive or predominantly negative symptom schizophrenia. In affective disorder, differential-in-gel electrophoresis (DIGE) was used to identify protein changes associated with electro convulsive therapy (ECT), changes that are potentially relevant as biomarkers of prediction of treatment response to ECT and psychotic depression (Glaviano et al., 2014). Biomarkers identified through proteomic and other methods such as neuroimaging, neuropsychology (Cannon et al., 2016), and more recently, lipidomic (O’Gorman et al., 2017) studies, will likely have improved accuracy, and therefore, clinical value. These biomarkers, when added to other clinical tools reflecting symptom profiles, will hopefully provide a shift toward a precision medicine in psychiatry that has real and tangible impacts on the patients attending new standard clinical services (Fond et al., 2015; Leucht et al., 2015). The following sections provide in depth outlines of the proteomic methods generally used, and a summary of their main findings to date.

3

Mass spectrometry workflows

Mass spectrometry is a high-throughput proteomic technique that works by fragmenting or ionizing peptides, and by sorting these ions by mass-to-charge ratio. Mass spectrometry workflows can be broadly distinguished into “bottom-up” “topdown,” and “discovery,” or “targeted” experiments. “Bottom-up” refers to the reconstruction of protein information from individually identified peptides or fragments, whereas “top-down” approaches are capable of identifying and quantifying intact proteins. “Bottom-up” is currently the more widely used approach for the analysis of multiple proteins in complex samples (for example, blood, postmortem brain, or cerebrospinal fluid), due to its superior sensitivity. Therefore, we will focus on “bottom-up” techniques here. The quantification of proteins in a comparative experiment using a bottom-up approach can be based on “labeled” or “label-free” peptides. Each method has strengths and weaknesses, but most biomarker investigations employ label-free approaches, because they are not limited by the number of labels, and can facilitate a larger number of samples being run in the experiment. This is important, as sample size and statistical power are key factors considered in the design of clinical biomarker investigations. Discovery proteomic methods are optimized for achieving maximum coverage of the proteome by limiting the number of samples analyzed. In contrast, targeted proteomics prioritize sensitivity and throughput by limiting proteome coverage.

4

Mass spectrometry subtypes

Mass spectrometry technology is rapidly advancing in line with the introduction of cutting-edge machinery. There has been increasing variation in the types of acquisition, ionization (for example, electrospray ionization (ESI) and matrix-assisted laser desorption/ionization (MALDI)), mass selection (time-of-flight (TOF) Quadrupole and Orbitrap), and detection used based on our expanding knowledge of the proteome. The introduction of tandem mass spectrometry (MS/MS) that involves multiple stages of mass spectrometry with fragmentation of precursor ions (MS1) to highly specific fragment ions (MS2) has led to significant improvements in the rate and reproducibility of protein identification and quantification. MS/MS can be performed on a specific tandem machine, and also on a single mass analyzer over longer time periods.

5

Acquisition methods for proteomic data

In choosing a proteomic acquisition method, the most important factors include sensitivity, reproducibility, and throughput. Different acquisition methods are capable of discovery and/or targeted analyses in that they are optimal for the analysis of a range of numbers of proteins in complex samples. Currently, data-dependent acquisition (DDA), selected-reaction monitoring (SRM), and data-independent acquisition (DIA) are the most commonly used methods. DDA, which stochastically fragments peptides based on ion abundance, is currently considered the gold standard for discovery experiments due to its ability to quantify a random subset of the entire proteome. Conversely, in SRM, the gold standard for targeted proteomics, only a small subset of the proteome with prespecified targets is selectively quantified, but measurements are highly sensitive and reproducible. Data-independent acquisition (DIA) experiments allow sensitive and reproducible quantitation (almost of SRM quality) on all proteins within a large range of the proteome (specified to incorporate the most proteins possible from the sample). DIA, first developed about a decade ago, has been gaining much attention in recent years with the newer generation mass spectrometers and the introduction of variations of the technique, such as sequential window acquisition of all

Biomarker discovery in schizophrenia Chapter

25

309

Proteomics

“Bottomup”

Common MS techniques/configurations

LC-MS/MS

“Top-down”

MALDI-TOF MS

or “Shotgun”

SELDI-TOFMS

Acquisition type Discovery

Targeted

SWATH

Acquisition method

DDA

DIA

SRM

DIA MSX or MSe

FIG. 1 Understanding mass spectrometry subtypes in relation to one another. MS, mass spectrometry; LC, liquid chromatography; MALDI-TOF, matrixassisted laser desorption/ionization time of flight; SELDI-TOF, surface-enhanced laser desorption/ionization time-of flight; DDA, data-dependent acquisition; DIA, data-independent acquisition; SRM, selected reaction monitoring/parallel reaction monitoring; SWATH, sequential window acquisition of all theoretical mass spectra.

theoretical mass spectra (SWATH), multiplexed mass spectrometry (MSX), and MSe (Martins-de-Souza, Fac¸a, & Gozzo, 2017). For discovery experiments, DIA has the ability to overcome limitations in reproducibility associated with DDA as a result of its more comprehensive sampling method, and is also more effective in quantifying low abundance proteins due to its nonbiased fragmenting procedure. On the other hand, there are modest sacrifices in sensitivity in DIA experiments compared with DDA due to the increased instrument time required to progress through the isolation windows. To a degree, this shortcoming can be compensated for by the increased signal-to-noise ratio in MS2 spectra, which is the level at which DIA quantifies (Chapman, Goodlett, & Masselon, 2014). As a targeted approach, DIA is inferior to SRM in terms of specificity and sensitivity. However, it overcomes the need for peptide scheduling in advance of running a targeted experiment. Fig. 1 visualizes mass spectrometry subtypes in relation to one another. In addition to improvements in data acquisition, advanced sample preparation techniques have been developed in tandem with enhancements in mass-spectrometry technology. These include the depletion of high-abundance proteins in order to improve the dynamic range (Echan, Tang, Ali-Khan, Lee, & Speicher, 2005), as well as advanced purification, enrichment, and fractionation techniques, in both brain and blood.

6 Multiplex immunoassays Antibody-based platforms have been recently developed using microsphere technology to provide researchers with reproducible, quantitative multiplex immunoassay data for hundreds of proteins, from relatively small quantities of blood plasma or serum. Furthermore, the development of biomarker discovery databases has made biomarker discovery, as opposed to purely hypothesis-driven protein quantitation, a feasible objective of immunoassay experiments (Sabherwal, English, F€ ocking, Cagney, & Cotter, 2016). The issue of antibody availability, and thus the lack of comprehensive proteome coverage, which has long been a significant limitation of any antibody-based biomarker studies, may eventually be overcome with the introduction of antibody-based platforms such as The Human Protein Atlas (www.proteinatlas.org), which contains expression and localization profiles for 86% of the predicted human genome (Sabherwal et al., 2016). Forty-four different human tissues, and annotation data for 83 different cell types are included. The ultimate goal is to continue to extend this analysis to the majority of human proteins (Uhlen, 2005).

310 Personalized psychiatry

In addition to “semidiscovery” experiments, multiplex immunoassays can be used as a validation technique for massspectrometry results. For example, MSe was previously used to identify a serum biomarker panel capable of distinguishing first-onset drug-naive schizophrenia subjects from healthy subjects. This panel was then validated by multiplex immunoassay to create the first commercially available blood-based laboratory test for schizophrenia (Schwarz et al., 2010).

7

A comparison between mass spectrometry and multiplex immunoassay

The two approaches currently driving high-throughput biomarker discovery, multiplex immunoassay and mass spectrometry, often give rise to different findings, due to their unique properties. Here, we will briefly discuss their relative strengths and weaknesses for biomarker discovery. The analytical specificity of mass spectrometry and its ability to discern one molecule from another is considered as “gold-standard.” For most molecules, diagnostic ions that can unequivocally identify the analyte can be isolated. For example, vitamin D is measured on immunoassay platforms using a binding assay that does not discriminate between vitamin D2 (ergocalciferol) and vitamin D3 (cholecalciferol), whereas mass spectrometry can discriminate between the two forms by their different molecular masses (Maunsell, Wright, & Rainbow, 2005). On the other hand, the reproducibility of mass spectrometry is inferior to that of antibody-based platforms, including multiplex immunoassays. This may reflect both the natural variation in chromatography, and the presence of missing data that arise as a consequence of the DDA sampling procedure in particular. However, it is also a consequence of the true discovery nature of the approach, as opposed to the use of specific antibodies, and also of analytical specificity, which results in the identification of multiple isoforms of the same protein (Sabherwal et al., 2016). In general, immunoassays allow greater sensitivity of detection than mass spectrometry. On the other hand, SRM type acquisition is now of comparable sensitivity to immunoassays (Doerr, 2010), and immunoassays can be difficult to scale up. Another factor to consider is that immunoassays are limited in terms of antibody availability, and as a result, are also economically dependent on predefined platforms. Overall, immunoassays are less costly than mass spectrometry approaches in relation to instruments and training.

8

Data analysis and bioinformatics

The creation and optimization of data analysis techniques capable of accurately analyzing the volume of data produced by various mass-spectrometry acquisition methods is ongoing. Software packages used in the analysis of DDA proteomics generally use a set of algorithms for peak detection and scoring of peptides, perform mass calibration and database searches for protein identification, quantify identified proteins, and provide summary statistics. Protein quantification can be performed via label-free quantification (LFQ) or intensity-based absolute quantification (iBAQ), and statistical analysis is based on MS1 level intensities (Cox & Mann, 2008). A commonly used approach for DIA data analysis involves targeted quantification, which employs a spectral library generated by DDA for matching peptides (as chromatogram peaks) and extracting their intensities (as an area under the curve). There is also software available capable of nonspectral library-based quantitation. DIA data can be quantified at the MS1 or MS2 level. DIA derived data, acquired by targeted or nontargeted extraction, poses significant challenges to data analysis. For example, MS2 level DIA data may contain fragments that are shared across multiple co-eluting precursor ions within the same isolation window, creating a difficult problem for quantification. Furthermore, fragment maps will not necessarily be reproducible across multiple runs if the chromatographic elution patterns are distorted by factors such as pressure and temperature changes in the column, or fragment ion interference. Therefore a reliable set of fragments has to be selected carefully before the statistical analysis is performed, which is possible in some bioinformatics software packages that allow the visualization and processing of data before statistical analysis (Egertson, MacLean, Johnson, Xuan, & MacCoss, 2015; Teo et al., 2015).

9

Functional analysis of proteomic data

Pathway analysis software is currently widely used in high-throughput biomarker studies to identify biological pathways of interest from a list of molecular candidates. Examples include ingenuity pathway analysis (IPA), Kyoto encyclopedia of genes and genomes (KEGG), and STRING. Something to consider when carrying out a biomarker study is that the knowledge base of pathway analysis tools is rudimentary. This is particularly true of protein data, as new protein functions and interactions are being discovered at an extremely fast rate. However, as the number and type of functional annotations increase in parallel with technological advances, the utility of pathway analysis and confidence in results is likely to improve.

Biomarker discovery in schizophrenia Chapter

25

311

10 Proteomics for blood biomarker discovery in psychiatry For the discovery phase of biomarker development, methods such as DDA and SRM are more established (especially in terms of data analysis) and widely accepted within the proteomic community. They do, however, have some significant limitations, some of which can be overcome by DIA approaches (Sajic, Liu, & Aebersold, 2015). There are also currently limitations in validation methods for proteomic experiments; most being comparatively low-throughput, and often costly; for example, enzyme-linked immunosorbent assay (ELISA). DIA approaches have the ability to validate data-dependent discovery proteomic findings in a high-throughput manner, similar to targeted approaches such as SRM (English, Wynne, Cagney, & Cotter, 2014). Therefore, further development of the DIA workflow, for both discovery and targeted experiments, and a surge in its use in biomarker development, is expected. The increasing use of targeted mass spectrometry instead of antibody-based approaches (for example, SRM, which can detect low abundance compounds with comparable sensitivity) is also foreseen. In this way, the exceptional analytical specificity of mass spectrometry, mentioned previously, could be better utilized. The future of these approaches may also be integrated along with other biomarker discovery approaches in order to provide more accurate and personalized markers of psychotic disorders. In fact, work has already begun on incorporating clinical, socio-environmental, molecular, neuroimaging, and neurophysiological findings for Alzheimer’s disease, depressive disorder, and schizophrenia in order to identify a particular multifactorial signature specific to each (Burton, 2011; Kemp, Gordon, John Rush, & Williams, 2008; Kennedy et al., 2012; Shah et al., 2012).

10.1 The search for schizophrenia biomarkers: An example of psychiatric biomarker discovery There would undoubtedly be value in identifying biological markers of schizophrenia. The clinical outcomes in schizophrenia, a psychiatric disorder affecting up to 1% of the population worldwide, significantly improves in patients who are identified and treated early in their course of illness (Larsen et al., 2011). Symptoms generally emerge in midadolescence, but criteria for full disorder are not usually met until late adolescence or adulthood. This progression provides researchers with the opportunity to search for predictive biomarkers in addition to diagnostic markers. Studies involving first-episode and nondrug treated schizophrenia are therefore extremely valuable to biomarker research, in order to allow for more accurate diagnosis, earliest possible intervention and to potentially provide an insight into pathophysiology. The search for blood biomarkers for brain disease has become increasingly popular in light of recent clinical trials for blood-based biomarkers of the neurodegenerative disorder Alzheimer’s disease, and significant studies in Parkinson’s disease (Alberio et al., 2012; Hampel et al., 2011). This is of significance to psychiatry research as patient blood is an easily accessible biological sample. In addition, patient blood can be obtained at any stage during the course of illness, unlike cerebrospinal fluid (CSF), for example. Strong evidence now supports an association between systemic abnormalities and schizophrenia pathology, which is also driving the search for peripheral, blood-based biomarkers for the disorder. Such studies have implicated processes such as inflammation, stress response signaling, innate/adaptive immune signaling, and energy metabolism (English et al., 2018; Maes et al., 1997; Marques-Deak, Cizza, & Sternberg, 2005; Upthegrove, Manzanares-Teson, & Barnes, 2014). In a recent review, drug-free schizophrenia blood biomarker findings found by mass-spectrometry and multiplex immunoassay were collated (Sabherwal et al., 2016) (Table 1). Pathway analysis software was then used to identify top pathways implicated in past studies. These included immune system pathways (communication between innate and adaptive immune cells, hepatic stellate cell activation, atherosclerosis signaling), lipid and glucose metabolism pathways (LXR/RXR activation, FXR/RXR activation and atherosclerosis signaling), blood formation and clotting pathways (hematopoiesis of multi/pluripotent cells and coagulation respectively), and the stress response pathways (glucocorticoid receptor signaling). A potential limitation of biomarker studies in psychosis is the uncertainty as to whether the findings reflect causality or are epiphenomena. Many studies in schizophrenia attempt to overcome this by identifying and matching cases and controls for potential confounding factors, or adjusting for confounding variables post-hoc. The presence of disorders such as diabetes mellitus, hyperlipidemia, hypertension, cardiovascular or immune diseases, and other neuropsychiatric disorders or substance abuse issues in experimental subjects could significantly influence findings in schizophrenia studies. Of interest, a recent lipidomic study identified significant alteration in lipids, particularly lysophosphatidylcholines (LPCs) in the age 12 blood of subjects who were then well, and later developed psychotic disorder at age 18 (O’Gorman et al., 2017). These findings, along with those of English et al. (2018) who identified complement protein changes in these same subjects at age 12, suggest that a biomarker’s fingerprint relevant to psychotic disorder is apparent early, even before formal illness. However, the studies do not, in themselves, inform causality, and whether the lipidomic or the protein changes are causal. Future studies will be able to go back to earlier samples and attempt to determine which changes arise first.

312 Personalized psychiatry

TABLE 1 A total of 47 biomarker candidates were identified in two or more studies, in the same direction, by mass-spectrometry (MS)-based and/or more multiplexed immunoassay (MIA) methods. Protein name |gene name_accession number (or KEGG identifier)

Method

DOC

Reference

1. Apolipoprotein A1jAPOA1_P02647

MIA

"

Schwarz et al. (2011)

MIA

###

Chan et al. (2015), Domenici et al. (2010), and Schwarz et al. (2012)

#

Yang et al. (2006)

e

#

Jaros et al. (2012)

e

LC-MS

#

Levin et al. (2010)

LC-MS/MS

"

Li et al. (2012)

MALDI-TOF-MS

"

Yang et al. (2006)

MIA

"""

Chan et al. (2015), Ramsey et al. (2013), and Schwarz et al. (2012)

MIA

""""

Chan et al. (2015), Perkins et al. (2015), Ramsey et al. (2013), and Schwarz et al. (2012)

MIA

#

Domenici et al. (2010)

4. Carcinoembryonic antigenjCEACAM5_P06731

MIA

""""

Chan et al. (2015), Domenici et al. (2010), Ramsey et al. (2013), and Schwarz et al. (2012)

5. Chromogranin A jCHGA_P10645

MIA

""""

Chan et al. (2015), Guest et al. (2011), Ramsey et al. (2013), and Schwarz et al. (2011)

6. EotaxinjCCL11_P51671

MIA

""""

Chan et al. (2015), Domenici et al. (2010), Ramsey et al. (2013), and Schwarz et al. (2012)

7. Interleukin-8jIL-8_P10145

MIA

""""

Chan et al. (2015), Domenici et al. (2010), Perkins et al. (2015), and Ramsey et al. (2013)

8. Pancreatic polypeptidejPPY_P01298

MIA

""""

Chan et al. (2015), Guest et al. (2011), Ramsey et al. (2013), and Schwarz et al. (2012)

9. Thyroxine binding globulinj SERPINA7_P05543

MIA

""""

Domenici et al. (2010), Perkins et al. (2015), Ramsey et al. (2013), and Schwarz et al. (2011)

10. Alpha-1 antitrypsin jSERPINA1_P01009

MIA

"""

Domenici et al. (2010), Ramsey et al. (2013), and Schwarz et al. (2012)

MALDI-TOF-MS

"

Yang et al. (2006)

MIA

""

Domenici et al. (2010) and Perkins et al. (2015)

###

Schwarz et al. (2012), Ramsey et al. (2013), and Chan et al. (2015)

MALDI-TOF-MS LC-MS

2. Haptoglobin jHP_P00738

3. Alpha-2 macroglobulinjA2M_P01023

11. Coagulation Factor VIIjF7_P08709

12. Epidermal growth factorjEGF_P01133

MIA

"##

Domenici et al. (2010), Ramsey et al. (2013), and Schwarz et al. (2012)

13. Follicle stimulating hormonejFSHR_P23945

MIA

"""

Chan et al. (2015), Ramsey et al. (2013), and Schwarz et al. (2012)

14. Growth hormonejGH1_P01241

MIA

""

Cheng et al. (2010) and Perkins et al. (2015)

#

Domenici et al. (2010)

15. Insulin-like growth factor-binding protein 2jIGFBP2_P18065

MIA

"""

Chan et al. (2015), Ramsey et al. (2013), and Schwarz et al. (2012)

16. Interleukin-15jIL-15_P40933

MIA

"""

Domenici et al. (2010), Perkins et al. (2015), and Ramsey et al. (2013)

Biomarker discovery in schizophrenia Chapter

25

313

TABLE 1 A total of 47 biomarker candidates were identified in two or more studies, in the same direction, by mass-spectrometry (MS)-based and/or more multiplexed immunoassay (MIA) methods.—cont’d Protein name | gene name_accession number (or KEGG identifier)

Method

DOC

Reference

17. LeptinjLEP_P41159

MIA

"

Domenici et al. (2010)

###

Chan et al. (2015), Cheng et al. (2010), and Ramsey et al. (2013)

18. Macrophage migration inhibitory factorjMIF_P01033

MIA

"""

Chan et al. (2015), Ramsey et al. (2013), and Schwarz et al. (2012)

19. RANTESjCCL5_P13501

MIA

"""

Domenici et al. (2010), Ramsey et al. (2013),and Schwarz et al. (2012)

20. ResistinjRETN_Q9HD89

MIA

###

Cheng et al. (2010), Harris et al. (2012), and Ramsey et al. (2013)

21. Serum amyloid PjAPCS_P02743

MIA

"""

Domenici et al. (2010), Ramsey et al. (2013), and Schwarz et al. (2011)

22. von Willebrand factorjVWF_P04275

MIA

"""

Chan et al. (2015), Domenici et al. (2010), and Perkins et al. (2015)

23. Angiotensin converting enzymejACE_P12821

MIA

##

Chan et al. (2015) and Ramsey et al. (2013)

24. Apolipoprotein A2jAPOA2_P02652

LC-MS

#

Levin et al. (2010)

LC-MS

#

Jaros et al. (2012)

25. Apolipoprotein H/beta-2glycoprotein jAPOH_P02749

MIA

""

Chan et al. (2015) and Domenici et al. (2010)

26. CD40 ligandjCD40LG_P29965

MIA

##

Ramsey et al. (2013) and Schwarz et al. (2012)

27. Complement C3jC3_P01024

MIA

""

Domenici et al. (2010) and Ramsey et al. (2013)

28. Complement factor BjCFB_P00751

MALDI-TOF-MS

"

Yang et al. (2006)

LC-MS

"

Jaros et al. (2012)

29. Connective tissue growth factorjCTGF_P29279

MIA

""

Schwarz et al. (2011) and Schwarz et al. (2012)

30. FibrinogenjFGA_P02671

MALDI-TOF/TOF-MS

"

Zhou et al. (2013)

MIA

"

Domenici et al. (2010)

31. Glutathione-Stransferase jGST_P09211

MIA

""

Domenici et al. (2010) and Schwarz et al. (2012)

32. Granulocyte macrophage colony stimulating factorjGMCS_P04141

MIA

##

Domenici et al. (2010) and Schwarz et al. (2012)

33. Immunoglobulin AjIgA_P01876

MIA

##

Chan et al. (2015) and Domenici et al. (2010)

34. InsulinjINS_P01308

MIA

"""

Domenici et al. (2010) and Guest et al. (2011)

35. Interleukin-10jIL-10_P22301

MIA

""

Ramsey et al. (2013) and Chan et al. (2015)

##

Domenici et al. (2010) and Schwarz et al. (2012)

""

Schwarz et al. (2012) and Ramsey et al. (2013)

#

Cheng et al. (2010)

36. Luteinizing hormonejLH_P01229

e

e

MIA

37. Macrophage-derived chemokinejMDC_O00626

MIA

""

Domenici et al. (2010) and Ramsey et al. (2013)

38. ProlactinjPRL_P01236

MIA

""

Guest et al. (2011) and Perkins et al. (2015)

39. Prostatic acid phosphatise jPAP_P15309

MIA

"

Domenici et al. (2010)

##

Schwarz et al. (2012) and Ramsey et al. (2013) Continued

314 Personalized psychiatry

TABLE 1 A total of 47 biomarker candidates were identified in two or more studies, in the same direction, by mass-spectrometry (MS)-based and/or more multiplexed immunoassay (MIA) methods.—cont’d Protein name |gene name_accession number (or KEGG identifier)

Method

DOC

Reference

40. Receptor for advanced glycosylation end productsjRAGE_Q15109

MIA

##

Chan et al. (2015) and Ramsey et al. (2013)

41. Serum glutamic oxaloacetic transaminase jSGOT_P00505

MIA

""

Chan et al. (2015) and Schwarz et al. (2012)

#

Schwarz et al. (2011)

42. SortilinjSORT1_Q99523

MIA

##

Ramsey et al. (2013) and Schwarz et al. (2012)

43. Stem cell factorjKITLG_P21583

MIA

"##

Chan et al. (2015), Domenici et al. (2010), and Schwarz et al. (2012)

44. Tenascin-CjTC_P24821

MIA

""

Chan et al. (2015) and Ramsey et al. (2013)

45. ThrombopoeitinjTHPO_P40225

MIA

""

Domenici et al. (2010) and Perkins et al. (2015)

#

Schwarz et al. (2012)

46. Tissue inhibitor of metalloproteinases 1jTIMP1_P01033

MIA

""

Domenici et al. (2010) and Ramsey et al. (2013)

47. Tumor necrosis factor receptor-like 2jTNFR2_Q92956

MIA

""

Domenici et al. (2010) and Ramsey et al. (2013)

All proteins were found to be significantly differentially expressed in the plasma or serum of drug-free schizophrenia (SCZ) subjects. "/# indicates the direction of change for each compound, per study (arrows are in order of references given). The compounds are arranged in order of number of replications, from highest to lowest. Protein names are listed with corresponding gene name and Uniprot accession number. Adapted from Sabherwal, S., English, J. A., F€ ocking, M., Cagney, G., & Cotter, D. R. (2016). Blood biomarker discovery in drug-free schizophrenia: The contribution of proteomics and multiplex immunoassays. Expert Review of Proteomics, doi:10.1080/14789450.2016.1252262.

There are relatively few proteomic studies conducted in first-onset drug-naive schizophrenia subjects. This is a result of the low recruitment rates of these patients (approximately 10–30 per year), ultimately leading to longer duration studies and longer storage time for samples that could potentially influence biomarker stability. This issue of recruitment is representative of psychiatry biomarker studies generally. Another factor to consider with regard to recruiting is whether the subjects are representative of the population you wish to study as a whole (for example, that first-onset drug-naive schizophrenia is not representative of the whole population of subjects who go on to develop schizophrenia). It is important to note that the establishment of biomarkers may not only be of value in the diagnosis and prediction of schizophrenia, but also in the assessment of treatment response, long-term outcome, and clinical phenotypes.

11

Conclusion

In conclusion, despite the fact that high-throughput blood biomarker research in schizophrenia is still in its infancy, studies are beginning to yield consistent and valuable findings. These valuable findings provide an opportunity for the early identification and treatment of these debilitating disorders, and thus lead to better patient outcomes. This is a good example of the evolving role of proteomics in psychiatry. The rapidly advancing mass spectrometry and multiplex immunoassay technologies will no doubt lead to a more accurate, sensitive, reproducible, and comprehensive biological signature across a range of psychiatric disorders. The work will impact, however, not just early diagnosis, but should also be clearly designed to address issues of treatment outcome generally. For example, truly significant clinical impact will be derived from an awareness of protein biomarkers of good response to specific drugs. Further, different clinical phenotypes may respond differentially to treatments. An example of this may be the proposed inflammatory subtype of schizophrenia (Fillman, Sinclair, Fung, Webster, & Shannon Weickert, 2014) and the possibility that an antiinflammatory therapy agent may be more effective among such a subgroup. This is unproven, but is an example of where the next generation of studies may be directed (Fond et al., 2015; Leucht et al., 2015). Finally, it is likely that the best biomarker signatures will be derived not from one modality, but from the use of data from multiple modalities, such that the most discriminatory

Biomarker discovery in schizophrenia Chapter

25

315

neuropsychological, neuroimaging, proteomic, and lipidic measures are combined into a single risk measure. This has been done to good effect for risk prediction in the at-risk mental state (Cannon et al., 2016; Clark et al., 2016). Future studies will need to refine this further in terms of transition from an at-risk mental state to psychosis, and to extend to “risk calculators” of treatment response and outcomes of different clinical phenotypes.

References Alberio, T., Pippione, A. C., Zibetti, M., Olgiati, S., Cecconi, D., Comi, C., … Fasano, M. (2012). Discovery and verification of panels of T-lymphocyte proteins as biomarkers of Parkinson’s disease. Scientific Reports, 2, 953. https://doi.org/10.1038/srep00953. Amminger, G. P., Sch€afer, M. R., Papageorgiou, K., Klier, C. M., Cotton, S. M., Harrigan, S. M., … Berger, G. E. (2010). Long-chain ω-3 fatty acids for indicated prevention of psychotic disorders. Archives of General Psychiatry, 67(2), 146. https://doi.org/10.1001/archgenpsychiatry.2009.192. Burton, A. (2011). Big science for a big problem: ADNI enters its second phase. The Lancet Neurology. https://doi.org/10.1016/S1474-4422(11)70031-X. Cannon, T. D., Yu, C., Addington, J., Bearden, C. E., Cadenhead, K. S., Cornblatt, B. A., … Kattan, M. W. (2016). An individualized risk calculator for research in prodromal psychosis. American Journal of Psychiatry, 173(10), 980–988. https://doi.org/10.1176/appi.ajp.2016.15070890. Chan, M., Krebs, M.-O., Cox, D., Guest, P., Yolken, R., Rahmoune, H., … Bahn, S. (2015). Development of a blood-based molecular biomarker test for identification of schizophrenia before disease onset. Translational Psychiatry, 5. https://doi.org/10.1038/tp.2015.91. Chapman, J. D., Goodlett, D. R., & Masselon, C. D. (2014). Multiplexed and data-independent tandem mass spectrometry for global proteome profiling. Mass Spectrometry Reviews, 33(6), 452–470. https://doi.org/10.1002/mas.21400. Cheng, T. M. K., Lu, Y.-E., Guest, P. C., Rahmoune, H., Harris, L. W., Wang, L., … Bahn, S. (2010). Identification of targeted analyte clusters for studies of schizophrenia. Molecular & Cellular Proteomics, 9(3), 510–522. https://doi.org/10.1074/mcp.M900372-MCP200. Clark, S. R., Baune, B. T., Schubert, K. O., Lavoie, S., Smesny, S., Rice, S. M., … Amminger, G. P. (2016). Prediction of transition from ultra-high risk to first-episode psychosis using a probabilistic model combining history, clinical assessment and fatty-acid biomarkers. Translational Psychiatry, 6(9), e897. https://doi.org/10.1038/tp.2016.170. Cox, J., & Mann, M. (2008). MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nature Biotechnology, 26(12), 1367–1372. https://doi.org/10.1038/nbt.1511. Doerr, A. (2010). Targeted proteomics. Nature Methods, 7(1), 34. https://doi.org/10.1038/nmeTh.F.284. Domenici, E., Wille, D. R., Tozzi, F., Prokopenko, I., Miller, S., McKeown, A., … Muglia, P. (2010). Plasma protein biomarkers for depression and schizophrenia by multi analyte profiling of case-control collections. PLoS One, 5(2). https://doi.org/10.1371/journal.pone.0009166. Echan, L. A., Tang, H. Y., Ali-Khan, N., Lee, K., & Speicher, D. W. (2005). Depletion of multiple high-abundance proteins improves protein profiling capacities of human serum and plasma. Proteomics, 5(13), 3292–3303. https://doi.org/10.1002/pmic.200401228. Egertson, J. D., MacLean, B., Johnson, R., Xuan, Y., & MacCoss, M. J. (2015). Multiplexed peptide analysis using data-independent acquisition and skyline. Nature Protocols, 10(6), 887–903. https://doi.org/10.1038/nprot.2015-055. English, J. A., Lopez, L. M., O’Gorman, A., F€ocking, M., Hryniewiecka, M., Scaife, C., … Cotter, D. R. (2018). Blood-based protein changes in childhood are associated with increased risk for later psychotic disorder: Evidence from a nested case–control study of the ALSPAC longitudinal birth cohort. Schizophrenia Bulletin, 44(2), 297–306. https://doi.org/10.1093/schbul/sbx075. English, J. A., Wynne, K., Cagney, G., & Cotter, D. R. (2014). Targeted proteomics for validation of biomarkers in early psychosis. Biological Psychiatry, 76(6), e7–e9. https://doi.org/10.1016/j.biopsych.2013.11.016. Fillman, S. G., Sinclair, D., Fung, S. J., Webster, M. J., & Shannon Weickert, C. (2014). Markers of inflammation and stress distinguish subsets of individuals with schizophrenia and bipolar disorder. Translational Psychiatry, 4(2). https://doi.org/10.1038/tp.2014.8. Focking, M., Pollack, T., Dicker, P., Cagney, G., Winter, I., Kahn, R., … Cotter, D. (2018). Proteomic analysis of blood based samples from the OPTiMiSE (optimization of treatment and Managment of schizophrenia in Europe) study point towards complement pathway protein changes. Schizophrenia Bulletin, 44(suppl_1), S74–S75. Fond, G., D’Albis, M. A., Jamain, S., Tamouza, R., Arango, C., Fleischhacker, W. W., … Leboyer, M. (2015). The promise of biological markers for treatment response in first-episode psychosis: A systematic review. Schizophrenia Bulletin. https://doi.org/10.1093/schbul/sbv002. Fusar-Poli, P., Bonoldi, I., Yung, A. R., Borgwardt, S., Kempton, M. J., Valmaggia, L., … McGuire, P. (2012). Predicting psychosis. Archives of General Psychiatry, 69(3), 220. https://doi.org/10.1001/archgenpsychiatry.2011.1472. Glaviano, A., O’Donovan, S. M., Ryan, K., O’Mara, S., Dunn, M. J., & McLoughlin, D. M. (2014). Acute phase plasma proteins are altered by electroconvulsive stimulation. Journal of Psychopharmacology, 28(12), 1125–1134. https://doi.org/10.1177/0269881114552742. Guest, P. C., Schwarz, E., Krishnamurthy, D., Harris, L. W., Leweke, F. M., Rothermundt, M., … Bahn, S. (2011). Altered levels of circulating insulin and other neuroendocrine hormones associated with the onset of schizophrenia. Psychoneuroendocrinology, 36(7), 1092–1096. https://doi.org/10.1016/j. psyneuen.2010.12.018. Hampel, H., Wilcock, G., Andrieu, S., Aisen, P., Blennow, K., Broich, K., … Vellas, B. (2011). Biomarkers for Alzheimer’s disease therapeutic trials. Progress in Neurobiology, 95(4), 579–593. https://doi.org/10.1016/j.pneurobio.2010.11.005. Harris, L. W., Pietsch, S., Cheng, T. M. K., Schwarz, E., Guest, P. C., Bahn, S., & Hashimoto, K. (2012). Comparison of peripheral and central schizophrenia biomarker profiles. PLoS One, 7(10). https://doi.org/10.1371/journal.pone.0046368. Jaros, J. A. J., Martins-De-Souza, D., Rahmoune, H., Rothermundt, M., Leweke, F. M., Guest, P. C., & Bahn, S. (2012). Protein phosphorylation patterns in serum from schizophrenia patients and healthy controls. Journal of Proteomics, 76, 43–55. https://doi.org/10.1016/j.jprot.2012.05.027.

316 Personalized psychiatry

Kelleher, I., Connor, D., Clarke, M. C., Devlin, N., Harley, M., & Cannon, M. (2012). Prevalence of psychotic symptoms in childhood and adolescence: A systematic review and meta-analysis of population-based studies. Psychological Medicine, 42(09), 1857–1863. https://doi.org/10.1017/ S0033291711002960. Kemp, A. H., Gordon, E., John Rush, A., & Williams, L. M. (2008). Improving the prediction of treatment response in depression: Integration of clinical, cognitive, psychophysiological, neuroimaging, and genetic measures. CNS Spectrums, 13(12), 1066–1086. quiz 1087-1088. https://doi.org/10.1017/ S1092852900017120. Kennedy, S. H., Downar, J., Evans, K. R., Feilotter, H., Lam, R. W., MacQueen, G. M., … Soares, C. (2012). The Canadian biomarker integration network in depression (CAN-BIND): Advances in response prediction. Current Pharmaceutical Design, 18(36), 5976–5989. https://doi.org/10.2174/ 138161212803523635. Larsen, T. K., Melle, I., Auestad, B., Haahr, U., Joa, I., Johannessen, J. O., … McGlashan, T. (2011). Early detection of psychosis: Positive effects on 5-year outcome. Psychological Medicine, 41(7), 1461–1469. https://doi.org/10.1017/S0033291710002023. Leucht, S., Winter-Van Rossum, I., Heres, S., Arango, C., Fleischhacker, W. W., Glenthøj, B., … Sommer, I. E. (2015). The optimization of treatment and management of schizophrenia in Europe (OPTiMiSE) trial: Rationale for its methodology and a review of the effectiveness of switching antipsychotics. Schizophrenia Bulletin, 41(3), 549–558. https://doi.org/10.1093/schbul/sbv019. Levin, Y., Wang, L., Schwarz, E., Koethe, D., Leweke, F. M., & Bahn, S. (2010). Global proteomic profiling reveals altered proteomic signature in schizophrenia serum. Molecular Psychiatry, 15(11), 1088–1100. https://doi.org/10.1038/mp.2009.54. Li, Y., Zhou, K., Zhang, Z., Sun, L., Yang, J., Zhang, M., … Wan, C. (2012). Label-free quantitative proteomic analysis reveals dysfunction of complement pathway in peripheral blood of schizophrenia patients: Evidence for the immune hypothesis of schizophrenia. Molecular BioSystems, 8(10), 2664–2671. https://doi.org/10.1039/c2mb25158b. Maes, M., Delange, J., Ranjan, R., Meltzer, H. Y., Desnyder, R., Cooremans, W., & Scharpe, S. (1997). Acute phase proteins in schizophrenia, mania and major depression: Modulation by psychotropic drugs. Psychiatry Research, 66(1), 1–11. https://doi.org/10.1016/S0165-1781(96)02915-0. Marques-Deak, A., Cizza, G., & Sternberg, E. (2005). Brain-immune interactions and disease susceptibility. Molecular Psychiatry, 10(3), 239–250. https://doi.org/10.1038/sj.mp.4001732. Martins-de-Souza, D., Fac¸a, V. M., & Gozzo, F. C. (2017). DIA is not a new mass spectrometry acquisition method. Proteomics, 17(7). https://doi.org/ 10.1002/pmic.201700017. Maunsell, Z., Wright, D. J., & Rainbow, S. J. (2005). Routine isotope-dilution liquid chromatography-tandem mass spectrometry assay for simultaneous measurement of the 25-hydroxy metabolites of vitamins D2 and D3. Clinical Chemistry, 51(9), 1683–1690. https://doi.org/10.1373/ clinchem.2005.052936. O’Gorman, A., Suvitaival, T., Ahonen, L., Cannon, M., Zammit, S., Lewis, G., … Cotter, D. R. (2017). Identification of a plasma signature of psychotic disorder in children and adolescents from the Avon longitudinal study of parents and children (ALSPAC) cohort. Translational Psychiatry, 7(9), e1240. https://doi.org/10.1038/tp.2017.211. Oresˇi
c, M., Tang, J., Sepp€anen-Laakso, T., Mattila, I., Saarni, S. E., Saarni, S. I., … Suvisaari, J. (2011). Metabolome in schizophrenia and other psychotic disorders: A general population-based study. Genome Medicine, 3(3). https://doi.org/10.1186/gm233. Perkins, D. O., Jeffries, C. D., Addington, J., Bearden, C. E., Cadenhead, K. S., Cannon, T. D., … Heinssen, R. (2015). Towards a psychosis risk blood diagnostic for persons experiencing high-risk symptoms: Preliminary results from the NAPLS project. Schizophrenia Bulletin, 41(2), 419–428. https:// doi.org/10.1093/schbul/sbu099. Ramsey, J. M., Schwarz, E., Guest, P. C., Van Beveren, N. J. M., Leweke, F. M., Rothermundt, M., … Bahn, S. (2013). Distinct molecular phenotypes in male and female schizophrenia patients. PLoS One, 8(11). https://doi.org/10.1371/journal.pone.0078729. Rutigliano, G., Valmaggia, L., Landi, P., Frascarelli, M., Cappucciati, M., Sear, V., … Fusar-Poli, P. (2016). Persistence or recurrence of non-psychotic comorbid mental disorders associated with 6-year poor functional outcomes in patients at ultra high risk for psychosis. Journal of Affective Disorders, 203, 101–110. https://doi.org/10.1016/J.JAD.2016.05.053. Sabherwal, S., English, J. A., F€ocking, M., Cagney, G., & Cotter, D. R. (2016). Blood biomarker discovery in drug-free schizophrenia: The contribution of proteomics and multiplex immunoassays. Expert Review of Proteomics. https://doi.org/10.1080/14789450.2016.1252262. Sajic, T., Liu, Y., & Aebersold, R. (2015). Using data-independent, high-resolution mass spectrometry in protein biomarker research: Perspectives and clinical applications. Proteomics, Clinical Applications, 9(3–4), 307–321. https://doi.org/10.1002/prca.201400117. Schwarz, E., Guest, P. C., Rahmoune, H., Harris, L. W., Wang, L., Leweke, F. M., … Bahn, S. (2012). Identification of a biological signature for schizophrenia in serum. Molecular Psychiatry, 17(5), 494–502. https://doi.org/10.1038/mp.2011.42. Schwarz, E., Guest, P. C., Rahmoune, H., Martins-de-Souza, D., Niebuhr, D. W., Weber, N. S., … Bahn, S. (2011). Identification of a blood-based biological signature in subjects with psychiatric disorders prior to clinical manifestation. World Journal of Biological Psychiatry, 2975(June 2011), 1–6. https://doi.org/10.3109/15622975.2011.599861. Schwarz, E., Izmailov, R., Spain, M., Barnes, A., Mapes, J. P., Guest, P. C., & Bahn, S. (2010). Validation of a blood-based laboratory test to aid in the confirmation of a diagnosis of schizophrenia. Biomarker Insights, 2010(5), 39–47. https://doi.org/10.4137/BMI.S4877. Shah, J., Eack, S. M., Montrose, D. M., Tandon, N., Miewald, J. M., Prasad, K. M., & Keshavan, M. S. (2012). Multivariate prediction of emerging psychosis in adolescents at high risk for schizophrenia. Schizophrenia Research, 141(2–3), 189–196. https://doi.org/10.1016/j.schres.2012.08.012. Teo, G., Kim, S., Tsou, C. C., Collins, B., Gingras, A. C., Nesvizhskii, A. I., & Choi, H. (2015). mapDIA: Preprocessing and statistical analysis of quantitative proteomics data from data independent acquisition mass spectrometry. Journal of Proteomics, 129, 108–120. https://doi.org/10.1016/j. jprot.2015.09.013. Uhlen, M. (2005). A human protein atlas for normal and cancer tissues based on antibody proteomics. Molecular & Cellular Proteomics, 4(12), 1920–1932. https://doi.org/10.1074/mcp.M500279-MCP200.

Biomarker discovery in schizophrenia Chapter

25

317

Upthegrove, R., Manzanares-Teson, N., & Barnes, N. M. (2014). Cytokine function in medication-naive first episode psychosis: A systematic review and meta-analysis. Schizophrenia Research, 155, 101–108. https://doi.org/10.1016/j.schres.2014.03.005. Yang, Y., Wan, C., Li, H., Zhu, H., La, Y., Xi, Z., … He, L. (2006). Altered levels of acute phase proteins in the plasma of patients with schizophrenia. Analytical Chemistry, 78(11), 3571–3576. https://doi.org/10.1021/ac051916x. Yung, A. R., & Lin, A. (2016). Psychotic experiences and their significance. World Psychiatry: Official Journal of the World Psychiatric Association (WPA), 15(2), 130–131. https://doi.org/10.1002/wps.20328. Zhou, N., Wang, J., Yu, Y., Shi, J., Li, X., Xu, B., & Yu, Q. (2013). Mass spectrum analysis of serum biomarker proteins from patients with schizophrenia. Biomedical Chromatography, 654–659. https://doi.org/10.1002/bmc.3084.