Schizophrenia is a psychiatric disorder of uncertain etiology that affects about 1% of the world's population. It is likely to be caused by a complex crosstalk between genetic, neurodevelopmental and environmental factors 1 resulting in the differential expression of several genes and proteins. The multi-factorial characteristic of schizophrenia not only hinders its biochemical understanding and characterization, but makes it difficult to establish preclinical models, which are needed urgently for testing of existing and novel therapies 2 . The current evaluation of preclinical models for psychiatric disorders is mostly based on readouts following behavioral tests which simulate particular features or symptoms of the disease. However, characteristic schizophrenia symptoms, such as hallucinations, delusions and disorganized thoughts cannot be assessed using these tests. In contrast, characterization of these models using molecular fingerprinting approaches can be translated directly between preclinical and clinical studies.

The blockade of NMDA receptors using drugs such as phencyclidine (PCP) and ketamine is widely used to mimic schizophrenia in rodents. The psychosis-inducing effects of these drugs in humans and the widespread nature of the NMDA receptor in the brain result in multiple effects which may resemble the multifactorial features of schizophrenia 3 . Previous studies have shown that PCP-treated rats present altered glucose utilization in the limbic regions of their brains 4 5 , consistent with findings from post mortem brain studies of schizophrenia patients 6 .

Several brain regions collected from schizophrenia patients post mortem have been analyzed by other large-scale profiling techniques such as transcriptomic microarrays 7 8 , and proteomic methods including two-dimensional gel electrophoresis and shotgun mass spectrometry 9 10 . These analyses have shown consistent effects on glycolysis enzymes 11 such as hexokinase 1 (Hk1), aldolase C (Aldoc), triosephosphate isomerase (Tpi1), glyceraldehyde-3-phosphate dehydrogenase (Gapdh), phosphoglycerate mutase 1 (Pgam1), phosphoglycerate kinase 1 (Pgk1) and enolase 2 (Eno2) (Figure 1 12 ). These data also support previous findings of impaired glucose handling in schizophrenia brains 13 14 15 . Although impairments in energy metabolism seem to be a common trait of psychiatric disorders 16 , disturbances in glycolysis appear to be more specific to schizophrenia 11 . On the other hand, effects on oxidative phosphorylation have been reported more frequently in brains from patients with major depressive disorder 17 . It is a requirement that preclinical models present similar traits to the disease in question. Considering the largely discovered dysregulation of glycolysis in schizophrenia brains as well as the evidence of differential glucose handling in preclinical models, the targeted analysis of glycolysis enzymes is one of the necessary steps in the molecular characterization of preclinical models of schizophrenia.

Most targeted protein expression analyses are performed using either Western blotting (WB) or enzyme-linked immunosorbent assay (ELISA) approaches. Although these methods are effective, both are dependent on antibody availability and neither are suitable for large-scale validation in their standard formats. Moreover, depending on the quality of the antibody employed, WB analysis is only considered to be a semi-quantitative method 18 . An alternative for targeted protein expression analysis is selective reaction monitoring (SRM) mass spectrometry [or multiple reaction monitoring (MRM)]. SRM is a mass spectrometry-based technique which can accurately measure the relative or absolute concentrations of selected peptides/proteins down to the attomolar range. In addition, SRM can be multiplexed, allowing the measurement of peptides from several proteins in a single experiment. Therefore, this method has the capability of measuring all components in a metabolic pathway in a single sample.

In this study, we have performed SRM mass spectrometry analysis of the seven glycolysis enzymes listed above in brain tissues of PCP-treated (1.0 and 2.5 mg/kg) and control rats. The resulting protein levels were then assessed using univariate and multivariate analyses, which showed that the acute PCP model reproduces some of the glycolytic deficits seen in schizophrenia at the molecular level.

SRM data for the seven analyzed glycolytic enzymes in rats treated acutely with PCP showed significant differences in the levels of Tpi1 among the 3 groups (p = 0.0101 by the Kruskal-Wallis test) and a trend toward change for the model for Pgam1 (p = 0.065). The source of the change was identified by comparing the two versions of the model to the control rats with corrected Mann Whitney U p-values (Figure 2). The levels of Tpi1 and Pgam1 were decreased in the frontal cortex of PCP rats consistent with findings from human post mortem schizophrenia studies 19 . The levels of Aldoc, Hk1, Gapdh, Eno2 and Pgk1 were not found to be significantly altered in contrast with previous studies of schizophrenia in humans which found significant changes in the levels of these proteins 19 20 21 .

As a multifactorial disorder, schizophrenia is likely to be a consequence of several molecular differences of small effect 22 triggered by environmental factors 23 . These subtle and sometimes even non-significant molecular differences may be one of the reasons why it has been difficult to replicate and even determine particular genes or biomarkers responsible for schizophrenia 24 . However, the combination of these small molecular differences may lead to major biological disturbances, especially if the differentially expressed molecules belong to the same biochemical pathway. We were therefore interested in determining whether the combination of the differential expression of glycolytic enzymes can be used to differentiate between controls and PCP treated groups despite the lack of statistical significance at the univariate level. In addition, we aim to check whether SRM is a suitable proteomic tool for this task.

In the scores plot (Figure 3a), partial least squares discriminant analysis (PLS-DA) projects all the proteomic intensity data into a multi-dimensional space according to the number of analyzed variables - seven in this case. The plot as a whole is rotated in order to maximize the separation between the analyzed groups. This is then compressed down and presented on a two dimensional plot. Based on the data of seven glycolytic enzymes, this data provides a clear separation between the PCP model and the control group as seen on the scores plot (Figure 3a). The mathematical model suggests that an injection of 1 mg of PCP is similar to 2.5 mg of PCP as the model highlights no separation between the different PCP injections (Figure 3a). This suggests that the same molecules were affected at both dosages, most likely because the same molecular pathways were triggered above a threshold dosage of PCP. Figure 3b shows the influence of the proteins on the projection. The values explain the contribution of each protein to the separation in the scores plot (Figure 3a). Tpi and Pgam were the highest contributors to this separation, although Tpi was more robust than Pgam, which is consistent with the univariate findings that showed Tpi to be significantly different between animal model and control, with Pgam trending toward significance. The PLS-DA loading plot (Figure 3c) presents the relationship between the different proteins and how they relate to the separation on the scores plot (Figure 3b), it showed that Gapdh, Hk, Aldoc and Eno were clustered together. This suggests a strong correlation of their intensities and may indicate a link in the abundance of these proteins in the tissue analyzed (Figure 3c). This is interesting as none of these molecules showed statistically significant differences at the univariate level. However, the multivariate model suggests that they interact as components of the same pathway in distinguishing the PCP and control groups.

The translation of academic findings to the clinic is now a major objective of biomarker validation studies, especially in support of drug discovery. The Food and Drug Administration (FDA) has now called for efforts to modernize methods, tools and techniques for the purpose of delivering more efficacious and safer drugs 25 26 . SRM mass spectrometry can be optimized for performing high throughput multiplex analyses, which is robust and user friendly for use in the clinical environment, as stated above. Moreover, we have used the multiplex SRM mass spectrometry approach here to determine molecular phenotypes of a pre-clinical model of schizophrenia. This demonstrated the potential of translating this model for use in drug discovery with a set of companion biomarkers representing the glycolytic pathway to facilitate this process.

The validation of large scale proteomic profiling results has been a bottle neck in biomarker discovery. This is due in part to the lack of a robust technological pipeline connecting discovery with validation and translation, which is applicable to a clinical setting 27 . Here, we have shown that SRM mass spectrometry is a useful platform for achieving this goal. This approach has already been used in clinical studies such as those screening for the levels of dihydroartemisinin (DHA) in plasma of malaria patients 28 , for determining the levels of apolipoproteins in human plasma 29 and for cancer biomarker screening 30 . Finally, although in a small number of samples, the results of the analysis support a role for the PCP rat as a good model for some aspects of schizophrenia, including metabolic dysfunction, as this has shown dysregulation of the glycolysis-related enzymes as previously shown in brain tissue from schizophrenia patients.

Limitations of our study include the number of analyzed samples. Replication for validating the biological interpretation here is suggested, which can also confirm the validity of our assessment using PLS-DA. Methodologically, Tpi1 was only analyzed by one peptide. All other possible Tpi1 peptides were neither proteotypic 31 nor quantotypic 32 as well as not prone to post-translational modifications as verified by PHOSIDA http://www.phosida.com 33 .

Previous studies have shown alterations in glucose uptake in the brain following acute PCP treatment, with an initial increase within 3 hours, followed by a decrease lasting up to 2 days 4 . This finding was recently corroborated by in vivo NMR spectroscopy 5 , which showed the increased glucose uptake peaked 20 minutes after PCP injection. However it is not known whether the increase in glucose concentration is due to increased uptake through the blood-brain barrier, to decreased utilization of glucose by the brain, or both. Nevertheless, these observations support our results considering the overall differential expression of glycolytic enzymes. The multivariate model demonstrated how non-significant variations of several components of a pathway can lead to a significant biological response, as observed previously on the behavior of PCP treated animals 34 35 . Such effects may also be likely in the case of schizophrenia as conflicting reports have been published on the expression changes of individual molecules (Table 1 in 9 ).

Our findings warrant further biological research on the analysis of glycolytic enzymes in other schizophrenia models, including investigating the effects of drug treatments. Further technical developments should be directed towards the use of SRM as a multiplex tool for diagnostic purposes. In addition, pathways representing other aspects of schizophrenia could be developed using the SRM format, especially in cases of models where other pathways are known to be modified.

HR, PCG and BA are consultants for Psynova Neurotech Ltd. This work has been supported by Stanley Medical Research Institute.

The study was conceived by DMS who executed the LC-SRM experiments and analyses. DMS wrote the manuscript helped by PCG. MA and LWH performed statistical analyses and contributed to the manuscript writing. AE and NA conducted animal experiments and AE prepared protein and peptide samples. NA, IL and PP provided the tissue samples. BA developed computation algorithms for SRM data analyses. HR, SB and PCG contributed to the manuscript writing and scientific discussions. All authors read and approved the final manuscript.