Identification of targeted analyte clusters for studies of schizophrenia

Abstract

The search for biomarkers to diagnose psychiatric disorders such as schizophrenia has been underway for decades. Many molecular profiling studies in this field have focused on identifying individual marker signals that show significant differences in expression between patients and the normal population. However, signals for multiple analyte combinations that exhibit patterned behaviors have been less exploited. Here, we present a novel approach for identifying biomarkers of schizophrenia using expression of serum analytes from first onset, drug-naïve patients and normal controls. The strength of patterned signals was amplified by analyzing data in reproducing kernel spaces. This resulted in the identification of small sets of analytes referred to as targeted clusters that have discriminative power specifically for schizophrenia in both human and rat models. These clusters were associated with specific molecular signaling pathways and less strongly related to other neuropsychiatric disorders such as major depressive disorder and bipolar disorder. These results shed new light concerning how complex neuropsychiatric diseases behave at the pathway level and demonstrate the power of this approach in identification of disease-specific biomarkers and potential novel therapeutic strategies.

Fig. 1.

Scheme of identifying and validating TAC.

Fig. 2.

Factor analysis for identifying analytes that account for data variance. The expression levels of the 17 serum analytes in schizophrenia patients and controls were measured as described under “Materials and Methods.” The ability of each analyte to account for the data variance of the combined schizophrenia and control samples was then determined using factor analysis to reduce the multidimensional data to fewer factors that are linear combinations of data attributes. This led to classification of analytes according to their importance in representing the overall variance of the data. The most important analytes according to these criteria were GLP-1, LH, peptide YY (PYY), testosterone, cortisol, GH, leptin, and insulin (red enclosures). ACE, angiotensin-converting enzyme.

Fig. 3.

Gaussian kernel PCA projection of schizophrenia and control data using TAC. Data points (subjects) projected within the red region are considered schizophrenia, and those projected into the blue region are considered controls. Precision is the percentage of points in the red region that are real schizophrenia. Male, female, and combined male/female samples were tested from clinical centers 1 and 2. Red dots indicate schizophrenia patients, and blue dots are controls. Female subjects included 29 schizophrenia and 28 controls from clinical center 1 and 11 schizophrenia and 11 controls from clinical center 2. All of these were matched to male subjects for age and BMI.

Fig. 4.

Gaussian kernel PCA projection of BD, MDD, and control data using TAC. Data points (subjects) projected within the red region are considered disease (BD or MDD), and those projected into the blue region are considered controls. Precision is the percentage of points in the red region that are actual disease. Male, female, and combined male/female samples were tested. Red dots indicate schizophrenia patients, and blue dots are controls.

Fig. 5.

Gaussian kernel PCA projection of preclinical animal models using TAC. Data points (animals) projected within the red region are considered model animals, and those projected into the blue region are considered controls. Precision is the percentage of points in the red region that are actual model animals. Male acute PCP, chronic PCP, isolation, and low protein rats were tested (see “Materials and Methods”). Red dots indicate model animals, and blue dots are controls.

Fig. 6.

In silico pathway mapping of TAC analytes. LEP, leptin; HGD, homogentisate 1,2-dioxygenase; TTPA, alpha-tocopherol transfer protein; GABP, GA-binding protein; the UniProt accession numbers (insulin, leptin, and growth hormone) and PubChem identifiers (cortisol) of the analytes were submitted to the IPKB for analysis as described under “Materials and Methods.” The interaction network suggests that IRS1 is highly associated with the TAC proteins. MAPK, mitogen-activated protein kinase; PI3K, phosphatidylinositol 3-kinase; Jnk, c-Jun N-terminal kinase.

Fig. 7.

Comparison between precisions of linear and kernel PCAs. The black bars are the precision of TAC based on kernel PCA; the white bars are the precision of TAC based on linear PCA. M1 (F1) and M2 (F2) stand for male (female) samples in hospital centers 1 and 2, respectively.