Research © 2015 by The American Society for Biochemistry and Molecular Biology, Inc. This paper is available on line at http://www.mcponline.org
Antibody Profiling of Bipolar Disorder Using Escherichia coli Proteome Microarrays* Po-Chung Chen‡, Guan-Da Syu‡, Kuo-Hsuan Chung§¶, Yu-Hsuan Ho‡, Feng-Hsiang Chung‡, Pao-Huan Chen§¶, Jyun-Mu Lin‡, Yi-Wen Chen‡, Shang-Ying Tsai§¶, and Chien-Sheng Chen‡储 To profile plasma antibodies of patients with bipolar disorder (BD), an E. coli proteome microarray comprising ca. 4200 proteins was used to analyze antibody differences between BD patients and mentally healthy controls (HCs). The plasmas of HCs and patients aged 18 – 45 years with bipolar I disorder (DSM-IV) in acute mania (BD-A) along with remission (BD-R) were collected. The initial samples consisting of 19 BD-A, 20 BD-R, and 20 HCs were probed with the microarrays. After selecting protein hits that recognized the antibody differences between BD and HC, the proteins were purified to construct BD focus arrays for training diagnosis committees and validation. Additional six BD-A, six BD-R, six HCs, and nine schizophrenic disorder (SZ, as another psychiatric control) samples were individually probed with the BD focus arrays. The trained diagnosis committee in BD-A versus HC combined top six proteins, including rpoA, thrA, flhB, yfcI, ycdU, and ydjL. However, the optimized committees in BD-R versus HC and BD-A versus BD-R were of low accuracy (< 0.6). In the single blind test using another four BD-A, four HC, and four SZ samples, the committee of BD-A versus HC was able to classify BD-A versus HC and SZ with 75% sensitivity and 80% specificity that both HC and SZ were regarded as negative controls. The consensus motif of the six proteins, which form the committee of BD-A versus HC, is [KE]DIL[AG]L[LV]I[NL][IC][SVKH]G[LV][VN][LV] by Gapped Local Alignment of Motifs. We demonstrated that the E. coli proteome microarray is capable of screening BD plasma antibody differences and the selected proteins committee was successfully used for BD diagnosis with From the ‡Graduate Institute of Systems Biology and Bioinformatics, National Central University, Taiwan; §Department of Psychiatry, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan; ¶Department of Psychiatry and Psychiatric Research Center, Taipei Medical University Hospital, Taipei, Taiwan Received, November 5, 2014 and in revised form, December 19, 2014 Published, MCP Papers in Press, December 24, 2014, DOI 10.1074/mcp.M114.045930 Author contributions: Po-Chung Chen, S.T., and C.C. designed research; Po-Chung Chen, G.S., J.L., and Y.C. performed research; G.S., K.C., Y.H., F.C., Pao-Huan Chen, and S.T. contributed new reagents or analytic tools; Po-Chung Chen, G.S., Y.H., F.C., J.L., Y.C., S.T., and C.C. analyzed data; Po-Chung Chen, G.S., K.C., Y.H., F.C., Pao-Huan Chen, S.T., and C.C. wrote the paper.
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79% accuracy. Molecular & Cellular Proteomics 14: 10.1074/mcp.M114.045930, 510–518, 2015.
The etiology and genetic contributions of bipolar disorder (BD)1 largely remain unknown (1). Because of the presumed high level of etiologic heterogeneity and the overlap of dimensions across mood disorders and schizophrenia (2), the main difficulty in making an exact diagnosis for psychiatric disorder is the lack of pathological biochemical index (3). However, several lines of evidence support that various immunomodulatory factors, such as cytokine and soluble cytokine receptor, play an integral role in the pathophysiology of bipolar disorder (4 –7). For example, several studies have reported that cellmediated immunity cytokine abundance is correlated with mood state (8, 9). Our early works also found that higher levels of soluble interleukin-2 receptor (sIL-2R) (5, 10) and interleukin 1 receptor antagonist (IL-1Ra) (5, 11) are accompanied with bipolar mania. Furthermore, the abnormalities of total immunoglobulins levels in body fluid are observed in BD patients (12, 13). The possibility of biomarkers for assisting BD diagnosis has been recently highlighted (14 –16). Tumor necrosis factor alpha (TNF-␣), 3-nytrotrosine, interleukin-6, interleukin-10, and brain-derived neurotrophic factor in body fluids are potentially useful for classifying stages of BD (15). Nevertheless, they are not specific for distinguishing from other psychiatric diseases (17). Chronic inflammation exists in medicated bipolar patients displaying varied correlations with leptin, insulin, soluble TNF receptor-1 (sTNF-R1), and IL-1Ra (11). Notwithstanding, controversy exists as to whether these phenomena are state-dependent (5), normalize in remission (18), or represent trait markers exacerbated by the affective episodes (19). These discrepancies may be explained by heterogeneity in mood state, methodological differences, and not controlling for known confounds, such as obesity (6). In addition to inflammatory markers, increasing production of antibodies (20 –22) and immunoglobulins (23, 24) may be implicated with BD. 1 The abbreviations used are: BD, bipolar disarder; BD-A, manic; HC, healthy control; TBST, tris buffered saline with tween; BSA, bovine serum albumin; BD-R, remission.
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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 (25). Protein microarrays have been demonstrated as an effective high throughput platform for analysis of aberrant immune responses in diseases (26 –29). It is hypothesized that the trait or state-dependent biomarkers of bipolar disorder may exist. We attempted to identify a committee of proteins for the diagnosis of BD through employing the ca. 4200 E. coli proteins in a microarray format. The two-phase strategy for identification and validation protein hits (30) was used in this study. Although the antigens on the microarray may not be directly associated with BD, this microarray provided hundreds of thousands of epitopes for analyzing antibody profiles of plasma samples in a high throughput fashion. MATERIALS AND METHODS
Plasma Collections—All the patients were recruited from the Department of Psychiatry, Taipei Medical University Hospital in Taiwan. Acute in-patients were invited to participate in the study on the basis of the inclusion criteria: (1) fulfilling the DSM-IV criteria for bipolar I, manic (BD-A) at index evaluation; (2) 18 to 45 years old; and (3) physically healthy condition. The diagnosis was established by two experienced psychiatrists using the structured interview schedule. Age- and gender-matched healthy controls (HCs) were recruited and screened for a history of any DSM-IV Axis I disorder using a well-validated Chinese version of the General Health Questionnaire (31). Exclusion criteria for both groups were the presence of any type of Axis I disorder, a history of autoimmune or any endocrine disease, a current infection or allergy, or the use of any medication possibly affecting cytokines, blood lipids, or endocrine levels. Acute manic episode was defined as patients currently fulfilling criteria for a manic episode along with ⬎26 scores on the Young Mania Rating Scale (32). Physically healthy schizophrenic (SZ) patients (DSM-IV) aged 18 to 45 years were recruited as a comparison group. After an overnight fast beginning at 24:00 h, BD-A patients and control subjects provided blood samples between 08:30 h and 09:30 h in the following morning. Follow-up blood samples of the same manic patients in remission (BD-R) were collected, that is, when they had Young Mania Rating Scale scores of ⬍5 and Hamilton Depression Rating Scale-21 scores of ⬍7 (33). Heparinized blood was drawn by venous puncture. Plasma was collected and frozen at ⫺80 °C until used. The study is registered at http://dcwrdsys.tmu.edu.tw/tmujirb/(NO. 201201014). Fabrication of E. coli Proteome Microarrays—The high throughput protein expression, protein purification, and protein printing were modified from our previous study (34). To print the proteome microarray, all purified protein were spotted in duplicate on each aldehyde slide by SmartArrayer 136 (CapitalBio, Beijing, China) at 4 °C. After printing proteins, the proteome microarray chips were kept at 4 °C for protein immobilization on the slides for 8 h. In the end, the chips were stored at ⫺80 °C before probing with samples. Profiling BD Plasma Antibody using Proteome Chip Assays—The proteome chip was thawed using tris buffered saline with tween 20 (TBST) and blocked with 3% bovine serum albumin (BSA) in tris buffered saline (TBS) for 1 h at ambient temperature. After removing the blocking solution, 3 ml of diluted plasma sample with 1:100 dilution in 1% w/v BSA in TBST was added on the chip and incubated at 4 °C for overnight. Then, the chip was washed with 100 ml of TBST for 10 min. After removing the washing solution, 3 ml of 60 ng/ml
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TABLE I Bipolar disorder protein hits identified by E. coli K12 proteome chips Pair group
Higher-expressed hits Lower-expressed hits
BD-A vs. HC
yiaF yaiX ybjQ ygcO ybiH yigT
BD-R vs. HC
lpxA yiaF yciQ lacZ
BD-A vs. BD-R
thrA ychM ycfK glpT ynaI ycdU ydjL fldA cheW glcF yhbZ rfaC flhB rfaE yqhD yqjG uup amyA malY flgJ ybbJ yjbC phbA ynjA dppA
ycdN rpoA stfR yebT yjgT yjcO flgF yecF purF yfcI lysU ybbJ yihI narG yeiQ
Cy3-conjugated rabbit anti-human IgA⫹IgG⫹IgM antibodies in 1% w/v BSA of TBST was introduced to the chip for 1 h incubation at room temperature for secondary antibody hybridization. The slide was washed by CapitalBio SlideWasherTM using TBST two times for 5 min and DI water once for 10 s and then dried by centrifugation. Finally, the chip was read using CapitalBio LuxScanTM scanner with an excitation wavelength of 532 nm and emission wavelength of 570 nm. Data Processing using RLM and Selection of Protein Hits by Limma—The chip images were analyzed by GenePix 6.0. The data were pretreated by the following procedures. First, signal intensities were normalized by robust-linear-model (RLM) (35). Second, signal intensities below 256 were replaced as the minimum values of 256. Third, principle component analysis strategy was used to screen out bad chips (36). Linear Models for Microarray Data (Limma) was used for statistical analysis with the R package (37, 38). Finally, the protein hits were examined by eyeballing with the original images. The rule of eyeballing was based on the comparison of the intensity of the interest spot with neighboring spots. Data Processing using ProCAT and Selection of Protein Hits by Binomial Test—Protein chip analysis tool (ProCAT) was used for normalization with Perl 5.0 (39). The local cut-off was set as the mean plus three standard deviations. The binomial test was used to select hits between sample groups. The p value lower than 0.05 was considered as specific hit for each group. The resultant hits list was checked by eyeballing with the original images. Fabrication of BD Focus Arrays—The selected 48 protein hits (Table I) were purified and printed on the slide for the fabrication of the BD focus array using the same protocol for the fabrication of the entire E. coli proteome array. The format of the focus array was designed for a 4 ⫻ 24-well cassette. The pitch of spot to spot was 350 m. Each protein was printed in triplicate by SmartArrayer 136 (Cap-
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italBio) at 4 °C. After printing proteins, the chips were kept at 4 °C for protein immobilization on the slides for 8 h. The resultant chips were stored at ⫺80 °C before carrying out experiments. Plasma Probing by BD Focus Array—The focus array was thawed using TBST (pH 7.4) and blocked with 3% w/v BSA of TBS (pH 7.4) for 1 h at ambient temperature. After removing the blocking solution, the chips were assembled by 4 ⫻ 24-well hybridization cassette (Arrayit®). Each 200 l of plasma sample with 1:100 dilution in 1% w/v BSA of TBST (pH 7.4) was added into the individual well and incubated at ambient temperature for 1 h. One percent w/v BSA was used as negative control. Then, the chips were rinsed with 100 ml of TBST (pH 7.4) for 10 min. After removing the rinsing solution, 3 ml of 60 ng/ml Cy3-conjugated rabbit anti-human IgA⫹IgG⫹IgM antibodies in 1% w/v BSA was introduced to the chip and incubated for 1 h at room temperature. The slide was washed and dried by centrifugation with CapitalBio SlideWasherTM. The resultant chip was read using CapitalBio LuxScanTM scanner with an excitation wavelength of 532 nm and emission wavelength of 570 nm. Heat Map—The Cluster 3.0 (40) was used to display heat map. The data was presented by ratio of hit foreground to background signal intensity. The correlation algorithm in Cluster 3.0 was used for classifying protein hits and samples in hierarchy. The Java TreeView was used to output the image of heat map. BD Committee Training—Additional plasma-collection of six BD-A, six BD-R, six SZ patients, and six HCs, were probed with the BD focus arrays. The best combination of proteins for diagnosis was decided according to the accuracy of each combination in each group. The trained cluster databases were used for the following single blind tests. Single Blind Test—Another sample collection of four BD-A, four HC, and six SZ were used and assigned randomly with X01 to X14 in the single blind tests that the experimenter did not know the sample X information and the results were compared with the real sample information provided by diagnosis of psychiatrists. The BD-A committee, including the combination of rpoA, thrA, flhB, yfcI, ycdU, and ydjL, was used to classify sample X01 to X14 with focus arrays and trained cluster databases. The result of sample X in each pair group of BD-A versus HC, BD-A versus SZ, and HC versus SZ, had the same weight for committee voting. The sample X was predicted by the majority of votes. The sensitivity was calculated using number of true positive divided by sum of number of true positive and number of false negative. The specificity was calculated using the number of true negatives divided by sum of the number of true negatives and the number of false positives. The accuracy was calculated using the sum of number of true positives and number of true negatives divided by sum of the number of positives and negatives. Motif Search with GLAM2—All six BD-A protein committees, including rpoA, thrA, flhB, yfcI, ycdU, and ydjL were converted to FASTA format and analyzed by Gapped Local Alignment of Motifs (GLAM2) for surveying consensus motif (41). The parameters of GLAM2 were set as default. The resultant motif was then searched in entire E. coli K12 proteome by GLAM2SCAN with hypergeometric probability distribution test. RESULTS
Screening BD Protein Hits with E. coli Proteome Chips— Twenty BD patients participated in the study, but one merely agreed to provide his blood sample in his remission phase. A total of 59 plasma samples included 19 in acute mania (BD-A), 20 in remission (BD-R), and 20 healthy controls (HCs). We probed each plasma sample with the E. coli proteome chip. Fig. 1A illustrates the chip assays. Fig. 1B
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shows the representative microarray image of the BD and HC, respectively. The microarray images of BD and HC samples show different antibody binding profiles. The representative thrA protein spots show higher signal intensity in HC than BD, and yiaF protein spots show lower signal intensity in HC than BD. This represents that BD plasma has both higher-expressed and lower-expressed antibodies compared with HC. The obtained images of BD-A, BD-R, and HC samples were further analyzed by bioinformatics. Using RLM and selection of protein hits by Limma under a stringent threshold (false discovery rate ⬍ 0.05 and log fold change ⬎ 1), we found 17, 11, and nine significant protein hits in each pair group of BD-A versus HC, BD-R versus HC, and BD-A versus BD-R. On the other hand, the normalized ProCAT data was analyzed by binomial test (p ⬍ 0.05), and we found 58, 56, and 13 hits in each pair group of BD-A versus HC, BD-R versus HC, and BD-A versus BD-R. To confirm the reliability of analyses, all hits were checked by eyeballing using the original image for removing false hits. The final list was shown in Table I. In BD-A versus HC, six higher-expressed hits and 30 lower-expressed hits were found. Higher-expressed hits in BD-A versus HC represented more antibodies binding in BD-A plasma samples compared with HC. Lower-expressed hits in BD-A versus HC represented less antibodies binding in BD-A plasma samples compared with HC. In BD-R versus HC, eight proteins (four higher-expressed and four lower-expressed hits) were obtained. In BD-A versus BD-R, only six lower-expressed hits were found. YiaF was both found in BD-A versus HC and BD-R versus HC; ybbJ was both found in BD-R versus HC and BD-A versus BD-R. Forty eight hit proteins were identified (Table I) and showed statistically different signal intensities between BD and HC. To overview the performance of individual hit and accuracy of combination hits in each pair group of BD-A versus HC, BD-R versus HC, and BD-A versus BD-R, Cluster 3.0(40) was used to display heat maps (Fig. 2). As shown in Fig. 2A, the pair groups of BD-A versus HC were clustered to two groups by using 36 protein hits in BD-A versus HC with an accuracy of 0.69, indicating these hits were able to classify BD-A and HC. In Fig. 2B, the pair groups of BD-R versus HC were not clearly classified by using the eight hits of BD-R versus HC (accuracy ⫽ 0.6). In Fig. 2C, the pair groups of BD-A versus BD-R were also not clustered using the six hits of BD-A versus BD-R (accuracy ⫽ 0.59). These results indicated the identified hits were not capable of distinguishing BD-R from either HC or BD-A. Identification of Protein Committee for BD Diagnosis using Focus Arrays and Validation with a Single Blind Test—The E. coli protein hits including 36 hits of BD-A versus HC, eight hits of BD-R versus HC, and six hits of BD-A versus BD-R (Table I) were purified from the E. coli clones and printed in triplicate for the fabrication of BD focus arrays. Another plasma collection of six BD-A, six BD-R, six HC, and nine SZ
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FIG. 1. A, E. coli proteome chip assays. The plasma antibodies were bound to the E. coli proteins on the chips and detected by Cy3-labeled anti-human IgA⫹IgG⫹IgM antibodies (green). B, Representative images of the E. coli proteome chips probed with BD and HC samples, respectively. The representative proteins of thrA and yiaF on the chips were enlarged from sample images of BD and HC, respectively. Each E. coli protein was printed in duplicate on the chip. In the negative control experiment without plasma sample, the image of E. coli proteins did show no signals from Cy3-labeled anti-human IgA⫹IgG⫹IgM antibodies compared with background (data not shown). The contrast and brightness of images have been equally adjusted using the same parameters.
were used in focus arrays for protein committee training. SZ samples were used as another psychiatry disease control to test the specificity. After conducting chip assays using BD focus arrays (Fig. 3A), most triplicate spots of hits showed highly reproducible signals. The negative control of 1% BSA in TBST and blank did not show any signal. In the results of focus arrays, an individual hit cannot classify samples with high accuracy (data not shown). To improve the classifying ability for BD, combinations of protein hits as diagnosis committees were trained and tested. The training and tuning strategy of the protein committee selection was based on ranking of each protein hit candidate in each pair group with individual accuracy from the results of the focus array. The combination of protein hits was optimized using the results based on
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clustering. As shown in Fig. 3B, combination of top six protein hits, including rpoA, thrA, flhB, yfcI, ycdU, and ydjL, classify BD-A and HC with an accuracy of 0.75. However, even after the committees of the BD-R versus HC and BD-A versus BD-R were optimized (Fig. 3C and D), the accuracy was still low (⬍ 0.60). Therefore, the BD-R related hits were ruled out in the BD committee. We focused the BD-A committee on the following validation. The specificity of BD-A committee in SZ samples was examined (Fig. 3E), and the BD-A committee, from the results of BD-A versus HC, was also capable of classifying BD-A and SZ samples with an accuracy of 0.67. The SZ and HC samples were regarded as similar groups by the BD-A committee (Fig. 3F). The trained clusters of BD-A versus HC, BD-A versus SZ, and HC versus SZ (Fig. 3B, 3E,
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FIG. 2. Heat map of A, BD-A versus HC, B, BD-R versus HC, and C, BD-A versus BD-R hits from the E. coli proteome chip assays. Plasma of 19 BD-A, 20 BD-R, and 20 HC were probed with the E. coli proteome chips. The correlation algorithm in Cluster 3.0 was used for classifying protein hits and samples in hierarchy.
and 3F) were used as databases for classifying samples in the following single blind test. To test the performance of the diagnosis committee and avoid bias, single blind tests were conducted. Another plasma collection of four BD-A, four HC, and six SZ were randomly assigned as sample X01 to X14 and conducted in the single blind test. A tested sample X was voted according to the clustering results from the training databases (BD-A versus HC, BD-A versus SZ, and HC versus SZ). The result of each group had the same weight for the committee voting. For instance, in Fig. 4A, sample X01 was classified to BD-A; in Fig. 4B, sample X01 was classified to BD-A; in Fig. 4C, sample X01 was classified to SZ. Because the total results of sample X01 were two BD-A and one SZ, the prediction of sample X01 was BD-A according the majority of votes. After finishing the predictions of sample X01 to X14, the results were compared with the real sample information. In BD-A samples, three BD-A samples were diagnosed correctly (true positive) and one BD-A were misdiagnosed (false negative); in HC samples, all four HC samples were diagnosed correctly (true negative); in SZ samples, four SZ samples were diagnosed correctly (true negative) and two SZ samples were misdiagnosed (false positive). Therefore, the sensitivity of BD-A committee was
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75% and the specificity of BD-A committee was 80%. The BD-A committee of rpoA, thrA, flhB, yfcI, ycdU, and ydjL was able to classify BD-A versus HC and SZ with an accuracy of 79% in the single blind test. Motif Searching with Gapped Local Alignment—To survey whether novel consensus motif existed among the six committee proteins, the Gapped Local Alignment of Motifs (GLAM2) was performed (41). The GLAM2 has been used in the random peptide microarray for exploring antibody recognition of amino acid sequence space (42). In this study, we found the consensus motif among the six proteins is [KE]DIL[AG]L[LV]I[NL][IC][SVKH]G[LV][VN][LV] (Fig. 5). Furthermore, we used this motif to search entire E. coli K12 proteome by GLAM2SCAN (Table II). The resultant top six ranking proteins containing this motif were indeed the coincided six proteins (p ⬍ 0.0001 by hypergeometric probability distribution test). It indicated this motif was statistically unique in the entire E. coli K12 proteome. DISCUSSION
The major finding of the present study is that E. coli proteome microarray reveals antibody profiles of bipolar manic patients varying from mentally healthy individuals. No evi-
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FIG. 4. Example of the decision rule using the diagnosis committee for classifying BD-A, HC, and SZ. The sample X01 was probed using BD focus array in the single blind test. The obtained sample signals of six committee proteins were used for heat map clustering with the trained committee cluster databases of A, BD-A versus HC, B, BD-A versus SZ, and C, SZ versus HC. The sample X01 in the each group was decided according the clustering result. The results of the groups were BD-A, BD-A, and SZ. Each result has the same weight for committee voting. Therefore, the prediction of sample X01 was BD-A according majority of votes.
FIG. 5. Consensus motif among the diagnosis committee including rpoA, thrA, flhB, yfcI, ycdU, and ydjL. A motif [KE]DIL[AG]L[LV]I[NL][IC][SVKH]G[LV][VN][LV] was identified by GLAM2.
dence shows E. coli could be relevant pathogen of BD until now. Therefore, the recognized proteins may not be the direct antigens causing the abnormal immune response in BD patients. After training protein hits committees and single blind tests, six hit proteins in bipolar mania can be considered as a committee to identify patients in acute mania. By using GLAM2, the conserved protein sequence is found in the bipolar mania committee. These results demonstrate that the E. coli proteome microarray is a promising platform for analyzing aberrant antibody related disease. The strengths of the E. coli proteome microarray are that they offer linear and conformational epitopes for systematic evolution of antibody– protein interactions and that antigen based microarrays allow
screening in a high throughput and cost-effective manner (43). The present results provide additional evidence that the E. coli proteome microarray is capable of being exploited as an epitope library for screening abnormal immune related diseases and has been demonstrated to identify serological biomarkers of inflammatory bowel disease in our previous study (44). To the best of our knowledge, this is the first study to systematically investigate the immune signatures of patients with BD by means of proteome microarrays. Intriguingly, the lower-expressed proteins in acute mania and remission of bipolar patients compared with mentally healthy controls show reduced production of some specific immunoglobulins,
Fig. 3. A, BD focus array assay for the committee training and validation. The selected 48 proteins hits were purified and printed on the slide for fabrication of BD focus array. Each protein was printed in triplicate. The layout of focus array was designed for a 4 ⫻ 24 wells cassette. Each plasma sample was conducted into the individual well. After carrying out the assay, the antibodies were specific bound to the E. coli protein on the chips and detected by Cy3-labeled anti-human IgA⫹IgG⫹IgM antibodies (green). Heat map of B, BD-A versus HC, C, BD-R versus HC, and D, BD-A versus BD-R protein hits committee using focus arrays with six BD-A, six BD-R, and six HC samples. Heat map of E, BD-A versus SZ, and F, SZ versus HC with BD-A committee using six BD-A, nine SZ, and six HC samples. SZ samples were used as another psychiatry disease control to test the specificity of BD-A committee. The correlation in Cluster 3.0 was used for classifying protein hits and samples in hierarchy.
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TABLE II Top 10 proteins list of 关KE兴DIL关AG兴L关LV兴I关NL兴关IC兴关SVKH兴G关LV兴关VN兴关LV兴 enriched in entire E. coli K12. The motif was searched in entire E. coli K12 proteome by GLAM2SCAN Rank
Name
Accession number
Start
Site
End
Score
1 2 3 4 5 6 7 8 9 10
ydjL flhB thrA rpoA ycdU yfcI yfdC emrD wzc sppA
NP_416290 NP_416394 NP_414543 NP_417754 NP_415548 NP_416808 NP_416849 NP_418129 NP_416564 NP_416280
244 87 504 76 200 186 130 362 431 54
KDNLGLVIECSGANI EAMLALLPLISGVVL ANSKALLTNVHGLNL EDILEILLNLKGLAV KSIIALLILC.GVVL RDLLGLVDQIVSLLV MSNVGLLIRLWGVVL QGSLGLLMTLMGLLI AIILGLMLSIVGVLL ASRGALLLDISGVIV
258 101 518 90 213 200 144 376 445 68
29.3 28.5 26.0 25.9 25.8 23.6 17.6 16.9 16.7 16.2
such as IgA, IgG, or IgM, in bipolar patients. This is additional evidence to support the state-dependent pathophysiology of patients with bipolar disorder (6, 11). The possible biomarkers of mood disorder and schizophrenia in the literature, including neurotrophic factors, circulating immunological parameters, thyroid hormones, neuropeptides, and oxidative stress markers, are limited by their specificity to each disorder (45). Most schizophrenic patients in this study were not classified as BD sample by the BD-A committee on the focus array. In the single blind test using 14 samples, the accuracy reached 79%. Not all the schizophrenic patients had active positive symptoms and schizophrenia shares some inflammatory parameters with bipolar mania (46). Notwithstanding, our findings suggest that the diagnosis committee in this study had favorable power to distinguish bipolar mania from schizophrenia. Several methodological limitations in this study should be addressed. First, the bipolar patients took various medications throughout the study period. The effects of various psychopharmacological treatments from acute mania to remission may confound the production of immunoglobulin. Second, although we controlled for age and sex, we could not rule out the possibility that smoking, activity level, dietary habits, and obesity may have confounding effects on these results. Third, our results also suffer from small sample size and the lack of measurements in BD euthymic phase. It is a preliminary report and further validation in large scale need to be conducted for reinforcing the use of BD-A six proteins committee. As second generation antipsychotics are indicated for acute phase of bipolar disorder and schizophrenia, it is worthwhile to use the aforementioned BD-A six protein committee to classify larger samples of BD and SZ patients with the same medication from acute phase to remission in further studies. * This work was supported by Taiwan National Science Council (Grants No. NSC101-2320-B-008-004-MY3; NSC102-2627-M-008001), the Aim for the Top University Project (Grant No. 102NCU-CGH07), and National Central University and Taipei Medical University Joint Research Program (Grant No. 100TMUH-NCU-005). 储 To whom correspondence should be addressed: Graduate Institute of Systems Biology and Bioinformatics, National Central Univer-
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sity, 300, Jhongda Rd., Jhongli City, Taoyuan County 32001, Taiwan. Tel.: ⫹886-3-4227151 ext. 36103; Fax: ⫹886-3-4273822; E-mail: [email protected]. REFERENCES 1. Phillips, M. L., and Frank, E. (2006) Redefining bipolar disorder: toward DSM-V. Am. J. Psychiat. 163, 1135–1136 2. Craddock, N., O’Donovan, M. C., and Owen, M. J. (2005) The genetics of schizophrenia and bipolar disorder: dissecting psychosis. J. Med. Genet. 42, 193–204 3. Phillips, M. L., and Kupfer, D. J. (2013) Bipolar disorder diagnosis: challenges and future directions. Lancet 381, 1663–1671 4. Kapczinski, F., Dal-Pizzol, F., Teixeira, A. L., Magalhaes, P. V. S., KauerSant’Anna, M., Klamt, F., Moreira, J. C. F., Pasquali, M. A. D., Fries, G. R., Quevedo, J., Gama, C. S., and Post, R. (2011) Peripheral biomarkers and illness activity in bipolar disorder. J. Psychiatr. Res. 45, 156 –161 5. Tsai, S. Y., Chen, K. P., Yang, Y. Y., Chen, C. C., Lee, J. C., Singh, V. K., and Leu, S. J. C. (1999) Activation of indices of cell-mediated immunity in bipolar mania. Biol. Psychiatry 45, 989 –994 6. Goldstein, B. I., Kemp, D. E., Soczynska, J. K., and McIntyre, R. S. (2009) Inflammation and the phenomenology, pathophysiology, comorbidity, and treatment of bipolar disorder: a systematic review of the literature. J. Clin. Psychiatry 70, 1078 –1090 7. Kronfol, Z., and Remick, D. G. (2000) Cytokines and the brain: implications for clinical psychiatry. Am. J. Psychiat. 157, 683– 694 8. Brietzke, E., Kauer-Sant’Anna, M., Teixeira, A. L., and Kapczinski, F. (2009) Abnormalities in serum chemokine levels in euthymic patients with bipolar disorder. Brain Behav. Immun. 23, 1079 –1082 9. Brietzke, E., Stertz, L., Fernandes, B. S., Kauer-Sant’Anna, M., Mascarenhas, M., Vargas, A. E., Chies, J. A., and Kapczinski, F. (2009) Comparison of cytokine levels in depressed, manic, and euthymic patients with bipolar disorder. J. Affect. Disord. 116, 214 –217 10. Tsai, S. Y., Yang, Y. Y., Kuo, C. J., Chen, C. C., and Leu, S. J. (2001) Effects of symptomatic severity on elevation of plasma soluble interleukin-2 receptor in bipolar mania. J. Affect. Disord. 64, 185–193 11. Tsai, S. Y., Chung, K. H., Wu, J. Y., Kuo, C. J., Lee, H. C., and Huang, S. H. (2012) Inflammatory markers and their relationships with leptin and insulin from acute mania to full remission in bipolar disorder. J. Affect. Disord. 136, 110 –116 12. Hamdani, N., Doukhan, R., Kurtlucan, O., Tamouza, R., and Leboyer, M. (2013) Immunity, inflammation, and bipolar disorder: diagnostic and therapeutic implications. Curr. Psychiat. Rep. 15, 387, 1– 8 13. Yolken, R. H., and Torrey, E. F. (1995) Viruses, schizophrenia, and bipolar disorder. Clin. Microbiol. Rev. 8, 131–145 14. Singh, I., and Rose, N. (2009) Biomarkers in psychiatry. Nature 460, 202–207 15. Kapczinski, F., Dias, V. V., Kauer-Sant’Anna, M., Brietzke, E., Vazquez, G. H., Vieta, E., and Berk, M. (2009) The potential use of biomarkers as an adjunctive tool for staging bipolar disorder. Prog. Neuropsychopharmacol. Biol. Psychiatry 33, 1366 –1371 16. Alawieh, A., Zaraket, F. A., Li, J. L., Mondello, S., Nokkari, A., Razafsha, M., Fadlallah, B., Boustany, R. M., and Kobeissy, F. H. (2012) Systems
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Antibody Profiling of Bipolar Disorder Using Escherichia coli Proteome Microarrays* Po-Chung Chen‡, Guan-Da Syu‡, Kuo-Hsuan Chung§¶, Yu-Hsuan Ho‡, Feng-Hsiang Chung‡, Pao-Huan Chen§¶, Jyun-Mu Lin‡, Yi-Wen Chen‡, Shang-Ying Tsai§¶, and Chien-Sheng Chen‡储 To profile plasma antibodies of patients with bipolar disorder (BD), an E. coli proteome microarray comprising ca. 4200 proteins was used to analyze antibody differences between BD patients and mentally healthy controls (HCs). The plasmas of HCs and patients aged 18 – 45 years with bipolar I disorder (DSM-IV) in acute mania (BD-A) along with remission (BD-R) were collected. The initial samples consisting of 19 BD-A, 20 BD-R, and 20 HCs were probed with the microarrays. After selecting protein hits that recognized the antibody differences between BD and HC, the proteins were purified to construct BD focus arrays for training diagnosis committees and validation. Additional six BD-A, six BD-R, six HCs, and nine schizophrenic disorder (SZ, as another psychiatric control) samples were individually probed with the BD focus arrays. The trained diagnosis committee in BD-A versus HC combined top six proteins, including rpoA, thrA, flhB, yfcI, ycdU, and ydjL. However, the optimized committees in BD-R versus HC and BD-A versus BD-R were of low accuracy (< 0.6). In the single blind test using another four BD-A, four HC, and four SZ samples, the committee of BD-A versus HC was able to classify BD-A versus HC and SZ with 75% sensitivity and 80% specificity that both HC and SZ were regarded as negative controls. The consensus motif of the six proteins, which form the committee of BD-A versus HC, is [KE]DIL[AG]L[LV]I[NL][IC][SVKH]G[LV][VN][LV] by Gapped Local Alignment of Motifs. We demonstrated that the E. coli proteome microarray is capable of screening BD plasma antibody differences and the selected proteins committee was successfully used for BD diagnosis with From the ‡Graduate Institute of Systems Biology and Bioinformatics, National Central University, Taiwan; §Department of Psychiatry, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan; ¶Department of Psychiatry and Psychiatric Research Center, Taipei Medical University Hospital, Taipei, Taiwan Received, November 5, 2014 and in revised form, December 19, 2014 Published, MCP Papers in Press, December 24, 2014, DOI 10.1074/mcp.M114.045930 Author contributions: Po-Chung Chen, S.T., and C.C. designed research; Po-Chung Chen, G.S., J.L., and Y.C. performed research; G.S., K.C., Y.H., F.C., Pao-Huan Chen, and S.T. contributed new reagents or analytic tools; Po-Chung Chen, G.S., Y.H., F.C., J.L., Y.C., S.T., and C.C. analyzed data; Po-Chung Chen, G.S., K.C., Y.H., F.C., Pao-Huan Chen, S.T., and C.C. wrote the paper.
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79% accuracy. Molecular & Cellular Proteomics 14: 10.1074/mcp.M114.045930, 510–518, 2015.
The etiology and genetic contributions of bipolar disorder (BD)1 largely remain unknown (1). Because of the presumed high level of etiologic heterogeneity and the overlap of dimensions across mood disorders and schizophrenia (2), the main difficulty in making an exact diagnosis for psychiatric disorder is the lack of pathological biochemical index (3). However, several lines of evidence support that various immunomodulatory factors, such as cytokine and soluble cytokine receptor, play an integral role in the pathophysiology of bipolar disorder (4 –7). For example, several studies have reported that cellmediated immunity cytokine abundance is correlated with mood state (8, 9). Our early works also found that higher levels of soluble interleukin-2 receptor (sIL-2R) (5, 10) and interleukin 1 receptor antagonist (IL-1Ra) (5, 11) are accompanied with bipolar mania. Furthermore, the abnormalities of total immunoglobulins levels in body fluid are observed in BD patients (12, 13). The possibility of biomarkers for assisting BD diagnosis has been recently highlighted (14 –16). Tumor necrosis factor alpha (TNF-␣), 3-nytrotrosine, interleukin-6, interleukin-10, and brain-derived neurotrophic factor in body fluids are potentially useful for classifying stages of BD (15). Nevertheless, they are not specific for distinguishing from other psychiatric diseases (17). Chronic inflammation exists in medicated bipolar patients displaying varied correlations with leptin, insulin, soluble TNF receptor-1 (sTNF-R1), and IL-1Ra (11). Notwithstanding, controversy exists as to whether these phenomena are state-dependent (5), normalize in remission (18), or represent trait markers exacerbated by the affective episodes (19). These discrepancies may be explained by heterogeneity in mood state, methodological differences, and not controlling for known confounds, such as obesity (6). In addition to inflammatory markers, increasing production of antibodies (20 –22) and immunoglobulins (23, 24) may be implicated with BD. 1 The abbreviations used are: BD, bipolar disarder; BD-A, manic; HC, healthy control; TBST, tris buffered saline with tween; BSA, bovine serum albumin; BD-R, remission.
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Antibody Profiling of Bipolar Disorder
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 (25). Protein microarrays have been demonstrated as an effective high throughput platform for analysis of aberrant immune responses in diseases (26 –29). It is hypothesized that the trait or state-dependent biomarkers of bipolar disorder may exist. We attempted to identify a committee of proteins for the diagnosis of BD through employing the ca. 4200 E. coli proteins in a microarray format. The two-phase strategy for identification and validation protein hits (30) was used in this study. Although the antigens on the microarray may not be directly associated with BD, this microarray provided hundreds of thousands of epitopes for analyzing antibody profiles of plasma samples in a high throughput fashion. MATERIALS AND METHODS
Plasma Collections—All the patients were recruited from the Department of Psychiatry, Taipei Medical University Hospital in Taiwan. Acute in-patients were invited to participate in the study on the basis of the inclusion criteria: (1) fulfilling the DSM-IV criteria for bipolar I, manic (BD-A) at index evaluation; (2) 18 to 45 years old; and (3) physically healthy condition. The diagnosis was established by two experienced psychiatrists using the structured interview schedule. Age- and gender-matched healthy controls (HCs) were recruited and screened for a history of any DSM-IV Axis I disorder using a well-validated Chinese version of the General Health Questionnaire (31). Exclusion criteria for both groups were the presence of any type of Axis I disorder, a history of autoimmune or any endocrine disease, a current infection or allergy, or the use of any medication possibly affecting cytokines, blood lipids, or endocrine levels. Acute manic episode was defined as patients currently fulfilling criteria for a manic episode along with ⬎26 scores on the Young Mania Rating Scale (32). Physically healthy schizophrenic (SZ) patients (DSM-IV) aged 18 to 45 years were recruited as a comparison group. After an overnight fast beginning at 24:00 h, BD-A patients and control subjects provided blood samples between 08:30 h and 09:30 h in the following morning. Follow-up blood samples of the same manic patients in remission (BD-R) were collected, that is, when they had Young Mania Rating Scale scores of ⬍5 and Hamilton Depression Rating Scale-21 scores of ⬍7 (33). Heparinized blood was drawn by venous puncture. Plasma was collected and frozen at ⫺80 °C until used. The study is registered at http://dcwrdsys.tmu.edu.tw/tmujirb/(NO. 201201014). Fabrication of E. coli Proteome Microarrays—The high throughput protein expression, protein purification, and protein printing were modified from our previous study (34). To print the proteome microarray, all purified protein were spotted in duplicate on each aldehyde slide by SmartArrayer 136 (CapitalBio, Beijing, China) at 4 °C. After printing proteins, the proteome microarray chips were kept at 4 °C for protein immobilization on the slides for 8 h. In the end, the chips were stored at ⫺80 °C before probing with samples. Profiling BD Plasma Antibody using Proteome Chip Assays—The proteome chip was thawed using tris buffered saline with tween 20 (TBST) and blocked with 3% bovine serum albumin (BSA) in tris buffered saline (TBS) for 1 h at ambient temperature. After removing the blocking solution, 3 ml of diluted plasma sample with 1:100 dilution in 1% w/v BSA in TBST was added on the chip and incubated at 4 °C for overnight. Then, the chip was washed with 100 ml of TBST for 10 min. After removing the washing solution, 3 ml of 60 ng/ml
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TABLE I Bipolar disorder protein hits identified by E. coli K12 proteome chips Pair group
Higher-expressed hits Lower-expressed hits
BD-A vs. HC
yiaF yaiX ybjQ ygcO ybiH yigT
BD-R vs. HC
lpxA yiaF yciQ lacZ
BD-A vs. BD-R
thrA ychM ycfK glpT ynaI ycdU ydjL fldA cheW glcF yhbZ rfaC flhB rfaE yqhD yqjG uup amyA malY flgJ ybbJ yjbC phbA ynjA dppA
ycdN rpoA stfR yebT yjgT yjcO flgF yecF purF yfcI lysU ybbJ yihI narG yeiQ
Cy3-conjugated rabbit anti-human IgA⫹IgG⫹IgM antibodies in 1% w/v BSA of TBST was introduced to the chip for 1 h incubation at room temperature for secondary antibody hybridization. The slide was washed by CapitalBio SlideWasherTM using TBST two times for 5 min and DI water once for 10 s and then dried by centrifugation. Finally, the chip was read using CapitalBio LuxScanTM scanner with an excitation wavelength of 532 nm and emission wavelength of 570 nm. Data Processing using RLM and Selection of Protein Hits by Limma—The chip images were analyzed by GenePix 6.0. The data were pretreated by the following procedures. First, signal intensities were normalized by robust-linear-model (RLM) (35). Second, signal intensities below 256 were replaced as the minimum values of 256. Third, principle component analysis strategy was used to screen out bad chips (36). Linear Models for Microarray Data (Limma) was used for statistical analysis with the R package (37, 38). Finally, the protein hits were examined by eyeballing with the original images. The rule of eyeballing was based on the comparison of the intensity of the interest spot with neighboring spots. Data Processing using ProCAT and Selection of Protein Hits by Binomial Test—Protein chip analysis tool (ProCAT) was used for normalization with Perl 5.0 (39). The local cut-off was set as the mean plus three standard deviations. The binomial test was used to select hits between sample groups. The p value lower than 0.05 was considered as specific hit for each group. The resultant hits list was checked by eyeballing with the original images. Fabrication of BD Focus Arrays—The selected 48 protein hits (Table I) were purified and printed on the slide for the fabrication of the BD focus array using the same protocol for the fabrication of the entire E. coli proteome array. The format of the focus array was designed for a 4 ⫻ 24-well cassette. The pitch of spot to spot was 350 m. Each protein was printed in triplicate by SmartArrayer 136 (Cap-
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italBio) at 4 °C. After printing proteins, the chips were kept at 4 °C for protein immobilization on the slides for 8 h. The resultant chips were stored at ⫺80 °C before carrying out experiments. Plasma Probing by BD Focus Array—The focus array was thawed using TBST (pH 7.4) and blocked with 3% w/v BSA of TBS (pH 7.4) for 1 h at ambient temperature. After removing the blocking solution, the chips were assembled by 4 ⫻ 24-well hybridization cassette (Arrayit®). Each 200 l of plasma sample with 1:100 dilution in 1% w/v BSA of TBST (pH 7.4) was added into the individual well and incubated at ambient temperature for 1 h. One percent w/v BSA was used as negative control. Then, the chips were rinsed with 100 ml of TBST (pH 7.4) for 10 min. After removing the rinsing solution, 3 ml of 60 ng/ml Cy3-conjugated rabbit anti-human IgA⫹IgG⫹IgM antibodies in 1% w/v BSA was introduced to the chip and incubated for 1 h at room temperature. The slide was washed and dried by centrifugation with CapitalBio SlideWasherTM. The resultant chip was read using CapitalBio LuxScanTM scanner with an excitation wavelength of 532 nm and emission wavelength of 570 nm. Heat Map—The Cluster 3.0 (40) was used to display heat map. The data was presented by ratio of hit foreground to background signal intensity. The correlation algorithm in Cluster 3.0 was used for classifying protein hits and samples in hierarchy. The Java TreeView was used to output the image of heat map. BD Committee Training—Additional plasma-collection of six BD-A, six BD-R, six SZ patients, and six HCs, were probed with the BD focus arrays. The best combination of proteins for diagnosis was decided according to the accuracy of each combination in each group. The trained cluster databases were used for the following single blind tests. Single Blind Test—Another sample collection of four BD-A, four HC, and six SZ were used and assigned randomly with X01 to X14 in the single blind tests that the experimenter did not know the sample X information and the results were compared with the real sample information provided by diagnosis of psychiatrists. The BD-A committee, including the combination of rpoA, thrA, flhB, yfcI, ycdU, and ydjL, was used to classify sample X01 to X14 with focus arrays and trained cluster databases. The result of sample X in each pair group of BD-A versus HC, BD-A versus SZ, and HC versus SZ, had the same weight for committee voting. The sample X was predicted by the majority of votes. The sensitivity was calculated using number of true positive divided by sum of number of true positive and number of false negative. The specificity was calculated using the number of true negatives divided by sum of the number of true negatives and the number of false positives. The accuracy was calculated using the sum of number of true positives and number of true negatives divided by sum of the number of positives and negatives. Motif Search with GLAM2—All six BD-A protein committees, including rpoA, thrA, flhB, yfcI, ycdU, and ydjL were converted to FASTA format and analyzed by Gapped Local Alignment of Motifs (GLAM2) for surveying consensus motif (41). The parameters of GLAM2 were set as default. The resultant motif was then searched in entire E. coli K12 proteome by GLAM2SCAN with hypergeometric probability distribution test. RESULTS
Screening BD Protein Hits with E. coli Proteome Chips— Twenty BD patients participated in the study, but one merely agreed to provide his blood sample in his remission phase. A total of 59 plasma samples included 19 in acute mania (BD-A), 20 in remission (BD-R), and 20 healthy controls (HCs). We probed each plasma sample with the E. coli proteome chip. Fig. 1A illustrates the chip assays. Fig. 1B
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shows the representative microarray image of the BD and HC, respectively. The microarray images of BD and HC samples show different antibody binding profiles. The representative thrA protein spots show higher signal intensity in HC than BD, and yiaF protein spots show lower signal intensity in HC than BD. This represents that BD plasma has both higher-expressed and lower-expressed antibodies compared with HC. The obtained images of BD-A, BD-R, and HC samples were further analyzed by bioinformatics. Using RLM and selection of protein hits by Limma under a stringent threshold (false discovery rate ⬍ 0.05 and log fold change ⬎ 1), we found 17, 11, and nine significant protein hits in each pair group of BD-A versus HC, BD-R versus HC, and BD-A versus BD-R. On the other hand, the normalized ProCAT data was analyzed by binomial test (p ⬍ 0.05), and we found 58, 56, and 13 hits in each pair group of BD-A versus HC, BD-R versus HC, and BD-A versus BD-R. To confirm the reliability of analyses, all hits were checked by eyeballing using the original image for removing false hits. The final list was shown in Table I. In BD-A versus HC, six higher-expressed hits and 30 lower-expressed hits were found. Higher-expressed hits in BD-A versus HC represented more antibodies binding in BD-A plasma samples compared with HC. Lower-expressed hits in BD-A versus HC represented less antibodies binding in BD-A plasma samples compared with HC. In BD-R versus HC, eight proteins (four higher-expressed and four lower-expressed hits) were obtained. In BD-A versus BD-R, only six lower-expressed hits were found. YiaF was both found in BD-A versus HC and BD-R versus HC; ybbJ was both found in BD-R versus HC and BD-A versus BD-R. Forty eight hit proteins were identified (Table I) and showed statistically different signal intensities between BD and HC. To overview the performance of individual hit and accuracy of combination hits in each pair group of BD-A versus HC, BD-R versus HC, and BD-A versus BD-R, Cluster 3.0(40) was used to display heat maps (Fig. 2). As shown in Fig. 2A, the pair groups of BD-A versus HC were clustered to two groups by using 36 protein hits in BD-A versus HC with an accuracy of 0.69, indicating these hits were able to classify BD-A and HC. In Fig. 2B, the pair groups of BD-R versus HC were not clearly classified by using the eight hits of BD-R versus HC (accuracy ⫽ 0.6). In Fig. 2C, the pair groups of BD-A versus BD-R were also not clustered using the six hits of BD-A versus BD-R (accuracy ⫽ 0.59). These results indicated the identified hits were not capable of distinguishing BD-R from either HC or BD-A. Identification of Protein Committee for BD Diagnosis using Focus Arrays and Validation with a Single Blind Test—The E. coli protein hits including 36 hits of BD-A versus HC, eight hits of BD-R versus HC, and six hits of BD-A versus BD-R (Table I) were purified from the E. coli clones and printed in triplicate for the fabrication of BD focus arrays. Another plasma collection of six BD-A, six BD-R, six HC, and nine SZ
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FIG. 1. A, E. coli proteome chip assays. The plasma antibodies were bound to the E. coli proteins on the chips and detected by Cy3-labeled anti-human IgA⫹IgG⫹IgM antibodies (green). B, Representative images of the E. coli proteome chips probed with BD and HC samples, respectively. The representative proteins of thrA and yiaF on the chips were enlarged from sample images of BD and HC, respectively. Each E. coli protein was printed in duplicate on the chip. In the negative control experiment without plasma sample, the image of E. coli proteins did show no signals from Cy3-labeled anti-human IgA⫹IgG⫹IgM antibodies compared with background (data not shown). The contrast and brightness of images have been equally adjusted using the same parameters.
were used in focus arrays for protein committee training. SZ samples were used as another psychiatry disease control to test the specificity. After conducting chip assays using BD focus arrays (Fig. 3A), most triplicate spots of hits showed highly reproducible signals. The negative control of 1% BSA in TBST and blank did not show any signal. In the results of focus arrays, an individual hit cannot classify samples with high accuracy (data not shown). To improve the classifying ability for BD, combinations of protein hits as diagnosis committees were trained and tested. The training and tuning strategy of the protein committee selection was based on ranking of each protein hit candidate in each pair group with individual accuracy from the results of the focus array. The combination of protein hits was optimized using the results based on
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clustering. As shown in Fig. 3B, combination of top six protein hits, including rpoA, thrA, flhB, yfcI, ycdU, and ydjL, classify BD-A and HC with an accuracy of 0.75. However, even after the committees of the BD-R versus HC and BD-A versus BD-R were optimized (Fig. 3C and D), the accuracy was still low (⬍ 0.60). Therefore, the BD-R related hits were ruled out in the BD committee. We focused the BD-A committee on the following validation. The specificity of BD-A committee in SZ samples was examined (Fig. 3E), and the BD-A committee, from the results of BD-A versus HC, was also capable of classifying BD-A and SZ samples with an accuracy of 0.67. The SZ and HC samples were regarded as similar groups by the BD-A committee (Fig. 3F). The trained clusters of BD-A versus HC, BD-A versus SZ, and HC versus SZ (Fig. 3B, 3E,
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FIG. 2. Heat map of A, BD-A versus HC, B, BD-R versus HC, and C, BD-A versus BD-R hits from the E. coli proteome chip assays. Plasma of 19 BD-A, 20 BD-R, and 20 HC were probed with the E. coli proteome chips. The correlation algorithm in Cluster 3.0 was used for classifying protein hits and samples in hierarchy.
and 3F) were used as databases for classifying samples in the following single blind test. To test the performance of the diagnosis committee and avoid bias, single blind tests were conducted. Another plasma collection of four BD-A, four HC, and six SZ were randomly assigned as sample X01 to X14 and conducted in the single blind test. A tested sample X was voted according to the clustering results from the training databases (BD-A versus HC, BD-A versus SZ, and HC versus SZ). The result of each group had the same weight for the committee voting. For instance, in Fig. 4A, sample X01 was classified to BD-A; in Fig. 4B, sample X01 was classified to BD-A; in Fig. 4C, sample X01 was classified to SZ. Because the total results of sample X01 were two BD-A and one SZ, the prediction of sample X01 was BD-A according the majority of votes. After finishing the predictions of sample X01 to X14, the results were compared with the real sample information. In BD-A samples, three BD-A samples were diagnosed correctly (true positive) and one BD-A were misdiagnosed (false negative); in HC samples, all four HC samples were diagnosed correctly (true negative); in SZ samples, four SZ samples were diagnosed correctly (true negative) and two SZ samples were misdiagnosed (false positive). Therefore, the sensitivity of BD-A committee was
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75% and the specificity of BD-A committee was 80%. The BD-A committee of rpoA, thrA, flhB, yfcI, ycdU, and ydjL was able to classify BD-A versus HC and SZ with an accuracy of 79% in the single blind test. Motif Searching with Gapped Local Alignment—To survey whether novel consensus motif existed among the six committee proteins, the Gapped Local Alignment of Motifs (GLAM2) was performed (41). The GLAM2 has been used in the random peptide microarray for exploring antibody recognition of amino acid sequence space (42). In this study, we found the consensus motif among the six proteins is [KE]DIL[AG]L[LV]I[NL][IC][SVKH]G[LV][VN][LV] (Fig. 5). Furthermore, we used this motif to search entire E. coli K12 proteome by GLAM2SCAN (Table II). The resultant top six ranking proteins containing this motif were indeed the coincided six proteins (p ⬍ 0.0001 by hypergeometric probability distribution test). It indicated this motif was statistically unique in the entire E. coli K12 proteome. DISCUSSION
The major finding of the present study is that E. coli proteome microarray reveals antibody profiles of bipolar manic patients varying from mentally healthy individuals. No evi-
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FIG. 4. Example of the decision rule using the diagnosis committee for classifying BD-A, HC, and SZ. The sample X01 was probed using BD focus array in the single blind test. The obtained sample signals of six committee proteins were used for heat map clustering with the trained committee cluster databases of A, BD-A versus HC, B, BD-A versus SZ, and C, SZ versus HC. The sample X01 in the each group was decided according the clustering result. The results of the groups were BD-A, BD-A, and SZ. Each result has the same weight for committee voting. Therefore, the prediction of sample X01 was BD-A according majority of votes.
FIG. 5. Consensus motif among the diagnosis committee including rpoA, thrA, flhB, yfcI, ycdU, and ydjL. A motif [KE]DIL[AG]L[LV]I[NL][IC][SVKH]G[LV][VN][LV] was identified by GLAM2.
dence shows E. coli could be relevant pathogen of BD until now. Therefore, the recognized proteins may not be the direct antigens causing the abnormal immune response in BD patients. After training protein hits committees and single blind tests, six hit proteins in bipolar mania can be considered as a committee to identify patients in acute mania. By using GLAM2, the conserved protein sequence is found in the bipolar mania committee. These results demonstrate that the E. coli proteome microarray is a promising platform for analyzing aberrant antibody related disease. The strengths of the E. coli proteome microarray are that they offer linear and conformational epitopes for systematic evolution of antibody– protein interactions and that antigen based microarrays allow
screening in a high throughput and cost-effective manner (43). The present results provide additional evidence that the E. coli proteome microarray is capable of being exploited as an epitope library for screening abnormal immune related diseases and has been demonstrated to identify serological biomarkers of inflammatory bowel disease in our previous study (44). To the best of our knowledge, this is the first study to systematically investigate the immune signatures of patients with BD by means of proteome microarrays. Intriguingly, the lower-expressed proteins in acute mania and remission of bipolar patients compared with mentally healthy controls show reduced production of some specific immunoglobulins,
Fig. 3. A, BD focus array assay for the committee training and validation. The selected 48 proteins hits were purified and printed on the slide for fabrication of BD focus array. Each protein was printed in triplicate. The layout of focus array was designed for a 4 ⫻ 24 wells cassette. Each plasma sample was conducted into the individual well. After carrying out the assay, the antibodies were specific bound to the E. coli protein on the chips and detected by Cy3-labeled anti-human IgA⫹IgG⫹IgM antibodies (green). Heat map of B, BD-A versus HC, C, BD-R versus HC, and D, BD-A versus BD-R protein hits committee using focus arrays with six BD-A, six BD-R, and six HC samples. Heat map of E, BD-A versus SZ, and F, SZ versus HC with BD-A committee using six BD-A, nine SZ, and six HC samples. SZ samples were used as another psychiatry disease control to test the specificity of BD-A committee. The correlation in Cluster 3.0 was used for classifying protein hits and samples in hierarchy.
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TABLE II Top 10 proteins list of 关KE兴DIL关AG兴L关LV兴I关NL兴关IC兴关SVKH兴G关LV兴关VN兴关LV兴 enriched in entire E. coli K12. The motif was searched in entire E. coli K12 proteome by GLAM2SCAN Rank
Name
Accession number
Start
Site
End
Score
1 2 3 4 5 6 7 8 9 10
ydjL flhB thrA rpoA ycdU yfcI yfdC emrD wzc sppA
NP_416290 NP_416394 NP_414543 NP_417754 NP_415548 NP_416808 NP_416849 NP_418129 NP_416564 NP_416280
244 87 504 76 200 186 130 362 431 54
KDNLGLVIECSGANI EAMLALLPLISGVVL ANSKALLTNVHGLNL EDILEILLNLKGLAV KSIIALLILC.GVVL RDLLGLVDQIVSLLV MSNVGLLIRLWGVVL QGSLGLLMTLMGLLI AIILGLMLSIVGVLL ASRGALLLDISGVIV
258 101 518 90 213 200 144 376 445 68
29.3 28.5 26.0 25.9 25.8 23.6 17.6 16.9 16.7 16.2
such as IgA, IgG, or IgM, in bipolar patients. This is additional evidence to support the state-dependent pathophysiology of patients with bipolar disorder (6, 11). The possible biomarkers of mood disorder and schizophrenia in the literature, including neurotrophic factors, circulating immunological parameters, thyroid hormones, neuropeptides, and oxidative stress markers, are limited by their specificity to each disorder (45). Most schizophrenic patients in this study were not classified as BD sample by the BD-A committee on the focus array. In the single blind test using 14 samples, the accuracy reached 79%. Not all the schizophrenic patients had active positive symptoms and schizophrenia shares some inflammatory parameters with bipolar mania (46). Notwithstanding, our findings suggest that the diagnosis committee in this study had favorable power to distinguish bipolar mania from schizophrenia. Several methodological limitations in this study should be addressed. First, the bipolar patients took various medications throughout the study period. The effects of various psychopharmacological treatments from acute mania to remission may confound the production of immunoglobulin. Second, although we controlled for age and sex, we could not rule out the possibility that smoking, activity level, dietary habits, and obesity may have confounding effects on these results. Third, our results also suffer from small sample size and the lack of measurements in BD euthymic phase. It is a preliminary report and further validation in large scale need to be conducted for reinforcing the use of BD-A six proteins committee. As second generation antipsychotics are indicated for acute phase of bipolar disorder and schizophrenia, it is worthwhile to use the aforementioned BD-A six protein committee to classify larger samples of BD and SZ patients with the same medication from acute phase to remission in further studies. * This work was supported by Taiwan National Science Council (Grants No. NSC101-2320-B-008-004-MY3; NSC102-2627-M-008001), the Aim for the Top University Project (Grant No. 102NCU-CGH07), and National Central University and Taipei Medical University Joint Research Program (Grant No. 100TMUH-NCU-005). 储 To whom correspondence should be addressed: Graduate Institute of Systems Biology and Bioinformatics, National Central Univer-
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sity, 300, Jhongda Rd., Jhongli City, Taoyuan County 32001, Taiwan. Tel.: ⫹886-3-4227151 ext. 36103; Fax: ⫹886-3-4273822; E-mail: [email protected]. REFERENCES 1. Phillips, M. L., and Frank, E. (2006) Redefining bipolar disorder: toward DSM-V. Am. J. Psychiat. 163, 1135–1136 2. Craddock, N., O’Donovan, M. C., and Owen, M. J. (2005) The genetics of schizophrenia and bipolar disorder: dissecting psychosis. J. Med. Genet. 42, 193–204 3. Phillips, M. L., and Kupfer, D. J. (2013) Bipolar disorder diagnosis: challenges and future directions. Lancet 381, 1663–1671 4. Kapczinski, F., Dal-Pizzol, F., Teixeira, A. L., Magalhaes, P. V. S., KauerSant’Anna, M., Klamt, F., Moreira, J. C. F., Pasquali, M. A. D., Fries, G. R., Quevedo, J., Gama, C. S., and Post, R. (2011) Peripheral biomarkers and illness activity in bipolar disorder. J. Psychiatr. Res. 45, 156 –161 5. Tsai, S. Y., Chen, K. P., Yang, Y. Y., Chen, C. C., Lee, J. C., Singh, V. K., and Leu, S. J. C. (1999) Activation of indices of cell-mediated immunity in bipolar mania. Biol. Psychiatry 45, 989 –994 6. Goldstein, B. I., Kemp, D. E., Soczynska, J. K., and McIntyre, R. S. (2009) Inflammation and the phenomenology, pathophysiology, comorbidity, and treatment of bipolar disorder: a systematic review of the literature. J. Clin. Psychiatry 70, 1078 –1090 7. Kronfol, Z., and Remick, D. G. (2000) Cytokines and the brain: implications for clinical psychiatry. Am. J. Psychiat. 157, 683– 694 8. Brietzke, E., Kauer-Sant’Anna, M., Teixeira, A. L., and Kapczinski, F. (2009) Abnormalities in serum chemokine levels in euthymic patients with bipolar disorder. Brain Behav. Immun. 23, 1079 –1082 9. Brietzke, E., Stertz, L., Fernandes, B. S., Kauer-Sant’Anna, M., Mascarenhas, M., Vargas, A. E., Chies, J. A., and Kapczinski, F. (2009) Comparison of cytokine levels in depressed, manic, and euthymic patients with bipolar disorder. J. Affect. Disord. 116, 214 –217 10. Tsai, S. Y., Yang, Y. Y., Kuo, C. J., Chen, C. C., and Leu, S. J. (2001) Effects of symptomatic severity on elevation of plasma soluble interleukin-2 receptor in bipolar mania. J. Affect. Disord. 64, 185–193 11. Tsai, S. Y., Chung, K. H., Wu, J. Y., Kuo, C. J., Lee, H. C., and Huang, S. H. (2012) Inflammatory markers and their relationships with leptin and insulin from acute mania to full remission in bipolar disorder. J. Affect. Disord. 136, 110 –116 12. Hamdani, N., Doukhan, R., Kurtlucan, O., Tamouza, R., and Leboyer, M. (2013) Immunity, inflammation, and bipolar disorder: diagnostic and therapeutic implications. Curr. Psychiat. Rep. 15, 387, 1– 8 13. Yolken, R. H., and Torrey, E. F. (1995) Viruses, schizophrenia, and bipolar disorder. Clin. Microbiol. Rev. 8, 131–145 14. Singh, I., and Rose, N. (2009) Biomarkers in psychiatry. Nature 460, 202–207 15. Kapczinski, F., Dias, V. V., Kauer-Sant’Anna, M., Brietzke, E., Vazquez, G. H., Vieta, E., and Berk, M. (2009) The potential use of biomarkers as an adjunctive tool for staging bipolar disorder. Prog. Neuropsychopharmacol. Biol. Psychiatry 33, 1366 –1371 16. Alawieh, A., Zaraket, F. A., Li, J. L., Mondello, S., Nokkari, A., Razafsha, M., Fadlallah, B., Boustany, R. M., and Kobeissy, F. H. (2012) Systems
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