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. 2020 Feb 25;29:105338. doi: 10.1016/j.dib.2020.105338

Proteome data of serum samples from patients with schizophrenia

TV Butkova a,, AT Kopylov a, AA Stepanov a, KA Malsagova a, GP Kostyuk b, NV Zakharova b, LV Bravve b, AA Sinicyna a, AL Kaysheva a
PMCID: PMC7058900  PMID: 32154357

Abstract

Schizophrenia is a complex chronic disease. The molecular determinants and neuropathology of schizophrenia are multifaceted; an important role in the pathogenesis is played by the dysregulation of molecular and epigenetic mechanisms. However, the molecular mechanisms of the development of the disease have not yet been studied.

An important task is the accumulation and systematization of “OMICS”-knowledge of the molecular profiles (transcriptome, proteome, metabolome) of blood specific to pathology. Thereby the development and improvement of mass spectrometric methods for the detection of biological molecules has become increasingly important in biomedical research. In the field of applied problems in biomedical research, the most prevalent issue involves the identification of serological protein markers associated with the development of schizophrenia, which account for the diseases that cause the a life-shortening illness, disability, decreased of functioning and quality of life and wellbeing or health status.

OMICS approaches are designed to detect genes (genomics), mRNA (transcriptomics), proteins (proteomics) and metabolites (metabolomics) in a specific biological sample.

We report the proteomic datasets on the serum samples from patients with schizophrenia (series “SCZ”) and healthy volunteers (series “CNT”). Data were acquired using shotgun ultra-high resolution mass spectrometry.

Keywords: Serum, Schizophrenia, Proteomics, Tandem mass spectrometry


Specifications Table

Subject Biology
Specific subject area Biochemistry, omics analysis, protein detection
Type of data Table, Text file
How data were acquired Liquid chromatography-tandem mass spectrometric analysis was carried out using Q Exactive high-resolution mass
spectrometer (Thermo Fisher Scientific, USA) coupled with an Ultimate 3000 Nano-flow HPLC system (Thermo Fisher Scientific, USA)
Data format Raw, filtered
Parameters for data collection 50 control samples blood serum from healthy volunteers and 49 samples from blood serum from patients with schizophrenia
Description of data collection - Digestion of proteins.
- LC-MS/MS analysis.
- Data processing.
Data source location Moscow, Russia
Data accessibility Proteomic data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD016297. https://www.ebi.ac.uk/pride/archive/projects/PXD016297
Value of the Data
  • Dataset represents proteomes of serum samples from patients with schizophrenia as well as from control healthy volunteers, which can be compared to reveal molecular pathways of pathology.

  • Blood plasma serves as an attractive source of candidate protein markers and specific pathologies for molecular profiling, as it contains molecular components secreted by cells in diseased tissues, as well as factors involved in the development of pathophysiological processes.

  • Protein profiling are perspective to reveal for clinical monitoring of drug therapy, for identification of affected signalling pathways may indicate the direction of research for the development of a systematic approach to the diagnosis and classification of schizophrenia disease.

  • Protein profiles are available in the form of “∗.raw” and “∗.mgf” data that can be further processed by researchers using their own bioinformatics algorithms and analysed together with their own data.

1. Data description

The dataset contains “∗.raw ” and “∗.mgf ” data obtained through the shotgun HPLC-MS/MS analysis of serum samples from 49 patients with schizophrenia and 50 healthy volunteers. Data are available via ProteomeXchange with identifier PXD016297 [1]. Information about blood samples collected from patients with schizophrenia and control samples from healthy donors is presented in Table 1. Dataset covers 99 biological samples (see Table 2).

Table 1.

Data of schizophrenia patients.

Parameter Patients, n = 49 Healthy volunteers, n = 50
Male 26 19
Female 23 31
Family status
Married 6 15
Divorced 3 3
Never married 40 32
Educationa
Incomplete secondary education 2 1
Secondary education 8 15
Secondary special education 13 1
Incomplete higher education 7 4
Higher education 19 29
Worker status
Student 6 37
Working 8 13
Unemployment 10 0
Disabled 0 0
Average age, years
at the time of blood sampling 27,0 ± 5,1 25,9 ± 5,8
first appeal 22,2
Diagnostic tools to confirm schizophrenia (Mann-Whitney U Test < 0.05)
Positive and Negative Syndrome Scale (PANSS) 112,7 33,1
The Bush-Francis Catatonia Rating Scale (BFCRS) 8,0 0
The 4-Item Negative Symptom Assessment (NSA-4) 21,0 0
a

Russia's Educational System.

Table 2.

Sample description.

Sample ID Files “∗.mgf ”, “∗.raw ” Size of “∗.mgf ”, MB Size of ”∗.raw ”, MB Type of set
YGW735 CNT_20190613_01_YGW735 36,496 299,933 control
KOHYW0 CNT_20190613_02_KOHYW0 32,508 310,499 control
F0TP43 CNT_20190613_03_F0TP43 12,141 354,819 control
SOV2XB CNT_20190613_04_SOV2XB 55,592 374,428 control
UNRJB0 CNT_20190613_05_UNRJB0 27,143 309,493 control
D06354 CNT_20190613_06_D06354 66,683 375,034 control
XTJKUA CNT_20190613_07_XTJKUA 20,075 228,465 control
BEZ9HT CNT_20190613_08_BEZ9HT 62,221 380,846 control
JXR1JH CNT_20190613_09_JXR1JH 71,333 384,764 control
V1KI56 CNT_20190613_10_V1KI56 65,864 377,322 control
8EUMZS CNT_20190613_11_8EUMZS 58,062 369,257 control
ODFBCY CNT_20190613_12_ODFBCY 55,578 376,181 control
CMIJXTK CNT_20190613_13_CMIJXTK 61,93 371,116 control
50DQE5 CNT_20190613_14_50DQE5 71,957 384,9 control
A6KS1M CNT_20190613_15_A6KS1M 30,391 310,726 control
ADAAWO CNT_20190613_16_ADAAWO 17,498 183,466 control
0O78QX CNT_20190613_17_0O78QX 59,479 370,889 control
4GO4TM CNT_20190613_18_4GO4TM 74,33 386,428 control
C5WJWQ CNT_20190613_19_C5WJWQ 31,879 307,852 control
FAGIC5 CNT_20190613_20_FAGIC5 28,488 275,702 control
WML65Y CNT_20190613_21_WML65Y 84,281 386,867 control
9L82IT CNT_20190613_22_9L82IT 56,14 369,903 control
6H1B6S CNT_20190613_23_6H1B6S 20,307 259,611 control
HIH318 CNT_20190613_24_HIH318 27,607 289,802 control
0SFACQ CNT_20190613_25_0SFACQ 20,354 264,346 control
9U3UGD CNT_20190613_26_9U3UGD 73,548 376,671 control
PZRMAK CNT_20190613_27_PZRMAK 26,31 310,059 control
VLXKLO CNT_20190613_28_VLXKLO 27,886 309,922 control
TANU6P CNT_20190613_29_TANU6P 17,669 244,21 control
MUM0IX CNT_20190613_30_MUM0IX 25,266 328,986 control
R6P2S2 CNT_20190613_31_R6P2S2 77,756 379,815 control
KLSD4V CNT_20190613_32_KLSD4V 61,32 378,474 control
705M82 CNT_20190613_33_705M82 28,658 312,383 control
CLGU1Q CNT_20190613_34_CLGU1Q 61,026 375,212 control
ZQRSP6 CNT_20190613_35_ZQRSP6 27,541 296,7 control
454ZZV CNT_20190613_36_454ZZV 3,181 242,924 control
F40I59 CNT_20190613_37_F40I59 28,703 292,511 control
CAQSMO CNT_20190613_38_CAQSMO 28,1 287,196 control
ZM532 N CNT_20190613_39_ZM532 N 51,479 371,66 control
50O6VV CNT_20190613_40_50O6VV 31,519 311,755 control
U9YFIR CNT_20190613_41_U9YFIR 80,734 371,034 control
C47UZM CNT_20190613_42_C47UZM 32,161 316,571 control
XJ6412 CNT_20190613_43_XJ6412 34,698 317,005 control
6ZKO63 CNT_20190613_44_6ZKO63 16,174 190,94 control
VMIHKL CNT_20190613_45_VMIHKL 28,302 285,948 control
OQLRX4 CNT_20190613_46_OQLRX4 31,157 291,616 control
NFP2WG CNT_20190613_47_NFP2WG 85,756 379,078 control
7CX0U3 CNT_20190613_48_7CX0U3 65,229 372,454 control
HARVQQ CNT_20190613_49_HARVQQ 52,139 373,033 control
EX6CFH CNT_20190613_50_EX6CFH 77,873 370,668 control



YC1350 SCH_20190610_01_YC1350 68,356 344,859 schizophrenia
CXB45F SCH_20190610_02_CXB45F 30,051 256,296 schizophrenia
Z354Y7 SCH_20190610_03_Z354Y7 25,949 272,672 schizophrenia
EFHMWB SCH_20190610_04_EFHMWB 21,491 272,698 schizophrenia
ZW4912 SCH_20190610_05_ZW4912 47,083 372,386 schizophrenia
YM6648 SCH_20190610_06_YM6648 36,862 349,4 schizophrenia
XO4528 SCH_20190610_07_XO4528 28,831 309,772 schizophrenia
214009 SCH_20190610_08_214009 32,955 329,272 schizophrenia
XT0224 SCH_20190610_09_XT0224 26,24 324,236 schizophrenia
XP2950 SCH_20190610_10_XP2950 30,717 322,526 schizophrenia
XZ8204 SCH_20190610_11_XZ8204 28,852 313,83 schizophrenia
ZL4104 SCH_20190610_12_ZL4104 30,721 300,117 schizophrenia
QL4MJF SCH_20190610_13_QL4MJF 9,633 419,259 schizophrenia
TW2654 SCH_20190610_14_TW2654 29,695 319,599 schizophrenia
ZM9268 SCH_20190610_15_ZM9268 36,432 331,841 schizophrenia
7T9E6N SCH_20190610_16_7T9E6N 28,278 306,671 schizophrenia
6BZGDC SCH_20190610_17_6BZGDC 24,741 294,882 schizophrenia
YN2166 SCH_20190610_18_YN2166 39,257 311,476 schizophrenia
WU2600 SCH_20190610_19_WU2600 23,116 280,623 schizophrenia
ZE3674 SCH_20190610_20_ZE3674 25,114 361,736 schizophrenia
ZT4320 SCH_20190610_21_ZT4320 29,695 297,123 schizophrenia
WZ0609 SCH_20190610_22_WZ0609 26,424 243,559 schizophrenia
XW7605r SCH_20190610_23_XW7605r 27,021 301,198 schizophrenia
TP9276 SCH_20190610_24_TP9276 32,121 282,769 schizophrenia
XS1165 SCH_20190610_25_XS1165 26,678 311,717 schizophrenia
7DLGDR SCH_20190610_26_7DLGDR 40,358 351,499 schizophrenia
Z3I4Y7 SCH_20190610_27_Z3I4Y7 26,408 288,453 schizophrenia
XP1143 SCH_20190610_28_XP1143 27,226 333,953 schizophrenia
XR1760 SCH_20190610_29_XR1760 28,219 306,195 schizophrenia
XW9999 SCH_20190610_30_XW9999 30,625 312,772 schizophrenia
YN7802 SCH_20190610_31_YN7802 35,292 300,74 schizophrenia
YM9980 SCH_20190610_32_YM9980 33,33 319,595 schizophrenia
XY2612 SCH_20190610_33_XY2612 35,366 332,174 schizophrenia
1B2AF3 SCH_20190610_34_1B2AF3 31,84 303,574 schizophrenia
KI2824 SCH_20190610_35_KI2824 34,055 308,374 schizophrenia
Z57012 SCH_20190610_36_Z57012 29,843 353,113 schizophrenia
Z00291 SCH_20190610_37_Z00291 32,211 342,074 schizophrenia
YX0920 SCH_20190610_38_YX0920 32,216 351,013 schizophrenia
2B2612 SCH_20190610_39_2B2612 26,518 273,173 schizophrenia
YN3925 SCH_20190610_40_YN3925 25,788 297,709 schizophrenia
GS4953 SCH_20190610_41_GS4953 28,146 314,648 schizophrenia
YT7896 SCH_20190610_42_YT7896 27,905 288,273 schizophrenia
ZB6894 SCH_20190610_43_ZB6894 32,331 300,684 schizophrenia
XW6458 SCH_20190610_44_XW6458 25,98 312,08 schizophrenia
YS2187 SCH_20190610_45_YS2187 37,132 327,972 schizophrenia
ZD0291 SCH_20190610_46_ZD0291 43,1 355,323 schizophrenia
XO6440 SCH_20190610_47_XO6440 28,316 292,074 schizophrenia
XX2845r SCH_20190610_48_XX2845r 34,265 350,4 schizophrenia
5O9UN9 SCH_20190610_49_5O9UN9 27,288 300,144 schizophrenia

2. Experimental design, materials, and methods

2.1. Reagents

Acetonitrile and TCA were from Merck (Germany). Formic acid was from ACROS Organics (USA). Ethylenediaminetetraacetic acid (EDTA) was from Sigma-Aldrich (USA). Modified trypsin was from Promega (USA). MOPS (4-morphline-propane-sulphonic acid sodium salt), BUN, deoxycholic acid sodium salt, hydrogen carbonate of ammonium triethyl, ТСЕР (tris-(2-carboxyethil)-phosphine), 4-vinyl pyridine, propane-2 olefins, formic acid (Merck, Germany), deoxycholic acid, methanol, trifluoroacetic acid (Fluka, Germany).

2.2. BioSamples

These data include patients with a diagnosis of schizophrenia and healthy controls, all participants signed the informed consent form.

The group of patients: 49 patients (26 men, 23 women, average age 26.9 ± 5.2 years) who were hospitalized in State Healthcare Institution «Psychiatric Clinical Hospital 1 n. a. N.A. Alekseev of Healthcare Department of Moscow» from February to April 2019 with a diagnosis of schizophrenia.

The control group consisted of 50 volunteers from among employees, students and residents who have never sought psychiatric help and who are not related to patients.

2.3. Inclusion criteria

  • 1.

    Age 18 or older

  • 2.

    Male or female

  • 3.

    Diagnosis of schizophrenia

The diagnosis of schizophrenia was established on the basis of the criteria of the International Classification of Diseases of the 10th revision (ICD-10) (see Table 3).

Table 3.

Diagnosis data.

The diagnosis of ICD-10 F20.0 F20.2 F20.8 F21.8 F25.1 F25.2
The number of patients with an established diagnosis 41 3 2 1 1 1

2.4. Non-inclusion criteria

  • 1.

    Organic disease of the central nervous system;

  • 2.

    Decompensation of somatic disease;

  • 3.

    The period of pregnancy and lactation in women;

  • 4.

    Abuse of alcohol and psychoactive substances.

The clinical and psychometric methods used in the practice of research on mental pathology were used. A single examination of patients involves:

  • -

    psychopathological and somatic examination;

  • -

    psychometric examination using standardized international scales (PANSS, FAB, NSA-4, BFCRS)

Blood sampling (8–12 ml) was carried out in vacuum tubes with heparin in a treatment room in compliance with aseptic and antiseptic rules. Transportation to the laboratory was carried out within 2 hours from the moment of collection. Blood sampling was carried out once between 8 and 9 a.m. in the clinic's treatment rooms from a cubital vein into tubes with EDTA and a gel separator, followed by centrifugation of 2000 rpm for 20 minutes. The isolated serum was stored in eppendorf type microtubes at −80, whole blood was stored at −20 until transported to the laboratory. Transportation was carried out in compliance with material safety requirements.

2.5. Sample preparation for MS analysis

The blood plasma in the volume of 40 μl was then brought to the final volume of 160 μl by adding the solution 15 mM MOPS (4-morpholinepropanesulfonic acid sodium salt), рН 7.4.

The dry residue was restored in 500 μl of 0.1% deoxycholic acid sodium salt, 6% acetonitrile, 75 mM triethylammonium bicarbonate, рН 8.5. The protein solution was heated up at 90 °C for 10 minutes at intensive shaking (1100 rpm). After equilibration to ambient temperature, 3 mM ТСЕР (Tris (2-carboxyethyl)phosphine) was added to the denatured protein solution to restore the sulfhydryl groups of amino-acid residues of cysteine. The reaction was incubated at 45 °C for 20 minutes. For alkylation, the denatured protein solution was added with a solution of 0.2% 4-vinylpyridine in 30% propan-2-ol up to a final concentration of 0.02% (V/V). The alkylation reaction was carried out for 30 minutes at normal temperature in the lightproof place.

Enzymatic cleavage of proteins was performed using a specific trypsin protease. The protein solution was added with modified (acetylated at primary amino groups of lysine) trypsin at enzyme-to-substrate ratio as 1:50. The reaction was incubated at 42 °C for 4 hours with intermittent mixing for 3 minutes every 15 minutes. After that, the second aliquot of trypsin was added at ration 1:100 and incubated at 37 °C continued for additional 12 hours.

Upon the time expiry the enzyme reaction was inhibited by adding the formic acid up to the final concentration of 0.5%, which also caused precipitation of insoluble deoxycholic acid. The obtained suspended solids were centrifuged at 12,000 rpm at 15 °C for 10 minutes. The supernatant (approximately 550 μl) was collected and applied to Discovery DSC solid-phase columns, which were preliminary equilibrated with the solution of 2% methanol with 0.1% formic acid. After sample application the columns were washed twice with 1 ml of 0.1% formic acid solution, and then peptides were eluted from the carrier using the solution of 70% methanol with 5% formic acid in the volume of 1 ml. The collected Eliot was dried at 30 °C for 45 minutes in a vacuum. The dry residue was restored in 40 μl of 0.5% formic acid solution and transferred into vials of deactivated glass for mass spectrometry analysis [2,3].

2.6. Mass spectrometry protein registration

The mass spectrometric analysis of the peptide composition of plasma samples was conducted for depleted plasma samples. HPLC-MS/MS registration of peptides was carried out using high resolution mass spectrometer Q Exactive (Thermo Scientific, USA, Catalog # IQLAAEGAAPFALGDK) by chromatographic separation using Ultimate 3000 Nano-flow HPLC system (Thermo Scientific, USA, Catalog # ULTIM3000RSLCNANO). Peptides in the volume of 5 μl were applied on enrichment column PepMap C18 for 4 minutes in the isocratic flow of the mobile phase C (2% acetonitrile, 0.08% formic acid, 0.015% trifluoroacetic acid) at a flow rate of 20 μl/min. Peptides were separated using Acclaim PepMap C18 analytical column (75 mm × 150 μm, particle size 2 μm, pore size 100 A) in the nano-flow mode in the linear gradient of the mobile phase A (0.08% formic acid, 0.015% trifluoroacetic acid) and the mobile phase B (0.08% formic acid, 0.015% trifluoroacetic acid in acetonitrile) at a flow rate of s400 nl/min at initial ratio А: В as 98:2. Separation was performed in the elution gradient from 2% to 35% of mobile phase B content for 80 minutes, followed by column washing at 90% of phase B for 10 minutes with subsequent system equilibration at initial gradient conditions for 20 minutes.

Registration of peptide signal was carried out in the dependent tandem scan mode with ionization source NSI (Thermo Scientific, USA). After rescanning of precursor ions with maximum accumulation time not more than 80 ms (or maximum accumulation value 3е6) with resolution R = 70 K in the range of 420–1250 m/z, 20 sequential tandem scans were made with maximum accumulation time not more than 120 ms (or maximum accumulation value 1е5) with resolution R = 17.5 K a with fixed minimum range value (from 220 m/z) and varying maximum range value depending on the resolved charge state. Ions with charge state z = 2+ … 5+ were selected for tandem scanning using the dynamic exclusion for the duration of one half-width of the chromatographic peak. Isolation of precursor ions was performed with the width of w = ±1 Th within the range from 9 to 17 s from the peak apex for the tandem scanning. Fragmentation was performed in the high-energy activation mode (HCD – Higher-energy collisional dissociation) with rating 27% (per weight of 400 m/z and charge z = 2+) and variation per each scanning within ±15%. HPLC-MS/MS spectra in RAW format were processed in Mass Hunter version В 2.0 [2,3].

Acknowledgments

This work was financially supported by the Russian Science Foundation, grant 19-14-00298. The peptide synthesis was performed using the equipment of “Human Proteome” Core Facilities of the Institute of Biomedical Chemistry.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.dib.2020.105338.

Conflict of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A. Supplementary data

The following is the Supplementary data to this article:

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mmc1.xml (320B, xml)

References

  • 1.Perez-Riverol Y., Csordas A., Bai J., Bernal-Llinares M., Hewapathirana S., Kundu D.J., Inuganti A., Griss J., Mayer G., Eisenacher M., Pérez E., Uszkoreit J., Pfeuffer J., Sachsenberg T., Yilmaz S., Tiwary S., Cox J., Audain E., Walzer M., Jarnuczak A.F., Ternent T., Brazma A., Vizcaíno J.A. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucl. Acids Res. 2019;47(D1):D442–D450. doi: 10.1093/nar/gky1106. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Kaysheva A.L., Kopylov A.T., Kushlinskii N.E. Comparative analysis of blood plasma proteome in patients with renal cell carcinoma. Bull. Exp. Biol. Med. 2019;167(1):91–96. doi: 10.1007/s10517-019-04468-2. [DOI] [PubMed] [Google Scholar]
  • 3.Kaysheva A.L., Kopylov A.T., Ponomarenko E.A., Kiseleva O.I., Teryaeva N.B., Potapov A.A., Izotov A.А., Morozov S.G., Kudryavtseva V.Y., Archakov A.I. Relative abundance of proteins in blood plasma samples from patients with chronic cerebral ischemia. J. Mol. Neurosci. 2018;64(3):440–448. doi: 10.1007/s12031-018-1040-3. [DOI] [PubMed] [Google Scholar]

Associated Data

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Supplementary Materials

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mmc1.xml (320B, xml)

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