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. 2018 Sep 20;13(9):e0199237.
doi: 10.1371/journal.pone.0199237. eCollection 2018.

Proteomic analysis of rat serum revealed the effects of chronic sleep deprivation on metabolic, cardiovascular and nervous system

Bo Ma  1 , Jincheng Chen  1 , Yongying Mu  2 , Bingjie Xue  1 , Aimei Zhao  1 , Daoping Wang  2 , Dennis Chang  3 , Yinghong Pan  2   4 , Jianxun Liu  1   3
Affiliations

Proteomic analysis of rat serum revealed the effects of chronic sleep deprivation on metabolic, cardiovascular and nervous system

Bo Ma et al. PLoS One. .

Abstract

Sleep is an essential and fundamental physiological process that plays crucial roles in the balance of psychological and physical health. Sleep disorder may lead to adverse health outcomes. The effects of sleep deprivation were extensively studied, but its mechanism is still not fully understood. The present study aimed to identify the alterations of serum proteins associated with chronic sleep deprivation, and to seek for potential biomarkers of sleep disorder mediated diseases. A label-free quantitative proteomics technology was used to survey the global changes of serum proteins between normal rats and chronic sleep deprivation rats. A total of 309 proteins were detected in the serum samples and among them, 117 proteins showed more than 1.8-folds abundance alterations between the two groups. Functional enrichment and network analyses of the differential proteins revealed a close relationship between chronic sleep deprivation and several biological processes including energy metabolism, cardiovascular function and nervous function. And four proteins including pyruvate kinase M1, clusterin, kininogen1 and profilin-1were identified as potential biomarkers for chronic sleep deprivation. The four candidates were validated via parallel reaction monitoring (PRM) based targeted proteomics. In addition, protein expression alteration of the four proteins was confirmed in myocardium and brain of rat model. In summary, the comprehensive proteomic study revealed the biological impacts of chronic sleep deprivation and discovered several potential biomarkers. This study provides further insight into the pathological and molecular mechanisms underlying sleep disorders at protein level.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Workflow of the proteomic profiling and data analysis.
Serum samples of normal (N, n = 12) group and chronic sleep deprivation (CSD, n = 12) group were analyzed by label-free quantitative proteomics methods. Proteins with differential abundance (DAPs) were analyzed using bioinformatics soft and online database. Several candidate proteins were identified and verified using western blot and LC-PRM analyses. Finally, the potential biomarkers were validated by LC-MS analysis using myocardial and brain tissue samples of rat models.
Fig 2
Fig 2. Overview of the DAPs between N- and CSD-groups.
(A) Area-proportional Venn diagram depicts the overlap of the identified serum proteins between N- and CSD-group rats from mass spectrometry measurements. A total of 309 non-redundant proteins with 297 and 298 proteins were identified in N- and CSD-groups, respectively. 286 proteins were found in both groups. (B) A heat map analysis of the DAPs between N- and CSD-group. 117 proteins displayed more than 1.8-fold quantitative alteration, of which 49 proteins were upregulated and 68 were downregulated in the CSD-group compared to that of the N-group. Color depth from blue to red indicates the intensity detected by LC-MS analysis from low to high.
Fig 3
Fig 3. Bioinformatics analysis of the DAPs.
(A & B) Functional assignments of protein class and molecular pathway of the DAPs according to gene ontology analysis (89 proteins out of the 117 DAPs were mapped). Numbers in the brackets represent the number of proteins. The protein classes include defense/immunity protein, enzyme modulator, hydrolase, signaling molecule, cytoskeletal protein, and so on. And the DAPs participate in a variety of biological pathways including glycolysis, blood coagulation, Integrin signaling pathway, Parkinson disease, Huntington disease, et al. (C) Biological and molecular function enrichment of DAPs is classified according to GO analyses and UniProt database. The bar chart shows the number of proteins in each functional class. The DAPs following CSD are associated with biological processes of metabolic process, cardiovascular function, nervous system function, and some other processes as response to stimulus, response to stress and so on.
Fig 4
Fig 4. Protein interaction networks of DAPs demonstrated two clusters involving in energy metabolism and cardiovascular function.
(A) STRING analysis of protein interaction networks of the DAPs. Groups 1 and 2 marked in red represent proteins involved in energy metabolism and cardiovascular function, respectively. (B) Comparison of body weight on the beginning (0w) and 6th weeks (6w) between N- and CSD-group. On the 6th week, body weight of the CSD-group rat was obviously lower than that of the N-group. (C) Echocardiography evaluation of the two groups of rats. EF: ejection fraction, FS: fraction shortening, CO: cardiac output. Myocardial function of the CSD-group was definitely weaker than that of the N-group.
Fig 5
Fig 5. Identification and verification of the candidate proteins involving in CSD.
(A) Western blots display the protein levels of four proteins PKM, CLU, KNG1 and PFN1 in N- and CSD-groups. Coomassie blue staining of the membranes were used as loading control. (B) The relative intensity of the Western blot bands. KNG1, PFN1 and PKM were expressed stronger and CLU was expressed weaker in CSD-group compared with N-group. The data are reported as the mean ± SD of three independent experiments. P < 0.05 was considered significant. (C) Serum levels of the four candidates protein detected by PRM. Expression levels of PKM, KNG1 and PFN1were increased while CLU was decreased in the CSD-group, compared with the N-group.
Fig 6
Fig 6. Validation of the four candidates in myocardial and brain tissues.
Proteomic comparison of PKM, CLU, KNG1 and PFN1 were detected using myocardial tissue (A) and brain tissue (B) samples by LFQ verification.
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Grants and funding

The current thesis was supported by National Natural Science Foundation of China (81573650), the National Key Basic Research Special Foundation of China (2015CB554405) and the Chinese Postdoctoral Science Foundation (2017M621041).

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