Letter pubs.acs.org/ac
Application of LC-MS-Based Global Metabolomic Profiling Methods to Human Mental Fatigue Zhenling Chen,*,† Xianfa Xu,*,† Jianping Zhang,§ Yongsuo Liu,† Xianggang Xu,‡ Lili Li,† Wei Wang,† Haishan Xu,† Wei Jiang,† and Yan Wang† †
Civil Aviation Medicine Center, Civil Aviation Administration of China (Civil Aviation Hospital), Gaojing No. A1, Chaoyang District, Beijing 100123, China § The Second Research Institute of Civil Aviation Administration of China, 2nd Ring Road, South Section 2, No. 17, Chengdu, Sichuan Province 610041, China ‡ Civil Aviation Management Institute of China, Huajiadi East Road No. 3, Chaoyang District, Beijing 100102, China S Supporting Information *
ABSTRACT: Mental fatigue is characterized by a reduced capacity for work and a loss of capacity to respond to stimulation and is usually accompanied by a feeling of tiredness and drowsiness. Mental fatigue at work is a serious problem and can raise safety concerns especially in the transportation system. It is believed that mental fatigue is a direct or contributing cause of road and air related accidents and incidents. Psychological studies indicate that fatigue results in reduced work efficiency, alertness, and impaired mental performance. However, its underlying biochemical mechanisms are poorly understood. We hypothesized that the human body is an integrated system, and mental fatigue results in changes not only in psychology but also in biochemistry of the human body. These biochemical changes are detectable in metabolites. We employed global metabolomic profiling methods to screen biochemical changes that occur with mental fatigue in air traffic controllers (ATCs) in civil aviation. A total of 45, all male, ATCs (two batches) were recruited as two mental fatigue groups and 23 executive staff acted as a control group for this study. The volunteers’ urine samples were collected before and after their work. The samples were analyzed with liquid chromatography/mass spectrometry equipped with a polar, a weak polar, and a nonpolar column, respectively. Three candidate biomarkers were selected on the basis of statistical significance, coefficient of variance, and compared with data of the three groups. The results suggest that urine metabolites may provide a complete new clue from biochemistry to understand, monitor, and manage human mental fatigue.
M
painful phenomenon, arises in overstressed muscles, and is localized.7 It seldom occurs in the modern industry due to automation. On the other hand, mental fatigue includes feelings of indolence and disinclination for any kind of activity and is associated with heavy, drowsy feelings.7 Mental fatigue is believed to be psychological in nature, and there are several psychological changes, such as reduction in alertness and reaction time leading up to prolonged cognitive impairment. Several neurophysiological parameters change with mental fatigue, such as increased delta and theta activity in the brain and Perclose increase.8,9 However, the underlying biochemical mechanisms of mental fatigue are poorly understood. We hypothesized that mental fatigue results in changes not only in psychology but also in biochemistry of the human body because the human body is an integrated system, and these biochemical changes can be detectable in metabolites by employing modern analytical methods.
ental fatigue is a complex problem and has been researched extensively in various disciplines. This condition has been noted in persons involved in prolonged periods of cognitive activity requiring sustained mental activity. It is a common occurrence in 24/7 industries such as road and rail transportation, aviation, healthcare systems, and many other public services. Fatigue-related problems cost an estimated $18 billion a year in terms of lost productivity in the United States.1 Statistical data suggests that 31% of all commercial driver fatalities might be related to fatigue.2 Fatigue among clinicians threatens patients’ safety.3 The concept of fatigue is an age old problem. During the First World War, the Industrial Fatigue Research Board carried out extensive fatigue research in England and found that fatigue resulted in decreased productivity.4 That was the first period of fatigue research. The second period was during the 1940s to 1950s. Extensive research was focused on the pattern of breakdown in skilled performance in aviation due to fatigue.5 The third period has been active since the 1970s. Mental fatigue has been firmly established as a direct or contributing cause for accidents and incidents in the transportation system.6 Scientists began to realize that fatigue included physical and mental fatigue. Physical fatigue, also called muscular fatigue, is an acutely © XXXX American Chemical Society
Received: August 31, 2016 Accepted: November 10, 2016 Published: November 10, 2016 A
DOI: 10.1021/acs.analchem.6b03421 Anal. Chem. XXXX, XXX, XXX−XXX
Letter
Analytical Chemistry Analytical methods have been developing rapidly with the development of nanoscience, computer science, and mass spectrometric technology since the end of the last century,10−17 and these developments have made it possible to learn biochemical changes in organisms at great depth even at the cellular level.18−26 We can even monitor changes and identify compounds in living organisms,27,28 in particular, with the emergence of metabolomics. Metabolomics “measures the fingerprint of biochemical perturbations caused by disease, drugs, and toxins” in order to find related biomarkers.29,30 These biomarkers help us to understand these biochemical perturbations better and contribute to diagnoses and therapy of disease, discovery of new drugs, and evaluation of toxins. For example, Mapstone et al., using metabolomic methods, discovered a set of ten lipids from the blood to predict phenoconversion to amnestic mild cognitive impairment and Alzheimer’s disease within a 2−3 year time frame with over 90% accuracy.31 Such results suggest that metabolomics is a very sensitive and selective analytical method for the detection of biochemical perturbation in organisms. In this paper, for the first time, we made attempts to expand metabolomics to mental fatigue studies. We recruited air traffic controllers (ATCs) in civil aviation as research objects for mental fatigue. We excluded physical fatigue because their typical work included sitting at their positions and directing the flow of air traffic. We considered that ATCs in civil aviation suffer mental fatigue for their high workload and stressful work environment. Generally, ATCs are involved in a large amount of multitasking in which they must utilize all the different information available as well as draw on past experiences and memory to make the right decisions at all times.32 The Federal Aviation Administration (FAA) defined fatigue as “a condition characterized by increased discomfort with lessened capacity for work, reduced efficiency of accomplishment, loss of power or capacity to respond to stimulation, and usually accompanied by a feeling of weariness and tiredness”.33 Previous research results show that the 8 h shift work results in fatigue among ATCs.32,34 At first, 20 male ATCs in civil aviation who worked at a single busy international airport with a throughput of about 800 flights per day based on two runways were recruited. Their urine samples were collected before and after their 8 h shift (group: ATC1) in the winter. The samples were analyzed with ultraperformance liquid chromatography/quadrupole time-of flight mass spectrometry (UPLC/Q-TOF MS). For the chromatography part, three kinds of columns including a nonpolar PFPP column, a weak-polar C18 column, and a polar HILIC column were applied to separate and detect nonpolar, weak-polar, and polar components in urine metabolites. For the mass spectrometry part, the electrospray ionization positive (ESI+) and electrospray ionization negative (ESI−) modes were employed in the full scan mode. This analytical strategy made metabolites in the urine samples detectable as complete as possible. As shown in Figure 1, 11 414 compounds were detected on the PFPP column with ESI+, 6176 compounds on the PFPP column with ESI−, 10 520 compounds on the C18 column with ESI+, 4980 compounds on the C18 column with ESI−, and 6732 compounds on the HILIC column with ESI+, respectively. The study of metabolomics in this study incorporated studying the processes of all metabolites in the before and after urine samples, thereby revealing mental fatigue-related metabolic pathways. The UPLC/Q-TOF MS data were processed using Progenesis QI software which was developed
Figure 1. Typical total ion chromatography (TIC) of samples on the HILIC column with ESI+, C18 column with ESI+, C18 column with ESI−, PFPP column with ESI+, and PFPP column with ESI−.
for processing metabolic profiling data. The samples collected before work were detected as the pretest, and the samples collected after work were detected as the post-test. The acquired metabolic data were applied to perform orthogonality partial least-squares-discriminant analysis (OPLS-DA) and a nonparametric test to detect the difference between before and after mental fatigue (Figure 2 and Figures S-1 to S-5).
Figure 2. Score plots of data analyses on a HILIC column with ESI+, C18 column with ESI+, and PFPP column with ESI+ for three groups.
All chromatographic peaks were extracted for discovery of metabolic biomarkers associated with mental fatigue. By combining the results of VIP-plots from the OPLS-DA analysis, the UPLC/Q-TOF MS provided the retention time, precise molecular mass, and HMDB database for the identification of biomarkers. A Wilcoxon-Mann−Whitney test was performed, and it was found that 20 ions (VIP > 1, P < 0.05, CV< 30%) significantly changed; 3 compounds were up regulated, and 17 B
DOI: 10.1021/acs.analchem.6b03421 Anal. Chem. XXXX, XXX, XXX−XXX
Letter
Analytical Chemistry
selected on the basis of analyses of the data acquired from the C18 column with ESI+ and the HILIC column with ESI+ (Figures 2 and S-1 to S-15). This result indicated that the three metabolites were probably indicators for individual normal metabolic changes with general works without mental fatigue. Results of index in the Metaboanalyst database showed that 5-hydroxy-L-trytophan belonged to the pathway of tryptophan metabolism (Wikipathways and SMPDB) and urocanic acid belonged to the pathway of histidine metabolism (KEGG and SMPDB) and metabolism of amino acids and derivatives (Wikipathways and Reactone), and no pathway was found related to N4-acetylcytidine. The sought four metabolic pathways of tyrosine, arylamine, tryptophan, and histidine are fully related to sleep,35−39 and mental fatigue is accompanied by a feeling of drowsiness and the tendency to sleep. Compared with executive office staff with only two sleep pathways activated when they finished a day’s work, ATCs who suffered mental fatigue had two more sleep pathways activated. This result suggests that ATCs with mental fatigue may feel more sleepy and have a tendency to sleep more quickly. The advantage of batch collection of samples was to overcome much interference from other factors such as foods, seasons, environments, and so on. For example, 20 candidates of metabolic biomarkers were selected from the first batch (ATC1), and 14 candidates were selected from the second batch (ATC2). Howver, most of these candidates were false positive and had no relationship with mental fatigue. Moreover, the advantage of the design to include a control group was to overcome interference from individual normal metabolism such as the three false positive candidates, 5hydroxy-L-trytophan, urocanic acid, and N4-acetylcytidine, which were selected from all three groups. In conclusion, mental fatigue is a serious problem in the transportation industry especially in aviation transportation. It is crucial to monitor mental fatigue more closely with a noninvasive marker in order to manage mental fatigue for decreasing risk and ensuring aviation safety. Metabolomics provides a powerful approach to discover biomarkers by analyzing global changes in an individual’s metabolic profile. Our research, for the first time, detected a range of metabolites that represented the metabolic regulation of before and after mental fatigue and illustrated the ability of metabolomics to identify the potential biomarkers related to mental fatigue. Using well-designed experiments, many false positive candidates were taken out from candidates of metabolic biomarkers. Three urinary differential metabolites were identified. Future quantitative targeted assays based on the identified biomarker candidates will be required to validate the predictive value of these metabolites. These results demonstrate that highthroughput UPLC/Q-TOF MS metabolomics combined with the proposed bioinformation database is pivotal for elucidating biomarkers. In addition, this also has the potential to be developed into a useful monitoring tool for establishing a profile to ensure aviation safety. The results suggest that urine metabolites may provide a complete new clue from biochemistry to understand, monitor, and manage mental fatigue. This may open an exciting development in the field of mental fatigue research and metabolomic application.
were down regulated (Table S-1). These 20 metabolites were the candidates for metabolic biomarkers related to mental fatigue. We further wanted to rule out normal metabolism difference among the 20 candidates. Hence, we designed one more experiment for further screening out biomarkers from the 20 candidates. Another set of 25 male ATCs was recruited as the second batch (group: ATC2), and 23 executive office male staff who undertook a light workload and light work stress compared with the ATCs and did not have a mental fatigue feeling after a day’s work were recruited as the control group (group: control) at the same airport. The volunteers’ urine samples were collected before and after their 8 h shift work in the autumn. Ninety six urine samples were analyzed with UPLC/Q-TOF MS using the same methods as for the ATC1 group. The acquired UPLC/Q-TOF MS data were processed with the same steps of the ATC1 group for screening out metabolic biomarkers in the ATC2 and the control groups, separately. Fourteen metabolites were screened out for the ATC2 group (Table S-2), and 35 metabolites were screened out for the control group (Table S-3). The selected biomarkers were compared from the three groups. Three metabolites (N2,N2-dimethylguanosine, Nacetylarylamine, and α-CEHC) were screened out only in the two ATC groups with the first 2 metabolites down regulated and the last one metabolite up regulated (Table 1). They were Table 1. Candidates for Urinary Biomarkers Related to Human Mental Fatigue and Normal Metabolic Changes namea
formula
HMDB
trend
N2,N2-dimehtylguanosine*
C17H17N5O5
04284
↓
N-acetylarylamine*
C6H9NO
01250
↓
α-CEHC*
C16H22O4
01518
↑
5-hydroxy-L-tryptophan
C11H12N2O3
00472
↓
urocanic acid
C6H6N2O2
00301
↓
N4-acetylcytidine
C11H15N3O6
05923
↓
selected group ATC1 ATC2 ATC1 ATC2 ATC1 ATC2 ATC1 ATC2 control ATC1 ATC2 control ATC1 ATC2 control
a
Note: Candidates for biomarkers related to human mental fatigue indicated with star.
selected on the basis of the analyses of the data acquired from the C18 column with ESI+ and the PFPP column with ESI+ (Figures 2 and S-1 to S-10). This result indicated that the three metabolites were probably potential biomarkers related to mental fatigue. Analyses of the relevant pathways of the three metabolites were performed on the Metaboanalyst database. Results showed that N2,N2-dimethylguanosine belonged to the pathway of tyrosine metabolism (KEGG) and N-acetylarylamine belonged to the pathway of arylamine metabolism (Wikipathways), and no pathway was found related to αCEHC. Moreover, another three metabolites (5-hydroxy-Ltrytophan, urocanic acid, and N4-acetylcytidine) were selected and down regulated in all three groups (Table 1). They were C
DOI: 10.1021/acs.analchem.6b03421 Anal. Chem. XXXX, XXX, XXX−XXX
Letter
Analytical Chemistry
■
(24) Liu, Y.; Zhang, J.; Nie, H.; Dong, C.; Li, Z.; Zheng, Z.; Bai, Y.; Liu, H.; Zhao, J. Anal. Chem. 2014, 86, 7096−7102. (25) Feng, B.; Zhang, J.; Chang, C.; Li, L.; Li, M.; Xiong, X.; Guo, C.; Tang, F.; Bai, Y.; Liu, H. Anal. Chem. 2014, 86, 4164−4169. (26) Wang, X.; Li, X.; Li, Z.; Zhang, Y.; Bai, Y.; Liu, H. Anal. Chem. 2014, 86, 4739−4747. (27) Liu, K.; Yu, P.; Lin, Y.; Wang, Y.; Ohsaka, T.; Mao, L. Anal. Chem. 2013, 85, 9947−9954. (28) Li, X.; Zhao, L.; Chen, Z.; Lin, Y.; Yu, P.; Mao, L. Anal. Chem. 2012, 84, 5285−5291. (29) Mishur, R. J.; Rea, S. L. Mass Spectrom. Rev. 2012, 31, 70−95. (30) Lindon, J. C.; Holmes, E.; Nicholson, J. K. Anal. Chem. 2003, 75, 384A−391A. (31) Mapstone, M.; Cheema, A.; Fiandaca, M.; Zhong, X.; Mhyre, T.; MacArthur, L.; Hall, W.; Fisher, S.; Peterson, D.; Haley, J.; Nazar, M.; Rich, S.; Berlau, D.; Peltz, C.; Tan, M.; Kawas, C.; Federoff, H. Nat. Med. 2014, 20, 415−421. (32) Nealley, M. A.; Gawron, V. J. Int. J. Aviat. Psychol. 2015, 25, 14− 47. (33) Salazar, G. J. Fatigue in Aviation, Series: Medical Facts for Pilots; OK-07-193; FAA Civil Aerospace Medical Institute: Oklahoma City, OK, 2007. (34) Hancock, P. A.; Verwey, W. B. Accid. Anal. And Prev. 1997, 29, 495−506. (35) Wang, G.; Lee, C.; Lee, E. Chronobiol. Int. 2004, 21, 229−237. (36) Zheng, W.; Cole, P. Curr. Med. Chem. 2002, 9, 1187−1199. (37) Silber, B.; Schmitt, J. Neurosci. Biobehav. Rev. 2010, 34, 387− 407. (38) Thakkar, M. Sleep Med. Rev. 2011, 15, 65−74. (39) Capuron, L.; Schroecksnadel, S.; Feart, C.; Aubert, A.; Higueret, D.; Barberger-Gateau, P.; Laye, S.; Fuchs, D. Biol. Psychiatry 2011, 70, 175−182.
ASSOCIATED CONTENT
S Supporting Information *
The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.analchem.6b03421. Experimental details and score plots of analyses of the three group data (PDF)
■
AUTHOR INFORMATION
Corresponding Authors
*E-mail:
[email protected]. Phone: 86-10-85762244-2322. Fax: 86-10-8576-9128 (Z.C.). *E-mail:
[email protected]. Phone: 86-10-8578-2661. Fax: 86-10-8575-2691 (X.X.). Notes
The authors declare no competing financial interest.
■
ACKNOWLEDGMENTS We acknowledge the financial support of the Joint Funds of the National Natural Science Foundation of China and the Civil Aviation Administration of China (No. U1333132), Major Science and Technology Programs of Civil Aviation Administration of China (MHRD20140101), and Safety Foundation of Civil Aviation Administration of China (TMSA 1608).
■
REFERENCES
(1) Caldwell, J. A. Air Med. J. 2001, 20, 25−32. (2) Lockridge, D. Understanding sleep science will help you recognize signs of fatigue. Newport’s Roadstar, 1999, Vol. 1, p 1−35. (3) Gaba, D. M.; Howard, S. K. N. N. Engl. J. Med. 2002, 347, 1249− 1255. (4) Chambers, E. A. Occup. Psychol. 1961, 35, 44−57. (5) Anon. U. S. Civil Aeronautics Administration. The role of fatigue in pilot performance. Report No. 61; CAB: College Park, MD, 1946. (6) Cameron, C. Ergonomics 1973, 16, 633−648. (7) Grandjean, E. Occup. Environ. Med. 1979, 36, 175−186. (8) Lal, S. K. L.; Craig, A. Psychophysiology 2002, 39, 313−321. (9) Ji, Q.; Zhu, Z.; Lan, P. IEEE Trans. Veh. Technol. 2004, 53, 1052− 1068. (10) Zhang, Y.; Li, X.; Nie, H.; Yang, L.; Li, Z.; Bai, Y.; Niu, L.; Song, D.; Liu, H. Anal. Chem. 2015, 87, 6505−6509. (11) Lin, Y.; Yu, P.; Hao, J.; Wang, Y.; Ohsaka, T.; Mao, L. Anal. Chem. 2014, 86, 3895−3901. (12) Huang, P.; Wu, F.; Mao, L. Anal. Chem. 2015, 87, 6834−6841. (13) Frost, D. C.; Greer, T.; Li, L. Anal. Chem. 2015, 87, 1646−1654. (14) Chen, Z.; Dai, Y.; Dong, Z.; Li, M.; Mu, X.; Zhang, R.; Wang, Z.; Zhang, W.; Lang, J.; Leng, J.; Jiang, X. Integr. Biol. 2012, 4, 1090−1095. (15) Chen, Z.; Li, Y.; Liu, W.; Zhang, D.; Zhao, Y.; Yuan, B.; Jiang, X. Angew. Chem., Int. Ed. 2009, 48, 8303−8305. (16) Wang, X.; Liu, Y.; Chen, Z.; Li, Y.; Sun, K.; Jiang, X. J. J. Mater. Sci. 2014, 49, 4394−4398. (17) Phan, N.; Mohammadi, A.; Dowlatshahi Pour, M.; Ewing, A. Anal. Chem. 2016, 88, 1734−1741. (18) Wang, J.; Trouillon, R.; Dunevall, J.; Ewing, A. Anal. Chem. 2014, 86, 4515−4520. (19) Chen, R.; Huo, L.; Shi, X.; et al. ACS Nano 2014, 8, 2562−2574. (20) Gemperline, E.; Rawson, S.; Li, L. Anal. Chem. 2014, 86, 10030−10035. (21) Li, L.; Wang, W.; Ding, M.; Luo, G.; Liang, Q. Anal. Chem. 2016, 88, 6734−6742. (22) Chen, Z.; Chen, W.; Yuan, B.; Xiao, L.; Liu, D.; Quan, B.; Wang, J.; Ibrahim, K.; Wang, Z.; Zhang, W.; Jiang, X. Langmuir 2010, 26, 17790−17794. (23) Gemperline, E.; Keller, C.; Li, L. Anal. Chem. 2016, 88, 3422− 3434. D
DOI: 10.1021/acs.analchem.6b03421 Anal. Chem. XXXX, XXX, XXX−XXX
