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Article

METABOLOMIC APPROACH WITH LC-MS REVEALSSIGNIFICANT EFFECT OF PRESSURE ON DIVER’S PLASMA

Michal Ciborowski, Francisco J. Ruperez, Mª Paz Martinez_Alcazar, SantiagoAngulo, Piotr Radziwon, Romuald Olszanski, Janusz Kloczko, and Coral Barbas

J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/pr100331j • Publication Date (Web): 26 May 2010

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METABOLOMIC APPROACH WITH LC-MS REVEALS SIGNIFICANT EFFECT OF 1

PRESSURE ON DIVER’S PLASMA 2

3

Michal Ciborowskia,b

, F. Javier Rupéreza, Mª Paz Martínez-Alcázar

a, Santiago Angulo

a, Piotr 4

Radziwonc,d

, Romuald Olszanskie, Janusz Kloczko

d, Coral Barbas

a* 5

6

a Pharmacy Faculty, Campus Monteprincipe, San Pablo-CEU University, 28668 Boadilla del 7

Monte. Madrid, Spain. 8

b Department of Physical Chemistry, Medical University of Bialystok, Kilinskiego 1, 15-089 9

Bialystok, Poland 10

cRegional Centre for Transfusion Medicine, Sklodowskiej 23, 15-950 Bialystok. Poland 11

dDepartment of Haematology, Medical University of Bialystok, Kilinskiego 1, 15-089 12

Bialystok, Poland 13

eMilitary Institute of the Health, Department of Maritime and Tropical Medicine, Gdynia, Poland 14

15

16

17

18

19

20

* To

whom correspondence should be addressed: Coral Barbas, Pharmacy Faculty, Campus 21

Monteprincipe, San Pablo-CEU University, 28668 Boadilla del Monte. Madrid, Spain, 22

tel:0034913724711 , fax: 0034913724712, e-mail: cbarbas@ceu.es 23

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ABSTRACT 24

Professional and recreational diving are growing activities in modern life. Diving has been 25

associated with increased prevalence of stroke, hypertension, asthma, diabetes, or bone necrosis. 26

We evaluated the effect of increased pressure equivalent to diving at 30 m and 60 m for 30 min 27

in two groups of divers using an untargeted approach with LC-MS fingerprinting of plasma. We 28

found over 100 metabolites to be altered in plasma post exposure and after the corresponding 29

decompression procedures. Among them, a group of lysophosphatidylcholines and 30

lysophosphatidylethanolamines were increased, including lysoplasmalogen, a thrombosis 31

promoter, together with changes in metabolic rate-associated molecules such as acylcarnitines 32

and haemolysis-related compounds. Moreover, three metabolites that could be associated to bone 33

degradation show different intensities between experimental groups. Ultimately, this non-34

targeted, short-term study opens the possibility of discovering markers of long-term effect of 35

pressure that could be employed in routine health control of divers and could facilitate the 36

development of safer decompression procedures. 37

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INTRODUCTION 38

Divers are repeatedly exposed to increased pressure, which cause several physiological and 39

pathophysiological effects, the best known of these being decompression sickness (DCS) (1). 40

Understanding of mechanisms responsible for DCS and other complications of diving has 41

become of great importance since the number of recreational scuba divers increases every year. 42

Professional Association of Diving Instructors (PADI) certifies about 500 000 new divers 43

annually (2). It is estimated that approximately 7 million people are active sport divers 44

worldwide (2), and among them 4 million are in the United States alone (3). 45

DCS is a disorder, which may develop in a short term after diving, caused by the evolution of gas 46

bubbles in tissues and blood following exposure to increased pressure. During a dive ambient 47

pressure increases and inert gases (mainly nitrogen) become dissolved in blood and tissues. 48

When divers reach the surface the partial pressure of gas in the blood and tissues exceeds 49

ambient pressure, and dissolved nitrogen may form bubbles that cause mechanical tissue injury, 50

vascular occlusion, activation of coagulation cascade, activation of fibrinolytic system and 51

inflammatory mediators (4, 5). The symptoms of DCS that may develop in divers vary from skin 52

rash and limb or joint pain, to neurologic, vestibular, or pulmonary systems disturbances, 53

depending on the location and scale of the appearing bubbles (3,6). Besides the risk of DCS, 54

diving is contraindicated in several medical conditions (e.g. coronary disease, hypertension, 55

asthma or diabetes) (3). There have been also reported cases of stroke (7, 8), pulmonary edema 56

(9), or bone necrosis (10), as long term consequences of diving. 57

Metabonomics, the quantitative measurement of the dynamic multi parametric metabolic 58

response of living systems to physiological stimuli or genetic modification (11), seems to be a 59

good way of extending knowledge of pathophysiology of DCS and other diseases related to 60

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diving. One of the approaches of metabol(n)omics is metabolic fingerprinting, which looks into a 61

total profile, or fingerprint, as a unique pattern characterizing a metabolism in a particular case 62

(12). Analytical techniques that are mainly used in this type of studies are nuclear magnetic 63

resonance (NMR), gas chromatography-mass spectrometry (GC-MS), high performance liquid 64

chromatography-mass spectrometry (HPLC-MS), or capillary electrophoresis-mass spectrometry 65

(CE-MS). Their strengths and drawbacks have been extensively reviewed and discussed (13-16). 66

Profiles are then analyzed and compared by multivariate statistics and bio-informatics tools in 67

order to find metabolites that discriminate samples (12). 68

Currently it is more and more evident that there is not one single perfect tool for global 69

metabolic profiling. LC-MS clearly represents an important component of the evolving 70

‘‘metabol(n)omics toolbox’’ with advantages of sensitivity and good potential for biomarker 71

identification. The broad applicability of LC-MS to metabolites of all classes justified the choice 72

for this first approach to the problem under consideration. 73

In our study, for the first time, we applied untargeted MS-based metabol(n)omics to compare the 74

plasma profiles of divers before and after a period in hyperbaric chamber. The experiment 75

provided information of both short term and long term effects because measurements were 76

performed just after one single pressurization and decompression cycle but working with 77

professional divers. Plasma profiles were obtained with liquid chromatography coupled to 78

accurate mass quadruple time-of-flight MS detector (LC-QTOF-MS). 79

80

MATERIALS AND METHODS 81

Investigated group 82

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Twelve healthy male divers, aged 18-40 years, who had not taken any drugs for at least 2 weeks 83

prior to blood sampling, underwent saturated air dives, simulated in a hyperbaric chamber. 84

Hyperbaric exposures to the pressure of either 400 kPa (corresponds to 30 meters of sea water, 85

msw) – group I (n=6) – or 700 kPa (corresponds to 60 msw) – group II (n=6) – with 30 min of 86

bottom time followed by a staged decompression were carried out in decompression habitat 87

DGKN-120 at the Department of Diving and Underwater Work Technology, Naval Academy in 88

Gdynia, Poland. Only air was breathed during these exposures. The water depth of 60 msw 89

represents the lower limit for dives with air as a breathing medium. Decompressions were carried 90

out according to the Polish Navy decompression tables. The details of the performed hyperbaric 91

expositions and the decompression profiles are presented as supplementary information 92

(Suplementary Table 1). The experiment was developed at room temperature and without 93

physical exertion. The divers were monitored for clinical symptoms of DCS and checked for 94

Doppler-detected venous gas bubbles as a risk factor for DCS using Doppler Bubble Monitor 95

(DBM9610, Canada). The investigations were carried out with the permission of the Bioethical 96

Committee of the Medical University of Bydgoszcz after obtaining the written informed consent 97

of the divers who participated in the study. 98

99

Blood collection and sample preparation 100

Venous blood was drawn with minimal stasis from divers before and 15 min after 101

decompression, into syringes containing sodium citrate (0.32% final concentration). The first 4 102

ml of blood were discarded. Platelet poor plasma was prepared by centrifugation of whole blood 103

at 2000 x g for 10 min. 104

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Protein precipitation and metabolite extraction was performed by adding 1 volume of plasma to 3 105

volumes of cold (-20ºC) mixture of methanol and ethanol (1:1). Samples were then vortex-mixed 106

and stored at –20 °C for 5 min. The pellet was removed by centrifuging at 16 000 g for 10 min at 107

4ºC, and supernatant was filtered through 0.22 µm nylon filter. 108

Quality control (QC) samples were prepared by pooling equal volumes of plasma from each of 109

the 24 samples. Five samples were independently prepared from this pooled plasma following 110

the same procedure as for the rest of samples. QC samples were analyzed throughout the run in 111

order to provide a measurement not only of the system’s stability and performance (17), but also 112

of the reproducibility of the sample treatment procedure. 113

Metabonomics Fingerprinting with ESI-QTOF-MS 114

The HPLC system consisted of a degasser, two binary pumps, and autosampler (1200 series, 115

Agilent); 10µL of extracted plasma sample was applied to a reversed-phase column (Discovery 116

HS C18 15cm x 2.1mm, 3µm; Supelco) with a guard column (Discovery HS C18 2cm x 2.1mm, 117

3µm; Supelco). The system was operated in positive ion mode at the flow rate 0.6 mL/min with 118

solvent A composed of water with 0.1% formic acid, and solvent B composed of acetonitrile 119

with 0.1% formic acid. The gradient started from 25% B to 95% B in 35 min, and returned to 120

starting conditions in 1 min, keeping the re-equilibration at 25% B for 9 min. Data were collected 121

in positive ESI mode in separate runs on a QTOF (Agilent 6520) operated in full scan mode from 122

50 to 1,000 m/z. The capillary voltage was 3,000 V with a scan rate of 1.02 scan per second; the 123

nebulizer gas flow rate was 10.5 L/min. 124

The resulting data file was cleaned of extraneous back-ground noise and unrelated ions by the 125

Molecular Feature Extraction (MFE) tool in the Masshunter Qualitative Analysis Software. The 126

MFE then created a listing of all possible components as represented by the full TOF mass 127

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spectral data. Exact mass databases quoted below were then searched for hits to identify the 128

compounds. 129

Primary data treatment (filtering and alignment) was performed with GeneSpring MS 1.2 130

(Agilent) software. Features were filtered by choosing the data that had "present"

or "marginal" 131

calls in minimum n-1 samples under any condition (5 for divers before or after staying in the 132

hyperbaric chamber in Groups I or II, and 11 for divers before or after the chamber when both 133

groups were combined together). In total 1,129 features (out of 17,113) were selected for the 134

further data treatment. Differences between plasma samples before and after the experiment were 135

evaluated for individual metabolites by using a pair t test (p ≤ 0.05), assuming unequal variance 136

(Welch’s t test), calculated by using Excel (Microsoft). SIMCA-P (Umetrics) was used for 137

multivariate statistical calculations and plotting. Accurate masses of features representing 138

significant differences were searched against the METLIN, KEGG, LIPIDMAPS and HMDB 139

databases. 140

Compound Identification 141

The identity of compounds that were found to be significant in class separation was confirmed 142

by LC/MS/MS by using a QTOF (model 6520, Agilent). Experiments were repeated with 143

identical chromatographic conditions as in the primary analysis. Ions were targeted for collision-144

induced dissociation (CID) fragmentation on the fly based on the previously determined accurate 145

mass and retention time. Comparison of the structure of the proposed compound with the 146

fragments obtained can confirm the identity. Accurate mass data and isotopic distributions for 147

the precursor and product ions can be studied and compared to spectral data of reference 148

compounds, if available, obtained under identical conditions for final confirmation (HMDB, 149

METLIN). Docosahexaenoic acid was confirmed by comparison of retention time and isotopic 150

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distribution of commercially (Sigma) available standard. Lysophosphatydylethanolamines and 151

lysophosphatydylcholines were also confirmed with characteristic fragments described in 152

literature (18). 153

154

RESULTS 155

Quality control of the methodology 156

The application of pressures equivalent to that experienced at 30 and 60 m during 30 min on two 157

groups of divers followed by the corresponding decompression procedures caused a clear and 158

consistent effect on the composition of the plasma, as can be observed in the PLS-DA model 159

built without any filtering of the variables generated as molecular features (17,113 variables in 160

total) in the LC-MS system. 161

The robustness of the analytical procedure was evident by the tight clustering of quality control 162

(QC) samples obtained by mixing equal volumes of all the samples. In addition, QCs were 163

located in the center of the plot when sent to be classified by the model (Figure 1) proving that 164

separation between groups is not random, but due to real variability. The quality of the model 165

built for three components was very good with variance explained (R2

=1), and variance 166

predicted (Q2= 0.573). Prediction of the model may be lower due to the noise produced by so 167

many unrelated variables before filtering. 168

Variable selection and identification 169

Considering that each individual acted as their own control, a paired t-test was employed, 170

obtaining 130 features with p < 0.05 differentiating group I: control and 30 m; 159 features for 171

group II: control and 60 m, and 113 features when considering together all the individuals before 172

and after being under the condition. 173

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Results of elucidation are summarized in Tables 1 and 2 including retention time, the mass 174

obtained in the LC-QTOF system and the mass error when comparing with the database. In 175

addition, average signal before entering the chamber, type of the identification (MS/MS 176

fragmentation, confirmation with the analysis of standard or with databases) and percentage of 177

change after leaving the chamber in each group (I (30 m), II (60 m) and both together) are also 178

presented in this table. 179

For the rest of unknown metabolites in Group II, the table with retention time and jack-knifed 180

confidence interval is presented as supplementary information (Supplementary Table 2) 181

including only those with possible value as biomarkers because the interval does not include zero 182

(19). 183

184

DISCUSSION 185

It is commonly accepted that formation of gas bubbles during decompression is responsible for 186

the development of DCS and other complications of diving (1, 2). Intravascular bubbles may 187

damage endothelial cells of blood vessels (20), and may also interact with blood cells. Divers 188

who took part in this study had neither clinical symptoms of DCS nor Doppler-detected venous 189

gas bubbles. Therefore all the changes observed in our experiment were asymptomatic. In 190

addition, the experiment took place at room temperature and without physical exertion of 191

individuals and, therefore, changes produced in metabolites should be explained as due to 192

pressure effect. 193

Phospholipases activity - lysophospholipids 194

From the list of metabolites in Table 1, it can be seen that most of the changes detected have 195

been assigned to variation in lysophospholipids (LPS), both lysophosphatidylethanolamines and 196

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phosphatydilcholines. Although LPS (21) are generally found in very low concentrations (0.5-197

6% of total lipid membrane weight) in biological membranes, LC/MS-QTOF has proven 198

sensitive enough as to obtain the MS/MS spectra in plasma samples and to detect differences in 199

the groups. 200

We have found that, after this simulated diving, there were increased levels of lysophospholipids 201

in Group I (30m) and that these levels were even higher in Group II (60m). These changes must 202

have been the product of phospholipases (PLs). This type of lipases are a ubiquitous group of 203

enzymes that share the property of hydrolyzing a common substrate, phospholipid. The 204

properties of phospholipids that define the aggregation state (micelle, bilayer vesicle, hexagonal 205

array, etc.) strongly determine PL activity. 206

Our results indicate that activities of PLA1, PLA2 and/or PLB could be increased as a result of 207

the experimental procedure. It is not possible to assign these changes to the hyperbaric pressure 208

used in the experiment or to the decompression stages, but changes in the microenvironment of 209

PLs that cleave the fatty acids are probably involved in the changes observed herein, because 210

most of the metabolites which appeared as significantly changed are LPS, the main product of 211

such PLs. 212

It can be assumed that hydrostatic pressure is transmitted throughout the system by the molecules 213

in all directions. Lipid bilayers are compressed and their structures ordered, and this effect 214

mimics that of decreasing temperature (22). It has been experimentally proved that the effect of 215

pressure on protein conformational transition is much smaller than the effect on the phospholipid 216

phase transitions. For example, it has been shown that a change in the physical state of the lipids 217

is a trigger for Azotobacter nitrogenase or Na-K-ATPase, not due to big phase transitions but to 218

changes in the interactions in the lipid annulus around the enzyme (23). 219

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It is noteworthy that all the observed lysophospholipids are increased after 60 m, but a different 220

pattern is observed in the lysophosphatidylethanolamines than in the lysophosphatidylcholines, 221

that may lead us to think of a sequential activation of different phospholipases with different 222

affinities for each type of phospholipids. Related to this we have found a decrease in one of the 223

sphinganines (C17), which are inhibitors of the activity of phospholipases (24). 224

It has now been clearly established that lysophospholipids are not simply intermediates in the 225

metabolism of glycerolipids, but they act as extracellular signaling molecules (25). These lipids 226

bind to a family of related cell-surface heptahelical receptors and are implicated in 227

tumorigenesis, angiogenesis, immunity, atherosclerosis, and neuronal survival. Other diseases 228

where there are links with lysophospholipid levels include inflammation, hyperlipidemia and 229

lethal dysrhythmias in myocardial ischemia. 230

The increased activity of PLA1 will produce an increase in the free fatty acids (FFA), however, 231

FFA are metabolites that will quickly undergo further metabolism, either anabolic or catabolic. 232

Among these, only docosahexaenoic has been found to be significantly decreased after the 233

experiment. Nevertheless, arachidonic acid and docosahexaenoic acid are very important 234

precursors of different signaling molecules (such as resolvins from DHA, and prostaglandins 235

from arachidonic). Related to this, one of the features found significant has been putatively 236

assigned as an endocannabinoid (16,16-dimethyldocosα-cis-5,8,11,14-tetraenoyl) propylamine), 237

which are known metabolites of arachidonic acid. 238

In a group of monoglycerides changes have been found, too. Monoglycerides can appear as the 239

next step in phospholipid metabolism after the subsequent action of a phospholipase A and 240

phospholipase C or vice versa. 241

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Among all the lysophosopholipids one deserves further attention: C21H44NO6P has been 242

putatively assigned as lysoplasmalogen, one ether-linked lysophospholipid with significant 243

implications because it is a precursor for Platelet Activation Factor (PAF) a mediator of many 244

functions, including platelet aggregation, inflammation, and anaphylaxis. One of the symptoms 245

or complications associated to Decompression Sickness is the increase in the fibrinolytic activity 246

(5, 26) that can lead to the cleavage of a potentially artery-blocking thrombus, and here we have 247

found one putative molecule that can be responsible for the increased probability of the 248

generation of a thrombus by platelet aggregation. 249

Haemolysis 250

It is known from more than a century ago, that the lytic compound produced by the cobra venom 251

phospholipase lysolecithin has hemolytic activity directed toward the membranes (27), and high 252

concentrations of lysophospholipids affect membrane properties and membrane enzymes even 253

leading to cell lysis. 254

This may also explain changes in haemoglobin metabolites (increase of bilirubin and decrease of 255

biliverdin and I-urobilin) observed after “diving” in our experiment. As the largest blood 256

corpuscles, red blood cells are the most exposed to those effects. An apparent inconsistency in 257

the result of bilirubin which shows over 400% increase at 30 m but disappears in 60 m can be 258

explained by the longer decompression time in the second group (with the corresponding longer 259

time for sampling after pressure) and the short half life of free bilirubin in plasma (28). 260

Acylcarnitines and energy metabolism 261

Fatty acids are the main source of energy in muscle tissues with a high amount of mitochondria, 262

and the only source in the heart. In the subjects submitted to the experimental conditions of our 263

study a significant change in acylcarnitines has been found, pointing to changes in the metabolic 264

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rate. But as it can be seen that although carnitine and all the derivatives found significant are 265

increased after 30 m, after 60 m carnitine and palmitoylcarnitine increased more whereas 266

decanoylcarnitine, octanoylcarnitine and stearoyl carnitine are decreased. It is difficult to 267

interpret these changes because the uptake of acylcarnitines by the mitochondria is a reversible 268

process that can be used to take out acyl-coenzime A from mitochondria to cytosol. Moreover, 269

long-chain (>C20) and branched fatty acids are not directly uptaken by the Carnitin-Palmitoyl 270

Transferase I, they are metabolized in peroxisomes to acetyl- and octanoylcarnitine. Together 271

with these variations deep changes have been found in a feature that has been assigned from 272

databases information as phosphoenolpyruvic acid, one of the most important intermediate 273

metabolites in glycolisis and gluconeogenesis. 274

In addition, there is a commonly observed “diving bradycardia” caused by pressure affecting the 275

autonomic control of heart rate and therefore affecting to the energy disposal in the heart. 276

All the findings described previously are summarized in Figure 2. 277

Bone degradation 278

It has been reported, that one of the consequences of diving is bone necrosis (10). Our results 279

confirm a correlation between diving and changes in bones metabolism, although it is not 280

possible to assign these changes to the action of pressure or to the decompresion procedures. It 281

must be also noted that the individuals submitted to this experiment were all professional divers, 282

with accumulated experience, and maybe some of the changes can be due to this fact. We 283

observed after the experiment an increase of galactosylhydroxylysine (GHL); an increase of the 284

GHL concentration in plasma is considered a biomarker of bone resorption (29). The other 285

compounds connected with bone metabolism were 1-hydroxyvitamin D3 3-D-glucopyranoside 286

(vitamin D3 derivative), which decreased after being in the hyperbaric chamber, and 1,25-287

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Dihydroxyvitamin D3-26,23-lactone (vitamin D3 metabolite). The second increased after the 288

experiment, which could be a natural defense against bone resorption, as it is known as natural 289

inhibitor of the process of bone resorption (30). 290

Other processes 291

More molecules have been found significant with this metabol(n)omic approach (see table). In 292

some of them putative assignation has been possible but the processes in which they are involved 293

have not been elucidated. Moreover, more than 70 features have not been assigned, neither by 294

fragmentation spectra (in some cases they are very small signals) nor by information from 295

databases. 296

Final considerations 297

The detection of venous gas emboli has been used as a method for validation of decompression 298

procedures (31). However in our study using a metabolomic approach and very sensitive 299

equipment like LC-QTOF-MS we can observe changes in the circulatory system which in the 300

long term may lead to DCS. Therefore, a metabolomic approach could be useful in developing 301

safer decompression procedures. 302

Moreover, a group of metabolites have been detected that could be treated as potential 303

biomarkers of some of the complications associated to long-term diving. Among this, 304

lysoplasmalogen as thrombosis promoter, and GHL, and the ratio between D3 metabolite and D3 305

derivative deserve further studies and maybe the development of more specific, selective and 306

sensitive target assays. 307

308

ACKNOWLEDGEMENTS 309

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The authors acknowledge EADS-CASA, Ministry of Science and Innovation (MICINN) 310

CTQ2008-03779 and Comunidad de Madrid, S-GEN-0247-2006, for funding. 311

312

Supporting Information 313

Table 1 (SI) contains information about pressure and decompression procedures. 314

Table 2 (SI) contains features found significant in the classification with the corresponding Jack-315

knifed confidence interval. These features are still unknown. 316

This information is available free of charge via the Internet at http://pubs.acs.org/ 317

318

References 319

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ed.; Bove, A.A.; Davis, J.C., Eds.; Saunders: Philadelphia, 320

2004; pp 11-35. 321

2. Levett, D.Z.H.; Millar, I.L. Bubble trouble: a review of diving physiology and disease. 322

Postgrad. Med. J. 2008, 84 (997), 571-578. 323

3. Lynch, J.H.; Bove, A.A. Diving Medicine: A review of current evidence. J. Am. Board. Fam. 324

Med. 2009, 22 (4), 399-407. 325

4. Vann, R. In Diving medicine. 4th

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2004; pp 127-164. 327

5. Radziwon, P.; Olszanski, R.; Tomaszewski, R.; Lipska, A.; Dąbrowiecki, Z.; Korzeniewski, 328

K.; Siermontowski, P.; Boczkowska-Radziwon, B. Decreased levels of PAI-1 and alpha2-329

antiplasmin contribute to enhanced fibrinolytic activity in divers. Thromb. Res. 2007, 121 (2), 330

235-240. 331

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Internal cartoid artery dissection and stroke after SCUBA diving: a case report and review of 337

the literature. J. Neurol. 2009, 256 (11), 1916-1919. 338

9. Slade, J.B. Jr.; Hattori, T.; Ray, C.S.; Bove, A.A.; Cianci, P. Pulmonary edema associated with 339

scuba diving. Chest 2001, 120 (5), 1686-1694. 340

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11. Nicholson, J.K.; Lindon, J.C.; Holmes, E. 'Metabonomics': understanding the metabolic 343

responses of living systems to pathophysiological stimuli via multivariate statistical analysis 344

of biological NMR spectroscopic data. Xenobiotica 1999, 29 (11), 1181-1189. 345

12. Shulaev, V. Metabolomics technology and bioinformatics. Briefings Bioinf. 2006, 7 (2), 128-346

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metabonomic approaches for evaluating physiological influences on biofluid composition. 349

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14. Lenz, E.M.; Wilson, I.D. Analytical strategies in metabonomics. J. Proteome Res. 2007, 6 351

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15. Theodoridis, G.; Gika, H.G.; Wilson, I.D. LC-MS-based methodology for global metabolite 353

profiling in metabonomics/metabolomics. Trends Analyt. Chem. 2008, 27 (3), 299-305. 354

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16. Ramautar, R.; Somsen, G.W.; de Jong, G.J. CE-MS in metabolomics. Electrophoresis 2009, 355

30(1), 276-291. 356

17. Gika, H.G.; Macpherson, E.; Theodoridis, G.A.; Wilson, I.D. Evaluation of the repeatibility 357

of ultra-performance liquid chromatography-TOF-MS for global metabolomic profiling of 358

human urine samples. J. Chromatogr. B: Anal. Technol. Biomed. Life. Sci. 2008, 871 (2), 299-359

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lipids by ESI-MS. Methods 2006, 39 (2), 92-103. 362

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Gottfries, J.; Moritz, T.; Trygg, J. Visualization of GC/TOF-MS-based metabolomics data for 364

identification of biochemically interesting compounds using OPLS class models. Anal. Chem. 365

2008, 80 (1), 115-122. 366

20. Nossum, V.; Koteng, S.; Brubakk, A.O. Endothelial damage by bubbles in the pulmonary 367

artery of the pig. Undersea Hyperb. Med. 1999, 26 (1), 1–8. 368

21. Wang, A.; Dennis, E.A. Mammalian lysophospholipases. Biochim. Biophys. Acta 1999, 1439 369

(1), 1-16. 370

22. Swezey, R.R.; Somero, G.N. Pressure effects on actin self-assembly: interspecific differences 371

in the equilibrium and kinetics of the G to F transformation. Biochemistry 1985, 24 (4), 852-372

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23. Heremans, K.; De Smedt, H.; Wuytack, F. Pressure effects on protein-lipid interactions. 374

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24. Merrill, A.H. Jr. l; Sandhoff, K. In New Comprehensive Biochemistry: Biochemistry of 376

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ed; Vance, D.E.; Vance, J.E. Eds.; Elsevier: New 377

York, 2002; pp 291-314. 378

25. Hla, T.; Lee, M.J.; Ancellin, N.; Paik, J.H.; Kluk, M.J. Lysophospholipids-receptor 379

revelations. Science 2001, 294 (5548), 1875-1878. 380

26. Olszanski, R.; Radziwon, P.; Galar, M.; Klos, R.; Kloczko, J. Diving up to 60 m depth 381

followed by decompression has no effect on pro-enzyme and total thrombin activatable 382

fibrinolysis inhibitor antigen concentration. Blood Coagul. Fibrin. 2003, (14) 7, 659-661. 383

27. Wilton, D.C. In Biochemistry of Lipids, Lipoproteins and Membranes 5th

ed; Vance, D.E.; 384

Vance, J.E. Eds.; Elsevier: New York, 2008; pp 311-314. 385

28. Dufour, D.R.; Lott, J.A.; Nolte, F.S.; Gretch, D.R.; Koff, R.S.; Seeff, L.B. Diagnosis and 386

Monitoring of Hepatic Injury. I. Performance Characteristics of Laboratory Tests. Clin. Chem. 387

2000, 46, 2027–2049. 388

29. Al-Dehaimi, A.W.; Blumsohn, A.; Eastell, R. Serum galactosyl hydroxylysine as a 389

biochemical marker of bone resorption. Clin. Chem. 1999, 45, 676-81. 390

30. Ishizuka, S.; Kurihara, N.; Hakeda, S.; Maeda, N.; Ikeda, K.; Kumegawa, M.; Norman, A.W. 391

1,25-DihydroxyvitaminD3[1,25-(OH)2D3]-26,23 lactone inhibits 1,25-(OH)2D3-mediated 392

fusion of mouse bone marrow mononuclear cells. Endocrinology 1988, 123 (2), 781-786. 393

31. Eftedal, O.S.; Tjelmeland, H.; Brubakk, A.O. Validation of decompression procedures based 394

on detection of venous gas bubbles: a Bayesian approach. Aviat. Space. Environ. Med. 2007, 395

78 (2), 94-99. 396

397

Legends to figures 398

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399

Figure 1. Panel A shows the scores plot for a PLS-DA model built with the whole dataset and 400

with prediction for QCs. Quality parameters for the model: Explained variance R2=100%, 401

predicted varianze: Q2=57.3 %. 402

Panels B, C, and D show PCA scores plots for 30 msw, 60 msw, and two groups together, 403

respectively. The quality parameters for those models are R2=44.8%, Q

2=11.7% - panel B, 404

R2=52.0%, Q

2=23.7% - panel C, and R

2=55.9%, Q

2=22.8% - panel D. 405

∆ - Before, ▲ - After staying into the hyperbaric chamber, ○ - Quality control 406

407

Figure 2. Changes due to the effect of pressure on membranes. 408

409

From the information provided by the changes in metabolites, it can be proposed that changes in 410

phospholipases activity are in the origin of several observed effects. 411

P = Pressure; PLs = Phospholipases; FFA = Free Fatty Acids; INF = Inflammation; ATH = 412

Atherosclerosis; NEU = Neuronal Changes; HMY = Haemolysis; THR = Thrombus Formation; 413

pal = palmitic acid; dha = docosahexaenoic acid; blv = biliverdin; blr = bilirubin, ubl = i-414

urobilinogen; lpg = lysoplasmalogen; 415

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Table 1. The identification of lysophospholipids, monoacylglycerides, and haemoglobin metabolites. 416

417

30 m 60 m Both

Groups Compound

RT

(min)

Measured

mass (Da)

Mass

error

(ppm)

Identification

diagnostic [M+H]+ ions

Signal

[x 106]

CV

for

QCs

[%] Change

[%]

Change

[%]

Change

[%]

C25H42NO7P 16.2 499.2701 0.4 500.272; 359.256 0.1 5 + 41* + 71* + 56**

C25H44NO7P 17.5 501.2862 1.20 502.322, 361.265 0.3 47 -79** + 16 - 33†

C23H44NO7P 18.0 477.2868 2.51 478.312; 337.272 3.5 15 + 34† + 83** + 58***

C27H46NO7P 19.0 527.3014 0.38 528.323; 387.284 0.1 4 + 26† + 20† + 22**

C25H46NO7P 19.5 503.3034 4.37 504.325; 363.275 0.08 11 + 83* + 94** + 90***

C21H44NO7P 19.6 453.286 0.88 454.342; 313.271 1.6 9 - 1 + 17* + 8†

C23H46NO7P 20.5 479.3018 1.25 480.334; 339.288 1.3 6 + 30† + 33† + 32*

C21H44NO6P 20.7 437.2905 -0.23 438.288; 420.282, 284.291; 266.282 0.2 42 - 17 + 51** + 11

Ly

so P

E

C23H48NO7P 23.7 481.3173 1.04 482.310; 341.302 1.6 9 - 7† + 16* + 5

C22H46NO7P 15.9 467.3013 0.21 468.307; 184.072; 104.106; 86.096 2.0 8 - 4 + 10* + 3

C24H48NO7P 17.0 493.3172 -0.06 494.472; 184.072; 104.106; 86.096 2.5 11 - 21† + 25* - 3

C23H48NO7P 18.0 481.3169 0.21 482.246; 184.071; 166.05; 104.106; 86.094 0.9 39 - 6 + 23* + 8†

C26H50NO7P 18.4 519.3339 2.70 520.34; 184.071; 104.106; 86.096 52.3 18 - 1 + 28* + 14†

C24H50NO7P 19.2 495.3339 2.83 496.345; 184.072; 104.107; 86.094 13.8 19 - 5† + 11* + 2

C28H52NO7P 19.8 545.3485 0.73 546.352; 184.072; 104.106; 86.096 2.4 35 - 26† + 65** + 20

C30H52NO7P 20.3 569.3475 -1.05 570.306; 184.072; 104.107; 86.096 0.1 27 + 10 + 57* + 37*

C30H54NO7P 21.4 571.3642 0.70 572.383; 184.073; 104.106; 86.098 0.1 25 - 18 + 86** +29†

C28H54NO7P 22.1 547.3609 -5.3 548.304; 184.072; 104.106; 86.097 0.4 26 - 8 + 53** + 23†

Lyso

PC

C28H56NO7P 25.0 549.3792 -0.55 550.383; 184.072; 104.106; 86.095 0.4 17 -18* + 9 - 4

Bilirubin 7.3 584.2633 -0.34 585.268; 568.244; 299.139; 285.123 0.1 53 + 463* - 7 + 101†

Biliverdin 10.5 582.2477 -0.34 583.249; 565.237; 297.12 0.05 24 - 26 - 14 - 21†

I-Urobilin 11.3 590.3096 - 1.36 591.312, 468.243, 343.161, 303.167, 180.100 0.03 20 - 53* + 34 - 47†

C27H42O4 18.2 430.3076 -1.63 431.39; 415.249; 371.219; 311.291 0.08 8 + 1 + 53* + 26*

C17H34O4 22.6 302.2439 -5.96 303.252; 212.234; 91.053 0.05 27 - 18 - 34* - 26†

C19H38O4 27.3 330.2773 0.91 331.283; 313.271; 257.247 1.8 11 - 12† - 18† - 15* MG

C21H42O4 31.6 358.3084 0.28 359.314; 341.302 0.7 11 - 17** - 21† - 19**

No mark – not significant; † - (p ≤ 0.1); * - (p ≤ 0.05), ** - (p ≤ 0.01), *** - (p ≤ 0.001) 418 419

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Table 2. The identification of the other (rest of) significantly changing metabolites. 420

421

30 m 60 m Both

Groups Compound

RT

(min)

Measured

mass (Da)

Mass

error

(ppm)

Identification

diagnostic [M+H]+ ions

Signal

[x 106]

CV

for

QCs

[%]

Change

[%]

Change

[%]

Change

[%]

L-carnitine 0.8 161.1057 3.10 162.112; 103.038; 85.028; 60.081 7.4 21 + 5 + 45† + 23*

L-octanoylcarnitine 4.1 287.2098 0.35 288.214; 85.028 0.2 3 + 42 - 38† + 9

L-decanoylcarnitine 8.2 315.2411 0.32 316.246; 257.173; 155.142; 85.028; 60.087 0.5 2 +45 - 41† + 11

L-palmitylocarnitine 18.6 399.335 0.25 400.343; 327.199, 73.028 0.3 28 + 8 + 43* + 21*

Car

nit

ines

L-stearoylcarnitine 22.1 427.3647 -3.51 428.264, 311.29, 73.03 0.05 75 + 3 - 78* - 72†

Galactosylhydroxylysine 23.0 324.1728 60.16 Databases, Isotopic distribution 0.04 8 + 11 + 91† + 42*

Vit. D3 derivative 23.9 576.3642 - 3.47 577.369; 533.1; 193.048; 149.023 0.03 27 - 59† - 71† - 64*

Vit. D3 metabolite 24.0 444.2836 -9.00 Databases 0.02 4 + 424* - 18 + 73

Docosahexaenoic acid 27.0 328.2417 4.57 Standard 0.1 17 - 11 - 38* - 23*

Palmitic acid 30.5 256.2405 1.17 257.264; 239.146; 57.07 3.3 7 + 5 - 10* - 2

Oxo-heneicosanoic acid 32.3 340.2977 0.29 341.261; 322.25; 240.23; 102.127 1.1 13 - 16* - 14 - 15†

Indoleacrylic acid 1.0 187.063 -1.63 188.069, 170.058, 146.058, 144.079, 118.065 2.3 26 - 26 - 68* - 47*

Decanoic acid 16.0 172.1461 - 1.16 173.138, 145.088, 103.071, 88.018, 74.017,

59.047 0.2 21 - 7 - 19** - 12

C17 Sphinganine 10.5 287.2826 0.7 288.288, 106.086, 88.076, 57.07 1.7 16 - 37† - 15 - 26*

Phosphoenolypruvic acid 0.7 167.981 - 8.3 Databases, Isotopic distribution 0.01 23 + 58 - 100* - 48

Deoxyuridine-diphosphate 0.6 388.013 14.9 Databases, Isotopic distribution 0.02 9 - 41* - 54** - 47***

pentadecatetraenal 6.0 218.167 - 0.5 Databases, Isotopic distribution 2.5 21 - 5 + 69* + 25

16,16-dimethyldocosα-cis-5,8,11,14-

tetraenoyl) propylamine 18.4 401.3505 - 38.1 402.3, 385.873, 283.258 0.7 4 + 9 - 17** - 5

Diacylglycerophosphocholine 18.3 589.314 102.4 Databases, Isotopic distribution 0.2 33 + 27 + 87* + 58*

Tripeptide (ARG, ARG, LEU/ILE) 16.0 443.361 144.5 Databases, Isotopic distribution 0.4 8 - 15 - 26* - 21*

Tripeptide (ARG, LEU/ILE, LYS) 12.1 415.3296 93.6 416.315, 399.215, 371.223, 73.028 1.2 6 - 14 - 25† - 20*

Oth

ers

2-dodecendioic acid 0.7 228.1473 48.6 Databases, Isotopic distribution 0.2 2 0 + 106* + 43

Unknown_1 0.6 225.9449 - 226.962, 209.162, 100.111, 90.976 1.7 4 + 5† + 3 + 4*

Unknown_2 0.6 565.8826 - 566.905, 532.917, 502.894, 384.926, 210.145 0.2 2 - 3 + 127* + 50

Unknown_3 0.8 131.9797 - 132.986, 103.053, 67.934 1.6 13 + 23 + 61* + 42*

Unknown_4 0.8 153.9617 - 154.966, 139.002, 112.958, 97.934, 84.963 0.8 1 - 66† - 100* - 83**

Unknown_5 0.8 438.0359 - 439.044, 258.979, 154.968 0.2 21 - 45 + 215** + 37 Un

kn

ow

n

Unknown_6 27.1 268.2193 - 269.227, 240.152, 195.121, 73.028 0.7 3 - 7† - 4† - 5*

No mark – not significant; † - (p ≤ 0.1); * - (p ≤ 0.05), ** - (p ≤ 0.01), *** - (p ≤ 0.001) 422

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SUMMARY 423

424

We evaluated the effect of increased pressure equivalent to diving at 30 m and 60 m for 30 min in two groups of divers with a 425

metabolomic approach with LC-MS. Over 100 metabolites were altered in plasma post exposure: a group of lysophosphatidylcholines 426

and lysophosphatidylethanolamines were increased, including lysoplasmalogen, a thrombosis promoter, together with changes in 427

acylcarnitines, haemolysis-related compounds and three metabolites that could be associated to bone degradation.428

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429

30 msw

60 msw

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Figure 1. Partial least square discriminant analysis (PLS-DA) and principal component analysis (PCA) of plasma metabolites profiles before and after diving.

∆ - Before diving, ▲ - After diving, ○ - Quality control

Panel A shows PLS-DA model for whole dataset with prediction for quality control samples, R2=1, Q2=0.573. Panels B, C, and D show PCA plots for 30 msw, 60 msw, and two groups together, respectively. The parameters for those models are R2=0.448, Q2=0.117 - panel B, R2=0.52,

Q2=0.237 - panel C, and R2=0.559, Q2=0.228 - panel D.

296x477mm (96 x 96 DPI)

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Figure 2. Changes due to the effect of pressure on membranes 48x26mm (600 x 600 DPI)

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