DEFINING SEX DIFFERENCES IN SELECTED LIPID METABOLITES
OF BLOOD PLASMA IN WISTAR RATS

Sex differences have long been recognized for apparent features of living things and nowadays, genomics and metabolomics have pointed to evidence of these differences at the DNA, RNA, protein and metabolite levels. It has been established that there are significant differences between sexes in respect to the occurrence, prevalence, age of onset, symptoms, and severity of diseases, such as neurodegenerative (1) and cardiovascular disorders (2, 3), diabetes mellitus type 2 as well as chronic kidney diseases and renal dysfunctions (4-7). Sex differences in brain functions, and/or in sex-typed behavior and sex identity have to be studied at all points in the life span (8-10).

Animal models provide an important tool to adopt potential therapies from preclinical studies to humans (11). Mammals, such as mice or rats, widely used in preclinical experiments are used to be males. Researchers usually avoid using female animals because of their reproductive cycles and hormone fluctuations that may confound the results of their studies (12, 13). However, many studies do not consider sexual dimorphism. Recent investigations have taken into account sex specificity of susceptibility to disease and pharmacokinetics (1) but only few studies have examined molecular differences in blood (14, 15) and urine of healthy subjects (16, 17). These studies are evaluating various classes of lipids and bring partial insight into the sex-specific metabolism.

Probably the most important substances affecting lipid metabolism are sex hormones. Estrogens have effects in many organ systems that contribute to cardiovascular risk/protection, including regulation of liver lipid metabolism and serum lipoprotein levels (12). It has been shown that also the favorable effect of cold swimming on the cardiovascular risk factors may be sex-dependent (18). However, sex differences are not limited only to the reproductive system but also include the differences in the structure and function of various organ systems (12). Nevertheless, there are some fundamental aspects of metabolic homeostasis, regulated differently concerning to sex (19, 20). As we know, females generally have a higher percentage of body fat than males. Also, females store more fat in the gluteal-femoral region, whereas males store more fat in the visceral (abdominal) depot. There are pronounced regional differences in the regulation of regional fatty acid metabolism between sexes (21, 22). Therefore, our study aimed to reveal the main variances in selected groups of lipid metabolites: lysophosphatidylcholines (lysoPCs), phosphatidylcholines (PCs) and sphingomyelins (SMs) using liquid chromatography-tandem mass spectrometry-based targeted metabolomics measurements in the blood plasma of healthy rats.

MATERIALS AND METHODS

Experimental design

In the experiment, 10 females and 10 males of Wistar rats (Dobra Voda, Slovak Republic) aged 60 days were used. Animals were kept under standard conditions with a room temperature of 21 – 24°C, relative humidity of 50 – 65% and a 12:12 hour light: dark regimen. Animals were fed with a standard rat pelleted diet (Peter Misko, Snina, Slovakia) ad libitum according to EU animal feed legislation and guidance, and tap water was freely available.

The animals were handled by the guidelines established by Law No. 377 and 436/2012 of Slovak Republic for the Care and Use of Laboratory Animals (Ro-2866/16-221).

Blood collection and metabolomics measurement

The blood from all experimental animals was collected at one time point (10:00 am) from great saphenous vein (vena saphena magna) in a total volume of 100 µL into microtubes with heparin. The place of collection was pre-shaved and treated with a disinfectant. After isolating, blood plasma was stored at –80°C. Frozen plasma was thawed on ice, centrifuged and the supernatant was used for further analysis. The samples were measured by AbsoluteIDQ p180 kit in the laboratory of BIOCRATES Life Sciences AG in Innsbruck (Austria). Flow injection analysis (FIA) and liquid chromatography-tandem mass spectrometry-based (LC-MS/MS) targeted metabolomics measurement of a selected group of lysoPCs, PCs and SMs was performed on plasma samples. The fully automated assay was based on PITC (phenylisothiocyanate) derivatization in the presence of internal standards followed by FIA-MS/MS and LC-MS/MS using a SCIEX 4000 QTRAP® (SCIEX, Darmstadt, Germany) or a Waters XEVO™ TQMS (Waters, Vienna, Austria) instrument with electrospray ionization. The assay was based on the principle described in the study of Pena et al. (23). Determined values were log2-transformed to obtain normally distributed data and to stabilize the variance.

Statistical analysis

Quantification of metabolite concentrations and quality assessment was performed using the MetIQ software package (BIOCRATES Life Sciences AG, Innsbruck, Austria). Internal standards served as the reference for the metabolite concentration calculations. Univariate (t-test) and multivariate statistics (partial least squares-discrimination analysis PLS-DA) as well as the variable importance in projection (VIP) plot, were performed using MetaboAnalyst 3.0 (24). Cross validation of PLS-DA classification applied 5 number of components for selection optimal number of components. LOOCV cross validation method was used. It has been considered performance of measures as Accuracy, R2, Q2. As a part of PLS-DA method, a VIP score was measured. VIP score is a measure of a feature’s importance in the PLS-DA model. It summarizes the contribution a feature makes to the model. The VIP score of a feature is calculated as a weighted sum of the PLS weight. PLS weight is the squared correlations between the PLS-DA components and the original feature. Tables, heat map and box plots were performed using GraphPad 6.0 (GraphPad Software, Inc., San Diego, CA, USA) and programming language R (version 3.6.0) with standard library and libraries ggplot2 (version 3.1.1), ggpubr (version 0.2), psych (version 1.8.12) and GGally (version 1.4.0).

RESULTS

As seen on Fig. 1, male rats had significantly higher body mass gain (P < 0.001) throughout the experiment. In the 3rd and 8th experimental week, food intake was monitored. Indeed, the male rats had significantly elevated food intake (P < 0.001). However, there were no differences between male and female groups in food intake per one gram of body weight (Fig. 1B).

Fig. 1. (A) Body mass gain (gram) of male and female animals. (B) Food intake (gram) in the 3rd and 8th experimental week, and food intake calculated per one gram of body weight in corresponding experimental week. Data are expressed as mean ± SD. Significance versus male is by ***P < 0.001.

In total, 104 lipid metabolites were analyzed. From these, 66 has been found to be sex-dependent (Table 1).

Table 1. Lipid metabolites analyzed in the experiment (concentration in µM).

Data are expressed as mean ± SD. Significance versus male is by *P < 0.05; **P < 0.01 and ***P < 0.001, respectively.

As seen in Fig. 2, 8 from totally 14 lysoPCs were influenced by sex. More than half of them had higher levels in females, roundly lysoPC a C18:0, lysoPC a C28:0, lysoPC a C17:0, lysoPC a C20:4, and lysoPC a C26:1. On the other hand, significantly lower concentrations in lysoPC a C20:3, lysoPC a C18:2, and lysoPC a C16:1 were found in females when comparing to males.

Fig. 2. Sex-dependent differences in lysophosphati-dylcholines (lysoPCs) between males and females. The data present the percentages of average. Significance is by *P < 0.05, **P < 0.01 and ***P < 0.001, respectively. LysoPCs with acyl residue a C. The numbers after lysoPC a C report the residue sum.

Overall, 76 PCs were analyzed. In female blood plasma, significantly higher concentrations of 28 different PCs were revealed when compared with males (in the concrete 10 PCs with diacyl residues and 18 with acyl-alkyl residues). On the other hand, 19 PCs were markedly lower in comparison with males (Fig. 3).

Fig. 3. Heat map presenting phosphatidylcholines (PCs) which are significantly different between males and females (five random selected animals from each group). Values of each metabolite are color-coded and represent a ratio to average. PCs with diacyl residue - PC aa; PCs with acyl-alkyl residue - PC ae. The numbers after PC aa/ae report the residue sum.

Up to 73.3% (11/15) of SMs evaluated were sex-specific. The most distinct increase in 91% (10/11) of them has been revealed in female blood plasma, compared with males (Fig. 4). Females had statistically markedly increased SMs, specifically SM (OH) C22:1, SM (OH) C22:2, SM C18:0, SM C18:1, SM C24:0, SM C26:1, SM (OH) C24:1, SM (OH) C16:1, SM C26:0, and SM C24:1. The only SM C16:0 sphingomyelin was decreased in females compared to males (P < 0.05).

Fig. 4. Differences in sphingomyelins between males and females. The data present the percentages of average. Significance is by *P < 0.05, **P < 0.01 and ***P < 0.001, respectively. Hydroxysphingomyelins with acyl residue - SM (OH) C; sphingomyelin with acyl residue - SM C. The numbers after SM (OH)/SM C report the residue sum.

Significance analysis of microarrays (SAM) is a statistical technique for determining whether changes in metabolites are statistically significant. The data generated is considerable, and a method is essential for sorting out what is significant and what isn’t. SAM identifies statistically significant metabolites by carrying out specific t-tests and computes a statistic dj for each metabolite (25). This analysis uses non-parametric statistics, since the data may not follow a normal distribution. The use of permutation-based analysis accounts for correlations in metabolites and avoids parametric assumptions about the distribution of individual metabolites (26). Table 2 shows the most significant metabolites identified by SAM analysis.

Table 2. Important (most significant) features identified by Significance Analysis of Microarrays (SAM).

As seen on separate cluster analysis (Fig. 5), the data grouped a set of blood lipid metabolites into females and males. VIP plot (Fig. 6) shows the top 15 most important metabolite features identified by PLS-DA, with the most important metabolite PC aa C38:4 with the VIP value of > 2.0. The second most important is PC aa C32:2.

Fig. 5. Partial least squares-discrimination analysis (PLS-DA) of selected lipid metabolites in male and female animals (PLS-DA scores plot includes 2 components, R2 = 0.97492 and Q2 = 0.7297). In the graphical output, 95% confidence ellipses for specific groups are included.

Fig. 6. Variable importance in projection (VIP) plot displays the top 15 most important metabolite features identified by PLS-DA. White and black boxes on right indicate relative concentration of corresponding metabolite in blood. VIP is a weighted sum of squares of the PLS-DA loadings considering the amount of explained Y-variable in each dimension.

DISCUSSION

While the need to include females in preclinical research has become more important, the progress for inclusion remained stagnant (10). Experimental results obtained from research using only one sex are sometimes generally extrapolated to both sexes without previous justification. Basic male biases in preclinical and clinical research were the main reason for the problem (27). Lipids are a class of metabolites that could be associated with many disorders. However, the differences between sexes has not been sufficiently described yet. Therefore, the aim of our study was to reveal main sex-specific features of lipid metabolism in blood of adult Wistar rats. We saw sex-specific pattern of lipid metabolites in all three tested groups - PCs, lysoPCs and SMs.

Proteomics and metabolomics are new addition to the ‘omics’ field, but both are still developing its own computational infrastructure by assessing the computational needs of its own (28). For example, in the analysis of platelet proteome in healthy rats, nano-LC MALDI-TOF/TOF-MS system was used, which allowed authors to identify of various proteins associated with coagulative activity of platelets (29). Metabolomics is a useful tool for analyzing intact metabolism in physiological conditions and is therefore used for search of biomarkers for some diseases and/or responses to drug toxicity (30, 31). Various approaches in metabolomics are used, including lipidomics. Lipid metabolites are not only constituents of cell membranes, but also participate in signal transduction (32). Lipid metabolites are therefore considered as potential biomarkers for diagnosis of various diseases and drug responses (33). Indeed, recent lipidomic studies have shown that lipid metabolites such as eicosanoids and sphingolipids are biomarker candidates for cardiovascular events (34), heart failure (35), traumatic brain injury (33), Alzheimer’s disease (36), diabetes mellitus type 2 (37), and depression (38).

According to VIP score calculated in the PLS-DA analysis, the most important metabolite found in our study was PC aa C38:4. PC is a major circulating phospholipid in plasma, where it is an integral part of lipoproteins, especially HDL (high-density lipoprotein) (39). PCs are synthesized de novo via two ways - the major pathway is the CDP-choline pathway in all nucleated cells and the second is the phosphatidylethanolamine N-methyltransferase (PEMT) pathway, occurring predominantly in liver cells (39). In our study, 61% of PCs analyzed showed significant sex-specific pattern. The PCs concentrations tended to be significantly elevated in females compared to males. When comparing to human studies, consistently with our data, Szymanska et al. found sexual dimorphism in some species of PCs in blood plasma of healthy population with central obesity (40). According to Mittelstrass et al., the most altered PC in human blood was PC aa C32:3 (41). In the study of Rauschert et al., healthy males and non-hormonal contraceptive females were compared. The authors showed sex-dependent pattern in PC aa C34:1, 34:2, 34:3, C36:0, 36:1, 36:2, 36:3, 36:4, C38:0, 38:3, 38:4, 38:6, C40:4, 40:5, 40:6, and PC ae C40:6 (42). These results indicate that there is a similarity between rats, commonly used in preclinical research, and humans.

In recent years, lysoPC species are becoming targets of anticancer drugs (43). Most lysoPCs are derived from PCs by effect of phospholipase A2 and other lipases and may have various functions in cell-signaling, including apoptosis (44). In our study, we observed increased levels of lysoPC a -C17:0, -C18:0, -C20:4, -C26:1 and -C28:0 in females, and lysoPC a C16:1, lysoPC a C18:2, and lysoPC a C20:3 in males. Our results are partially consistent with human studies. Rauschert et al. revealed sex-dependent differences in lysoPC a C16:0, C16:1, C18:0, C18:1, C18:3, C20:3, C20:4, C20:5, C22:5, C22:6, and lysoPC e C18:0 and C18:1 in blood of young adult subjects (42). However, our results are not consistent with the findings of Mittelstrass et al., who observed in general higher concentrations of lysoPCs in men then in women. Nonetheless, in our study lysoPC a C18:2 was significantly elevated in males; and the same was seen also in the work of Mittelstrass et al. (41). Szymanska et al. revealed generally higher levels of lysoPCs in man, whereas higher levels of SMs and PCs were seen in woman (40).

Our results further show that SMs are subject to sexual dimorphism. We observed increased values of SM (OH) C16:1, SM C18:0, SM C 18:1, SM (OH) C22:1 and 22:2, SM C24:0, SM C24:1 and SM (OH) C24:1, SM C26:0 and 26:1 in females when compared with males. Our observations of higher SMs levels in females are consistent with previous human studies by Mittelstrass et al., who observed increased levels of SM (OH) C22:2, SM C18:1 and SM C20:2 (41). In addition, SMs have also been studied in the context of hormones and age. Nikkila et al. found that SMs play an important role in the development and progression of systemic metabolic states in early childhood. In their study, small children (from birth to 4 years) showed age- and sex-related metabolome changes. They found out that the major developmental state differences between girls and boys are attributed to sphingolipids (45). Likewise, Ishikawa et al. observed levels of lipid metabolites related to body mass index (BMI) and age, and found that SMs are significantly higher in woman regardless of BMI and age of individuals (46). Thus, SMs appear to be reliable biomarker of sex differences in lipid metabolism. Sex-specific differences in concentration of lipid metabolites are not random but may affect the whole metabolic pathway.

It should be also noted, that the level of lipid metabolites may be affected by food intake. In our study, we recorded no significant difference in food intake per one gram of body weight between sexes. Previous study indicates that females are more sensitive to insulin’s inhibitory effect on glycolysis (47). The greater sensitivity of females to the antilipolytic effect of insulin compensates for a higher flow of basal fatty acids, thereby helping to maintain fatty acids homeostasis (48). In addition, accumulation of body fat may also influence lipid kinetics. Rodriguez-Nava et al. has shown that brain lipids are differently sensitive to eating habits among the sexes. Male mice showed an increased percentage of saturated fatty acids and reduced levels of ω-6-polyunsaturated fatty acids (PUFA) compared to females (49). In combination with other modulators of lipid metabolism, sex hormones may also play a significant role (50). In our study, sex differences of selected lipids were monitored independently on the stage of reproductive cycle. So, we did not monitor the cycle stage of the female individuals. However, from previous studies we know that female rats become sexually mature at about the sixth week (51).

In conclusion, our study has shown differences between plasma lipid metabolite concentrations in healthy male and female Wistar rats, especially PCs, lysoPCs and SMs. According to VIP score, the most important metabolite in our study was PC aa C38:4. The knowledge about complex metabolic processes represent an important tool for understanding of biological body functions. However, in order to achieve an individual therapeutic approach, it is essential to know the fundamental differences in male and female metabolism. Lipidomics could be a promising new approach for the identification of new biomarkers for monitoring or predicting disease states and/or drug responsiveness. In this study, we provide a comprehensive overview of sexual dimorphism in the lipid metabolism of intact rats. It is obvious that many differences emerge in lipid metabolism related to sex, and that metabolomics along with other ‘omics’ technologies can help dissecting sex-related traits in pathophysiology.

List of abbreviations: BMI, body mass index; FIA, flow injection analysis; HDL, high-density lipoprotein; LC-MS/MS, liquid chromatography-tandem mass spectrometry; LOOCV, leave-one-out cross-validation; lysoPCs, lysophosphatidylcholines; MALDI, matrix-assisted laser desorption/ionization; PCs, phosphatidylcholines; PEMT, phosphatidylethanolamine N-methyltransferase; PITC, phenylisothiocyanate; PLS-DA, partial least squares-discrimination analysis; PUFA, polyunsaturated fatty acids; SAM, significance analysis of microarrays; SMs, sphingomyelins; TOF, time-of-flight; VIP, variance importance in projection.

Authors’ contribution: A. Leskanicova wrote the manuscript and performance the experiment; O. Chovancova and M. Babincak analyzed the data and drafted a portion of the manuscript; A. Blicharova and M. Kolesarova contributed to the study design and to the writing of paper; D. Macekova and J. Kostolny drafted a significant portion of figures and prepared the tables; B. Smajda contributed to the study design and critically corrected the manuscript, T. Kiskova designed the study, analyzed the data and drafted a significant portion of the manuscript.

Acknowledgements: The work was supported by university internal grant schemas VVGS-UPJS-2019-1071, VVGS-PF-2019-1054 and VVGS-PF-2019-1040 and by project “Competence Center for Research and Development in the Field of Diagnostics and Therapy of Oncological Diseases”, ITMS: 26220220153, cofinanced from EU sources and European Regional Development Fund.

The authors would like to thank Dr. Natalia Pipova and Dr. Frantiska Horvathova for technical help.

Conflict of interests: None declared.

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