AbstractUnderstanding the metabolic profile within the follicular microenvironment is crucial for optimizing reproductive efficiency in camels. In this study, we examined the metabolomic profile of camel follicular fluid (FF) during the breeding (n = 10) and non-breeding seasons (n = 10). Gas chromatography-mass spectrometry (GC-MS) was utilized to describe the metabolites present in follicular fluid samples. The results found considerable differences in the metabolomics profiles between the breeding and non-breeding seasons. Hexadecenoic acid, galactose and glucose levels were significantly (P < 0.05) higher in camel FF during the breeding season, while 9-octadecenamide, oleonitrile, glycine, octadecanamide, cholesterol, and propanoic acid were higher (P < 0.05) in FF during the non-breeding season. Multivariante analyses pointed to those 9 metabolites, and univariate analysis showed hexadecenoic acid, galactose, glucose, and oleanitril were the most significant ones in camel follicular fluid collected during both breeding and non-breeding seasons. The univariate and multivariate analyses showed an increase in the levels of hexadecanoic acid, galactose, glucose, and a depletion in the level of oleanitrile in the breeding season compared to the non-breeding season. The ROC curve and statistical analysis showed that hexadecanoic acid, galactose, and oleanitril with AUC = 1 were promising to be seasonal biomarkers of fertility in female camels. In conclusion, the metabolomic analysis of camel FF reveals distinct changes in metabolite levels between breeding and non-breeding seasons, reflecting adaptive metabolic responses to support reproductive processes. These results offer valuable insights into the reproductive physiology of camels and offer practical implications for potential biomarkers and assessing the reproductive status in camels, which can be utilized in reproductive management and conservation efforts in these valuable animal species.
IntroductionCamels (Camelus dromedarius) are unique mammals adapted to harsh desert environments; they play a vital role in the economies and cultures of many countries worldwide1. Camels served as a source of meat, milk, wool, and transportation, as well as a show and racing animal. Reproductive efficiency is crucial for sustaining camel populations and maximizing economic productivity. Camel reproduction is impacted by various factors, including seasonality, which is affected by the availability of resources and environmental conditions2,3. Seasonal variations can affect follicular development and ovulation rates, leading to distinct breeding and non-breeding seasons in camels4,5. Environmental factors such as diet, stress, and temperature can influence the metabolic profile of follicular fluid and impact reproductive outcomes in mammals6,7,8. Therefore, understanding the reproductive physiology of camels is vital for increasing their production and sustainability in various agricultural and pastoral systems9. One aspect of camel reproduction that remains relatively understudied is the metabolic changes occurring within the ovarian follicles during different reproductive seasons.Metabolomics offers a comprehensive approach to describing the metabolite profile of follicular fluid, providing insights into the metabolic pathways and molecular mechanisms underlying ovarian follicle development and function in mammals10. It finds small metabolic changes that are linked to reproductive status, providing a more complete understanding of reproductive health and physiology11. Also, metabolomics helps find metabolic pathways and biomarkers that are unique to each species. The unique reproductive physiology and adaptability of camels to extreme environments may involve complex and multifaceted pathways that might not be possible to detect with traditional methods. Therefore, metabolomics analyses of camel follicular fluid could identify novel biomarkers and several dynamic interactions between metabolites, offering insights into metabolic shifts associated with reproduction.The ovarian follicle is the main functioning unit of the female reproductive system, and it plays a key role in oocyte maturation and eventual fertility. Follicular fluid, the fluid surrounding the oocyte in the ovarian follicle, plays a crucial function in the maturation and development of oocytes12. Follicular fluid serves as a reservoir of nutrients, signaling molecules, and metabolic substrates essential for oocyte growth, development, maturation, and fertilization potentials13. During follicle development, alterations in metabolites are detected in energy metabolism, amino acid metabolism, lipid metabolism, and steroidogenesis, reflecting the metabolic requirements linked to the growth of follicles, proliferation of granulosa cells, and manufacture of steroid hormones14,15. Variations in the metabolome profiles of the follicular fluid regulate crucial processes that are essential for the follicle’s preparation and oocyte to achieve optimal fertility16. The stage of bovine follicular development significantly influences a total of 67 metabolites, with 33.3% reduced and 46 elevated in peri vs. preovulatory FF. The concentrations of hypoxanthine, xanthine, 17β-oestradiol, and inosine decreased. In contrast, the concentrations of retinal, 1-methyl-5-imidazoleacetate, and isovaleryl carnitine were increased17. Metabolomic profiling of FF could also help create biomarkers that can predict the success of bovine in vitro fertilization (IVF) and find metabolic targets that can improve the outcomes of assisted reproductive technology18. Researchers looked at the metabolic profile of bovine follicular fluid and found clear differences between oocytes that successfully developed into blastocysts and oocytes that broke down. They found that l-alanine, glycine, l-glutamate, and linolenic acid were higher in FF from competent oocytes, while urea, palmitic acid, and total fatty acids were much lower in FF from incompetent oocytes19. Furthermore, metabolomic analyses have identified metabolic signatures associated with oocyte quality, follicle maturation, and ovulation, providing valuable biomarkers for assessing reproductive health and fertility potential20.Metabolomics analysis of camel follicular fluid during both the breeding and non-breeding periods might offer crucial insights into the metabolic pathways that underlie follicular growth, can help identify biomarkers associated with reproductive status and can provide valuable information for optimizing breeding strategies and enhancing reproductive efficiency. However, as far as we know, there has not been a specific study conducted on seasonal changes in the metabolomics of camel follicular fluid. Seasonal changes in camel follicular fluid could significantly influence the biochemical environment of the follicular fluid, which in turn affects follicular development, oocyte quality, and overall reproductive performance. We hypothesized that by analysing the metabolomics of camel follicular fluid, we could identify biomarkers associated with reproductive and seasonal variations. This information has the potential applications in improving reproductive management strategies, such as optimizing breeding programs, enhancing in vitro fertilization (IVF) protocols, and developing season-specific nutritional or hormonal interventions to maximize fertility. Therefore, the aim of this study was to examine the metabolomics profile of camel follicular fluid during the breeding and non-breeding seasons; we sought to identify metabolic signatures associated with reproductive seasonality in camels.Materials and methodsEthics approval and consent to participateThe in vivo experimental protocol received approval from the Institutional Animal Care and Use Committee of the National Research Centre of Egypt (Approval no: 13050416-3). The study complies with the ARRIVE guidelines and is conducted in accordance with the EU Directive 2010/63/EU for animal experiments.Camel follicular fluidIn this study, the ovaries were collected from twenty female camels, aged 3–7 years, who had a clinically normal reproductive tract. These camels were slaughtered over one year (October 2022 – October 2023) and were collected, representing the breeding and non-breeding seasons (10 animals per season)21,22. Follicular fluid (FF) is aspirated from dominant follicles of > 10 mm diameter using an 18-gauge needle attached to a 10-ml syringe. The samples were promptly centrifuged at 1000×g for 10 min to eliminate any cellular debris. The supernatant was then put into hermetically sealed glass containers and preserved at a temperature of -80 °C until it was ready for subsequent analysis.Sample Preparation for GC-MS analysisThe camel FF samples were thawed at room temperature for approximately 5 min and then vigorously mixed for 1 min using a vortex. 200 µl of a ternary mixture containing chloroform, methanol, and water at a ratio of 2:5:2% v/v/v was combined with 200 µl of the sample. Next, 200 µl of methanol of high-performance liquid chromatography (HPLC) quality was added, ensuring full mixing, and then vortexed for 30 s. The mixes were subjected to sonication at room temperature for approximately 30 min. The materials were thereafter transferred for centrifugation at 700×g at 4 °C. A volume of 200 µl of the sample was put into a screw-top vial with a capacity of 2 ml. The liquid thereafter underwent evaporation until it completely dried out due to the application of a nitrogen gas stream. Afterward, 100 µl of methoxyl amine hydrochloride in pyridine (20 mg/ml) was introduced, and the combination was allowed to react for 2 h at a temperature of 70 °C. Subsequently, N, O-bis (trimethylsilyl) trifluoroacetamide (BSTFA) containing 1% trimethylsilyl (TMCS) was introduced into the vial, and the resulting combination underwent a 1-hour reaction at a temperature of 40 °C23.GC-MS conditionA Thermo Gas Chromatograph coupled to an MS (Turbomass) was used to analyze camel FF. A tiny amount (1 µl) of the given treated sample was added to a PerkinElmer Clarus system that had an Elite 5MS 30.0 m × 0.25 mm internal diameter capillary column and Turbomass software. As the stationary phase, the column was 0.25 μm thick and composed of fused silica (PerkinElmer). The injector’s temperature was set to 270 °C. Helium was the carrier gas used.Program for capillary column temperatureThe oven temperature of the GC-MS system was initially set at 40 °C. It was then increased to 200 °C at a heating rate of 5 °C and then to 300 °C at the same heating rate. Every step took about two minutes to complete. Injector and interface temperatures were controlled at 220 and 240 degrees Celsius, respectively. Helium gas, which was administered at a flow rate of 1.0 ml/min, made up the mobile phase. With a scanning range of 50–600 m/z, the electron ionization mode was used to detect MS. The multiplier’s voltage and the electrons’ energy were set to 323 V and 70 eV, respectively. The inclusion of pyridine resulted in a solvent delay of 6.5 min. Unknown chemicals were identified by comparing their spectra to those in the Wiley Library’s 2006 collection and the National Institute of Standards and Technology’s (NIST) 2005 collection. It took 58 min in total to analyze one sample24.Statistical analysisFor multivariate PLS-DA and OPLS-DA were employed to examine disparities between the seasonal and non-seasonal cohorts. To conduct univariate analysis, we perform volcano analysis (p < 0.01) and calculate fold change (FC). All previous analyses were performed using MetaboAnalyst 6.0 as a statistical tool. Also, the statistical analysis was conducted using the two-tailed Student’s t-test (p < 0.05) in SPSS 17.0 software (SPSS Inc, Chicago, IL, USA). P-value with (W) is calculated by the Wilcoxon Mann Whitney test, and the more significant the less false discover rate (FDR).The purpose was to assess the statistical significance of the results between volcano test and T- test. Finally, compounds having VIP values greater than 1.0 in OPLS-DA and univariate analysis (two-tailed student’s t-test p values less than 0.05& volcano results) were identified as prospective discriminant metabolites using a Venn diagram. The analysis was conducted utilizing the website http://www.metaboanalyst.ca. The diagnostic efficacy of putative metabolic biomarkers was evaluated using Receiver Operating Characteristic (ROC) curves, with the software MetaboAnalyst 6.0. Additionally, binary logistic regression was conducted using SPSS 17.0 software to determine the optimal combination of follicular biomarkers. (SPSS Inc., USA). In addition, a heat map and pathway analysis were conducted using MetaboAnalyst 6.0 (http://www.metaboanalyst.ca) and the Kyoto Encyclopedia of Genes and Genomes (KEGG; http://www.kegg.jp) to provide an overview of the metabolites that showed significant changes. Pathways that have raw p values less than 0.05 and impact values greater than 0 were considered as possible biomarkers.Follicular metabolite profiling by GC-MSThe workflow of metabolomics research of follicular fluid for breeding and non-breeding seasons, validating biomarker discovery and establishment. The metabolomics species obtained from follicular fluid were effectively isolated and examined using GC-MS. Finally, these metabolites varied from sugars, amino acids, and lipid metabolic pathways.Multivariate statistical analysisA Partial Least Squares Discriminant Analysis (PLS-DA) model was created using metabolomics profiling data obtained from both breeding season and non-breeding season modes. The PLS-DA model yielded the first two principal components, PC1 and PC2. Figure 1A demonstrates that the follicular fluids of seasonal camels distinctly differ from those of non-seasonal camels in the direction of the PC1. The R2Y and Q2 (cumulative) model values were 98% and 97%, respectively, demonstrating satisfactory fitting and predictive capability for the PLS-DA model (Fig. 1B). In addition, response permutation testing was conducted with a sample size of 100, which showed that the model did not suffer from overfitting, as shown by a P-value of less than 0.05. Hence, the multivariate analysis demonstrated that the PLS-DA model exhibited a considerable level of reliability and potential for identifying reproductive biomarkers in follicular secretions.Fig. 1PLS-DA score plots obtained from modeling follicular metabolites in the breeding & non-breeding seasons. (A) The score plot showing a separation of FF collected during breeding (1) & non-breeding seasons (2) in camels; (B) Hierarchical clustering a0nalysis of metabolites in FF collected during breeding & non-breeding seasons, (C) The permutation test of PLS-DA model, respectively. The heat map of metabolomics metabolites indicates the increase or decrease of each metabolite in each group with classes as 1 Breeding & non-breeding season 2. The top 9 metabolites according to VIP of PLS-DA in breeding & non-breeding season were determined from down to top. aIndicated to metabolite clusters increased in seasonal group. bindicated to metabolite clusters decreased in seasonal group. Data transformation is done using log transformation especially Pareto-scaling option prior to multivariate data analysis.Full size imageResultsA preliminary analysis of the metabolomics data revealed significant differences in the metabolomics profiles of camel follicular fluid between the breeding and non-breeding seasons. Several metabolites exhibited seasonal variations, with some being more abundant during the breeding season, while others were elevated during the non-breeding season. Key metabolites associated with reproductive processes, such as amino acids and lipids, showed distinct patterns of abundance between the two seasons.Multivariate statistical analysisA Partial Least Squares Discriminant Analysis (PLS-DA) model was developed using metabolomics profiling data obtained from follicular fluid samples collected during both breeding and non-breeding seasons. The PLS-DA model yielded the first two principal components, PC1 and PC2. As depicted in Fig. 2A, follicular fluids of camels during the breeding season displayed a clear separation from the non-breeding season in the direction of PC1. The R2 and Q2 (cumulative) model values were 98% and 97%, respectively, demonstrating a satisfactory level of fitting and predictive capability for the PLS-DA model (Fig. 2c). In addition, response permutation testing was conducted with a sample size of 100, which showed that the model did not suffer from overfitting, as shown by a P-value of less than 0.05. Hence, the multivariate analysis demonstrated that the PLS-DA model had a considerable degree of reliability and potential in identifying reproductive biomarkers in follicular secretions. In the multivariate analysis PLS-DA, only species with variable importance (VIP) values higher than 1 were kept, as they made significant contributions to the categorization. According to the PLS-DA model, nine metabolites with VIP > 1 were selected as candidate biomarkers. These metabolites are hexadecanoic acid, galactose, 9-octadecenamide, oleonitril, glycine, glucose, cholesterol, octadecanamide, and propanoic acid. An overview of nine metabolites that experienced substantial changes was generated using a heat map analysis of the Pearson correlation coefficients (Fig. 2B).Fig. 2OPLS-DA score plots obtained from modelling follicular metabolites in the breeding and non-breeding seasons. (A): The score plot showing a separation of breeding (1) & non-breeding (2) seasons; (B, C): The permutation test of the OPLS-DA model. Data transformation is done using log transformation especially Pareto-scaling option prior to multivariate data analysis.Full size imageThe heat map of metabolomics metabolites indicates the increase or decrease of each metabolite in each group with classes as1 Breeding and non-breeding season 2. The top 9 metabolites according to VIP of PLS-DA in breeding and non-breeding season were determined from down to top. S-DA analyses, there were clear distinctions between the FF collected during the breeding & non-breeding season. The model parameters achieved scores that were approximately 1.0. (R2 X = 0.556, R2 Y = 0.947, Q2 = 0.925), signifying that the model was effective and distinctly distinguished the FF of breeding and the non-breeding season. The permutations plot confirmed the validity of the original model because the Q2 value = 0.941, near to one with P < 0.01, also, the R2 Y = 0.994, near to one with P < 0.01 (Fig. 1A–C).The outcomes of the cluster analysis on the expression of metabolites in follicular fluids in the breeding season and non-breeding season are shown in Fig. 1. The sample clusters exhibited a small overlap during the non-breeding season group, the metabolite content concentration in the breeding season group was notably different from that in the non-breeding season group, signifying that the samples exhibited excellent consistency, and the data was trustworthy.For OPLS-DA metabolites of VIP > 1, they are the same as mentioned in PLS-DA, noticing that the increased levels of hexadecanoic acid, glucose, and galactose in FF collected during the breeding season and the increase in 9-octadecenamide, oleonitril, glycine, octadecanamide, cholesterol, and propanoic acid in the non-breeding season group when compared with the breeding one as mentioned in the violin plot Fig. 3. Finally, 9 metabolites were selected by multivariate analysis, including PLS-DA and OPLS-DA.Fig. 3Violin plot of metabolites derived from VIP of the OPLS-DA model. 1: indicating FF of the breeding season (red colour) and 2: indicating FF of the non-breeding season (green colour).Full size imageFor univariate analysis, volcano analysis was performed, indicating 4 statistically significant metabolites (p < 0.01) were selected for further validation when comparing follicular fluid collected during the breeding season with the non-breeding seasonal group in female camels as shown in Fig. 4. The plot shows an increase in the levels of hexadecanoic acid, galactose, and glucose and an increase in the level of oleanitril in the FF of the non-breeding season group compared to the breeding seasonal group.Fig. 4Volcano plot of follicular fluid metabolomics data.Full size imageIn addition, the student’s t-test two-tailed analysis using SPSS Ver. 17 indicated that 10 metabolites significantly differ between FF of breeding and non-breeding season groups with P values less than 0.05. These metabolites include aceton, glycine, proline, hexadecanoic acid, galactose, glucose, hexadecanamide, octadecinamide, oleonitrile, and cholesterol.Finally, four metabolites were selected by univariate analysis, including volcano and Student’s t-test, including hexadecanoic acid, glucose, galactose, and oleanitrile. A Venn plot of multivariate analysis and univariate analysis was performed and showed that hexadecanoic acid, glucose, galactose, and oleanitrile metabolites are promising biomarkers, as shown in Fig. 5.Fig. 5Pathway analysis diagram performed using MetaboAnalyst 6.0. The red color indicates the more significant pathways at p < 0.01.Full size imageValidation of metabolomic biomarkersTo verify the precision of these three potential metabolic fertility indicators in distinguishing the metabolomics of FF obtained during the reproductive and non-reproductive periods. These three possible biomarkers are needed to fulfill the following criteria. First, the VIP of PLS-DA and VIP of OPLS-DA values of these four metabolite species must be simultaneously greater than 1. Hexadecanoic acid, glucose, galactose, 9-octadecenamide, oleonitril, glycine, octadecanamide, cholesterol, and propanoic acid were met and set aside for additional verification. Secondly, potential species must exhibit simultaneous statistical dissimilarity (p < 0.05) between FF collected during the breeding and non-breeding season. Glycine, hexadecanoic acid, galactose, glucose, oleonitrile, and cholesterol were verified successfully (Table 1). Finally, FC values of candidate metabolite species must be simultaneously more than 1.5 or less than 0.5 in both comparison groups. Four metabolites, including hexadecanoic acid, glucose, galactose, and oleanitrile met this standard (Table 1).Table 1 Statistical information of candidate metabolite biomarkers.Full size tableAssessment of the diagnostic accuracy of potential metabolic biomarkersFollowing the confirmation of prospective biomarkers in the validation set, an ROC curve was employed to evaluate the diagnostic capacity of these four metabolic biomarkers. According to the information presented in Table 2, only the AUC value of hexadecanoic acid, galactose, glucose, and oleanitril reached 1.0 when comparing the FF collected during the breeding and non-breeding season. However, the ability of glucose metabolite to differentiate the breeding from non-breeding seasons, to which we want to pay greater attention, was unsatisfactory because of a partial lower P value compared to other compound P values.Table 2 The results of ROC analysis for fertility metabolic biomarkers.Full size tableConsequently, a binary logistic regression using forward stepwise selection (Wald) was conducted to determine the optimal combination of these metabolic biomarkers. The findings revealed that a panel consisting of hexdecanoic acid, galactose, and oleanitril was the most effective combination of these three metabolic species (Table 3), and without galactose, the P value was increased from 0.008 to 0.031 indicating that the combination set of three metabolites was stronger and better than the individual metabolite for differentiating between FF collected during the breeding and non-breeding seasons in camels.Table 3 Binary logistic regression results of the biomarkers set.Full size tableAnalyses of metabolic pathwaysSeveral metabolic pathways exhibited a strong correlation with fertility during the breeding season in female camels. The pathways of strong significance identified in the follicular fluid were fatty acid biosynthesis, saturated (palmitic acid) & unsaturated (oleic acid) and galactose metabolism, respectively, as shown in Fig. 6. Additional details are shown in Table 4.Fig. 6Pathway analysis diagram performed using MetaboAnalyst 6.0. The red color indicates the more significant pathways.Full size imageTable 4 Metabolic pathways related to female camel fertility intervention mechanisms in the seasonal group.Full size tableDiscussionMetabolomics is a relatively new field, and while there is a growing interest in applying it to various biological systems, including reproductive biology, there may not yet be comprehensive data available on the metabolomics of camel follicular fluid specifically. Monitoring these metabolic changes could aid in predicting optimal breeding periods, assessing reproductive performance, and implementing targeted interventions to enhance fertility and reproductive efficiency in camel herds.In the present work, the observed changes in metabolite abundance and metabolic pathway activation reflect the dynamic nature of ovarian physiology in dromedary camels and its regulation by seasonal cues, which could be due to metabolic adaptations occurring during different reproductive seasons. During the breeding season, which extends from October to the end of April, increased availability of nutrients and energy substrates may promote enhanced follicular development and oocyte maturation. Conversely, during the non-breeding season, metabolic pathways are redirected towards maintenance and survival rather than reproduction, leading to alterations in follicular fluid composition. The present results showed that the levels of hexadecanoic acid (palmitic acid), glucose, and galactose significantly (P < 0.05) increased in camel FF during the breeding season, while the levels of 9-octadecenamide, oleonitrile, glycine, octadecanamide, cholesterol, and propanoic acid were significantly (P < 0.05) higher in FF collected during the non-breeding season compared to the breeding season. The elevated levels of hexadecanoic acid, glucose, and galactose in FF collected during the breeding season may indicate a metabolic shift towards increased energy production and utilization. They achieve this by increasing the energy supply and providing the necessary building blocks to produce important hormones, such as steroid hormones and prostaglandins, which play a crucial role in reproductive processes. Glucose is crucial for ovarian metabolism as it serves as the primary energy source for ovary. Through glycolysis and oxidative phosphorylation, glucose provides ATP essential for cellular processes, including meiosis and maturation25. Cumulus cells metabolize glucose to pyruvate and lactate, which are directly utilized by oocytes26. Also, the small ovarian follicles possess the capacity to selectively extract and store the elevated levels of glucose from the bloodstream for their growth into the fully developed Graafian follicle. Glucose and metabolic hormones have a direct impact on the regulation of steroidogenesis at the ovarian level27. A reduction in the amount of glucose present in the pre-ovulatory follicle fluid that was growing in cows and mares from a dominant follicle28. The larger follicles in dairy cows were associated with higher glucose levels in the follicular fluid29. In addition, our findings demonstrate a noteworthy elevation in galactose levels throughout breeding seasons, suggesting the presence of other carbohydrates in follicular fluids apart from glucose. Galactose contributes to glycoprotein and glycolipid biosynthesis, which are integral for cell signalling and membrane integrity in follicular cells and oocyte30. The utilization of galactose for glycolysis within the follicle might lead to a substantial accumulation of this sugar. Its presence in follicular fluids may influence carbohydrate metabolism31. The interaction of glucose and galactose in follicular fluid is crucial for ensuring the developmental competence of oocytes, influencing their ability to progress through fertilization and embryonic development, and this is essential for creating an environment that supports oocyte metabolism, growth, and developmental potential32. Maintaining a delicate balance of these monosaccharides is critical for ensuring optimal oocyte viability and successful reproductive outcomes33. In contrast, mouse oocytes exposed during in vitro maturation to 2µM D-galactose, and its metabolites disturbed the spindle structure and chromosomal alignment, which was associated with significant decline in oocyte cleavage and blastocyst development after in-vitro fertilization34. This discrepancy could be due to species differences.In addition, functional fatty acids and their byproducts enhance the growth of follicles, maturation of eggs, development of embryos, and the receptivity of the uterus lining35. Hexadecanoic acid (commonly known as palmitic acid) is a saturated fatty acid that plays a crucial role in metabolic processes. Its role in energy production is primarily mediated through beta-oxidation, a process that occurs in the mitochondria. In steroidogenesis, palmitic acid serves as a precursor for cholesterol biosynthesis. Through the activity of the enzyme acetyl-CoA carboxylase, acetyl-CoA derived from palmitic acid is converted into malonyl-CoA, a key building block for fatty acid synthesis. Further elongation and desaturation processes contribute to the synthesis of cholesterol, a critical substrate for steroid hormone production. These pathways highlight the multifaceted roles of palmitic acid in both cellular energy homeostasis and the biosynthesis of biologically active lipids36,37. Hexadecenoic acid is known to influence reproductive physiology by modulating prostaglandin synthesis, improving oocyte quality, and enhancing the uterine environment. It has been incorporated into diets to boost reproductive performance. On the other hand, a study on oocytes demonstrated that elevated concentrations of non-esterified fatty acids, including palmitic acid, during in vitro maturation of human oocytes impaired developmental competence, leading to blastocysts with lower cell numbers and increased apoptosis38. This discrepancy may be related to species difference or the culture conditions.In dromedary camels, the non-breeding season is associated with significant changes in their reproductive physiology, often regulated by hormonal fluctuations and metabolic adjustments. The increased levels of certain metabolites, such as 9-octadecenamide, oleonitrile, glycine, octadecanamide, cholesterol, and propanoic acid, in follicular fluid (FF) during non-breeding season might play distinct roles in reproductive function. These metabolites play diverse roles in lipid metabolism, signaling cascades, and cellular homeostasis. During the non-breeding season, elevated octadecanamide levels might influence ovarian quiescence by reducing follicular activity. Its involvement in stress and energy homeostasis could also help camels adapt to challenging environmental conditions. While the increase in oleonitrile may indicate shifts in lipid metabolism during the non-breeding season. This could support the maintenance of follicular integrity and regulate the balance between growth and atresia of follicles. Elevated glycine levels in FF may serve to protect ovarian follicles from oxidative stress during metabolic downregulation in the non-breeding season, and it might also support follicular cell survival by promoting glutathione synthesis and osmoregulation39. While the elevated cholesterol levels may reflect a reserve for potential steroidogenesis during the transition to the breeding season40. In sheep, during pregnancy, fetal growth depends not on the type of FA during the finishing phase but the interaction of different sources of FA and different stages. Also, supplementation with FA during early pregnancy changes productive performance and neuropeptides’ mRNA expression of lambs independently of the finishing diet41. Although, the lack of active ovulation in camel during the non-breeding season might lead to cholesterol accumulation within FF. Meanwhile, the increase in propanoic acid might indicate shifts in metabolic activity within the follicular microenvironment. It may serve as an alternative energy source for follicular cells and contribute to maintaining low-grade inflammation or immune tolerance42. These metabolic adjustments might suggest their role in maintaining follicular dormancy and preventing premature follicular development during the non-breeding season and reflect the camel’s ability to cope with seasonal variations in temperature and resource availability. The changes in metabolite levels may be linked to reduced gonadotropin activity and shifts in local ovarian signalling pathways. The observed changes in metabolite profiles between breeding and non-breeding seasons highlight the intricate interplay between metabolic pathways and reproductive physiology in camels. These seasonal metabolic adaptations likely optimize resource allocation, energy utilization, and nutrient availability to meet the specific demands of reproduction. Such metabolic reprogramming ensures optimal follicular development, ovulation, and successful reproductive outcomes during the breeding season while conserving energy and metabolic resources during periods of reproductive quiescence and preserve reproductive function for the upcoming breeding season.ConclusionMetabolomic analysis of camel FF reveals distinct alterations in metabolite levels between breeding and non-breeding seasons, reflecting adaptive metabolic responses to support reproductive processes. These findings deepen our understanding of seasonal reproductive physiology in camels and offer practical implications for reproductive management and conservation efforts in these valuable animal species. Further studies are needed to elucidate the precise molecular mechanisms behind these changes.
Data availability
The data that support the findings of this study are available on request from the first author [ASSA].
ReferencesAbdoon, A. S. S., Soliman, S. S. & Nagy, A. M. Uterotubal junction of the bovine (Bos taurus) versus the dromedary camel (Camelus dromedarius): Histology and histomorphometry. Reprod. Domest. Anim. 59(7), 14665. https://doi.org/10.1111/rda.14665 (2024).Article
CAS
Google Scholar
Marai, I. F. M. et al. Camels reproductive and physiological performance traits as affected by environmental conditions. Trop. Subtrop. Agroecosyst. 10, 129–149 (2009).
Google Scholar
Soliman, S., Elsanea, A., Kandil, O., Aboelmaaty, A. & Abdoon, A. impact of reproductive status, body condition score, and locality on hormonal, and some blood metabolites in Egyptian buffaloes. Egypt. J. Vet. Sci. 55(5), 1387–1396. https://doi.org/10.21608/ejvs.2024.252235.1699 (2024).Abdoon, A. S. Factors affecting follicular population, oocyte yield and quality in camels (Camelus dromedarius) ovary with special reference to maturation time in vitro. Anim. Reprod. Sci. 66(1–2), 71–79. https://doi.org/10.1016/S0378-4320(01)00078-1 (2001).Article
CAS
PubMed
Google Scholar
Sghiri, A. & Driancourt, M. A. Seasonal effects on fertility and ovarian follicular growth and maturation in camels (Camelus dromedarius). Anim. Reprod. Sci. 55(3–4), 223–237. https://doi.org/10.1016/S0378-4320(99)00017-2 (1999).Article
CAS
PubMed
Google Scholar
Abdoon, A. S. S. et al. Effect of reproductive status and season on blood biochemical, hormonal and antioxidant changes in Egyptian buffaloes. Int. J. Vet. Sci. 9(1), 131–135. https://doi.org/10.5555/20203194109 (2020).Article
Google Scholar
Soliman, S. S. et al. Seasonal variation in ovarian functions in Egyptian buffalo and cattle. Int. J. Pharm. Tech. Res. 9(6), 34–42 (2016).CAS
Google Scholar
Abdoon, A. S. et al. Seasonal variation in number of ovarian follicles and hormonal levels in Egyptian buffalo and cattle. Int. J. Vet. Sci. 9(1), 126–130 (2020).
Google Scholar
El-Sanea, A. M. & Soliman, S. S. Ovarian morphometry and follicular population in non-pregnant and pregnant camelus dromedaries. Egypt. J. Vet. Sci. 1–7. https://doi.org/10.21608/EJVS.2025.342608.2545 (2025).Ravisankar, S. et al. Metabolomics analysis of follicular fluid coupled with oocyte aspiration reveals importance of glucocorticoids in primate periovulatory follicle competency. Sci. Rep. 11, 6506. https://doi.org/10.1038/s41598-021-85704-6 (2021).Article
ADS
CAS
PubMed
PubMed Central
Google Scholar
Hennet, M. L. & Combelles, C. M. H. The antral follicle: A microenvironment for oocyte differentiation. Int. J. Dev. Biol. 56, 819–831 (2012).Article
CAS
PubMed
Google Scholar
Revelli, A. et al. Follicular fluid content and oocyte quality: From single biochemical markers to metabolomics. Reprod. Biol. Endocrinol. 7, 40. https://doi.org/10.1186/1477-7827-7-40 (2009).Article
CAS
PubMed
PubMed Central
Google Scholar
Izquierdo, D. et al. Fatty acids and metabolomic composition of follicular fluid collected from environments associated with good and poor oocyte competence in goats. Int. J. Mol. Sci. 23, 4141 (2022).Article
CAS
PubMed
PubMed Central
Google Scholar
Da Broi, M. G. et al. Influence of follicular fluid and cumulus cells on oocyte quality: Clinical implications. J. Assist. Reprod. Genet. 35, 735–751. https://doi.org/10.1007/s10815-018-1143-3 (2018).Article
PubMed
PubMed Central
Google Scholar
Zhang, C. H., Liu, X. Y. & Wang, J. Essential role of granulosa cell Glucose and lipid metabolism on oocytes and the potential metabolic imbalance in polycystic ovary syndrome. Int. J. Mol. Sci. 24, 16247. https://doi.org/10.3390/ijms242216247 (2023).Article
CAS
PubMed
PubMed Central
Google Scholar
Hessock, E. A. et al. Metabolite abundance in bovine preovulatory follicular fluid is influenced by follicle developmental progression post estrous onset in cattle. Front. Cell Dev. Biol. 11, 1156060. https://doi.org/10.3389/fcell.2023.1156060 (2023).Article
PubMed
PubMed Central
Google Scholar
Alrabiah, N. A. et al. Biochemical alterations in the follicular fluid of bovine peri-ovulatory follicles and their association with final oocyte maturation. J. Reprod. Fertile. 4, 220090. https://doi.org/10.1530/RAF-22-0090 (2023).Article
Google Scholar
McRae, C., Sharma, V. & Fisher, J. Metabolite profiling in the pursuit of biomarkers for IVF outcome: The case for metabolomics studies. Int. J. Reprod. Med. 16, 603167. https://doi.org/10.1155/2013/603167 (2013).Article
Google Scholar
Matoba, S. et al. Predictive value of bovine follicular components as markers of oocyte developmental potential. Reprod. Fertil. Dev. 26(2), 337–345. https://doi.org/10.1071/RD13007 (2013).Article
CAS
Google Scholar
Yang, J. et al. Metabolic signatures in human follicular fluid identify lysophosphatidylcholine as a predictor of follicular development. Commun. Biol. 5, 763. https://doi.org/10.1038/s42003-022-03710-4 (2022).Article
CAS
PubMed
PubMed Central
Google Scholar
Bernard, W. M. & Swanson, D. L. Metabolic profiling and integration of metabolomic and transcriptomic data from pectoralis muscle reveal winter-adaptive metabolic responses of black-capped chickadee and American goldfinch. Front. Ecol. Evolut. 10, 866130. https://doi.org/10.3389/fevo.2022.866130 (2022).Article
Google Scholar
Walker, H. K. et al. Seasonal variation in serum metabolites of northern European dogs. J. Vet. Int. Med. 36(1), 190–195 (2022).Article
Google Scholar
Olivier, I. & Loots, D. T. A. metabolomics approach to characterise and identify various Mycobacterium species. J. Microbiol. Methods 88(3), 419–426 (2012).Article
PubMed
Google Scholar
Ahamad, S. R. et al. Metabolomics and trace element analysis camel tears by GC-MS and ICP-MS. Biol. Trace Elem. Res. 177, 251–257 (2017).Article
CAS
PubMed
Google Scholar
Xiong, Y. Y. et al. Regulation of glucose metabolism: Effects on oocyte, preimplantation embryo, assisted reproductive technology and embryonic stem cell. Helyino 10(19), e38551 (2024).Article
CAS
Google Scholar
Xie, H. L. et al. Effects of glucose metabolism during in vitro maturation on cytoplasmic maturation of mouse oocytes. Sci. Rep. 6, 20764. https://doi.org/10.1038/srep20764 (2016).Article
ADS
CAS
PubMed
PubMed Central
Google Scholar
Munõz-Gutiérrez, M. et al. Ovarian follicular expression of mRNA encoding the type 1 insulin like growth factor receptor (IGF-IR) and insulin like growth factor binding protein 2 (IGFBP2) in anoestrous sheep after 5 days of glucose, glucosamine or supplementary feeding with lupin grain. Reproduction 128, 747–756. https://doi.org/10.1530/rep.1.00439 (2004).Article
CAS
PubMed
Google Scholar
Iwata, H. et al. Effects of follicle size and electrolytes and glucose in maturation medium on nuclear maturation and developmental competence of bovine oocytes. Reproduction 127, 159–164. https://doi.org/10.1530/rep.1.00084 (2004).Article
CAS
PubMed
Google Scholar
Leroy, J. L. et al. Metabolite and ionic composition of follicular fluid from different sized follicles and their relationship to serum concentrations in dairy cows. Anim. Reprod. Sci. 80, 201–211. https://doi.org/10.1016/S0378-4320(03)00173-8 (2004).Article
CAS
PubMed
Google Scholar
Shivatare, S. S., Shivatare, V. S. & Wong, C. H. Glycoconjugates: Synthesis, functional studies, and therapeutic developments. Chem. Rev. 122(20), 15603–15671. https://doi.org/10.1021/acs.chemrev.1c01032 (2022).Article
CAS
PubMed
PubMed Central
Google Scholar
Pan, Y. et al. Unraveling the complexity of follicular fluid: insights into its composition, function, and clinical implications. J. Ovarian. Res. 17, 237. https://doi.org/10.1186/s13048-024-01551-9 (2024).Article
PubMed
PubMed Central
Google Scholar
Fontana, J. et al. Metabolic cooperation in the ovarian follicle. Physiol. Res. 69(1), 33–48. https://doi.org/10.33549/physiolres.934233 (2019).Article
CAS
PubMed
PubMed Central
Google Scholar
Gu, L. et al. Metabolic control of oocyte development: Linking maternal nutrition and reproductive outcomes. Cell. Mol. Life Sci. 72, 251–271 (2015).Article
CAS
PubMed
Google Scholar
Thakur, M. et al. Galactose and its metabolites deteriorate metaphase II mouse oocyte quality and subsequent embryo development by disrupting the spindle structure. Sci. Rep. 7(1), 231. https://doi.org/10.1038/s41598-017-00159-y (2017).Article
ADS
CAS
PubMed
PubMed Central
Google Scholar
Zeng, X. Z. et al. Role of functional fatty acids in modulation of reproductive potential in livestock. J. Anim. Sci. Biotechnol. 14, 24. https://doi.org/10.1186/s40104-022-00818-9 (2023).Article
PubMed
PubMed Central
Google Scholar
Prakash, S. Beta (β)-Oxidation of fatty acid and its associated disorders. Int. J. Clin. Biochem. 5(1), 158–172 (2018).
Google Scholar
Hu, J. et al. Cellular cholesterol delivery, intracellular processing and utilization for biosynthesis of steroid hormones. Nutr. Metab. (Lond.) 7, 47. https://doi.org/10.1186/1743-7075-7-47 (2010).Article
CAS
PubMed
Google Scholar
Uhde, K. et al. Metabolomic profiles of bovine cumulus cells and cumulus-oocyte-complex-conditioned medium during maturation in vitro. Sci. Rep. 8, 9477. https://doi.org/10.1038/s41598-018-27829-9 (2018).Article
ADS
CAS
PubMed
PubMed Central
Google Scholar
Gao, L. et al. Glycine regulates lipid peroxidation promoting porcine oocyte maturation and early embryonic development. J. Anim. Sci. 101, 425. https://doi.org/10.1093/jas/skac425 (2023).Article
Google Scholar
Jungheim, E. S. et al. Associations between free fatty acids, cumulus oocyte complex morphology and ovarian function during in vitro fertilization. Fertil. Steril. 95(6), 1970–1974. https://doi.org/10.1016/j.fertnstert.2011.01.154 (2011).Article
CAS
PubMed
PubMed Central
Google Scholar
Oviedo-Ojeda, M. F. et al. Effect of supplementation with different fatty acid profile to the dam in early gestation and to the offspring on the finishing diet on offspring growth and hypothalamus mRNA expression in sheep. J. Anim. Sci. 99(4), skab064. https://doi.org/10.1093/jas/skab064 (2021).Article
PubMed
PubMed Central
Google Scholar
Kujoana, T. C., Mabelebele, M. & Sebola, N. A. Role of dietary fats in reproductive, health, and nutritional benefits in farm animals: A review. Open Agric. 9, 20220244. https://doi.org/10.1515/opag-2022-0244 (2024).Article
Google Scholar
Download referencesFundingOpen access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB).The research was financed by the National Research Centre of Egypt for funding this research project (Proj. ID: 13050416).Author informationAuthors and AffiliationsDepartment of Animal Reproduction and Artificial Insemination, Veterinary Research Institute, National Research Centre (NRC), Dokki, Cairo, 12622, EgyptAhmed Sabry S. Abdoon, Seham Samir Soliman & Amro M. El-SaneaTherapeutic Chemistry Department, National Research Centre, El-Tahrir St., Dokki, Cairo, EgyptNoha S. Hussein, Samir H. I. Haggag & Abdel-Hamid Z. Abdel-HamidAuthorsAhmed Sabry S. AbdoonView author publicationsYou can also search for this author in
PubMed Google ScholarSeham Samir SolimanView author publicationsYou can also search for this author in
PubMed Google ScholarNoha S. HusseinView author publicationsYou can also search for this author in
PubMed Google ScholarSamir H. I. HaggagView author publicationsYou can also search for this author in
PubMed Google ScholarAmro M. El-SaneaView author publicationsYou can also search for this author in
PubMed Google ScholarAbdel-Hamid Z. Abdel-HamidView author publicationsYou can also search for this author in
PubMed Google ScholarContributionsA.S. and S.S. conceptualization of the study; S.S. funding acquisition; N.S., S.H., A.Z. and S.S. data collection and evaluation, statistical analysis; A.S. and S.S manuscript writing and AZA, AME, and S.S. manuscript correcting; All authors paper reviewing.Corresponding authorsCorrespondence to
Ahmed Sabry S. Abdoon or Seham Samir Soliman.Ethics declarations
Competing interests
The authors declare no competing interests.
Additional informationPublisher’s noteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Reprints and permissionsAbout this articleCite this articleAbdoon, A.S.S., Soliman, S.S., Hussein, N.S. et al. Metabolomic profile of dromedary camel follicular fluid during the breeding and non-breeding seasons.
Sci Rep 15, 8923 (2025). https://doi.org/10.1038/s41598-025-91710-9Download citationReceived: 07 November 2024Accepted: 24 February 2025Published: 15 March 2025DOI: https://doi.org/10.1038/s41598-025-91710-9Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard
Provided by the Springer Nature SharedIt content-sharing initiative
KeywordsCamelFollicular fluidMetabolomicsBreeding seasonNon-breeding seasonGC-MS