Comprehensive serum, follicular fluid, and ovarian tissue profiling reveals systemic metabolic alterations in high-yielding dairy cows with postpartum inactive ovaries

Comprehensive serum, follicular fluid, and ovarian tissue profiling reveals systemic metabolic alterations in high-yielding dairy cows with postpartum inactive ovaries | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comprehensive serum, follicular fluid, and ovarian tissue profiling reveals systemic metabolic alterations in high-yielding dairy cows with postpartum inactive ovaries Yuxi Song, Xuejie Jiang, Yu Hao, Rui Sun, Yunlong Bai, Chuang Xu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5526997/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Jan, 2026 Read the published version in BMC Veterinary Research → Version 1 posted 9 You are reading this latest preprint version Abstract Background Inactive ovaries (IO) commonly cause postpartum anestrus in high-yielding dairy cows. Despite the widespread reporting of single-metabolic characteristics of postpartum IO, a comprehensive metabolic framework is lacking. Liquid chromatography–tandem mass spectrometry was used to compare the metabolic changes in the serum, follicular fluid (FF), and ovarian tissue of six multiparous Holstein cows from each of the IO and healthy control (HC) groups. Results Cows with IO had a higher milk yield during the previous lactation, poorer BCS, smaller largest follicle (LF) diameter at 63 days in milk, and slower LF growth rate than HC cows ( p < 0.01). They also exhibited higher serum levels of non-esterified fatty acids and aspartate aminotransferase and lower levels of estradiol, progesterone, insulin-like growth factor 1, calcium, and phosphorus ( p < 0.01). Under IO conditions, 40, 51, and 14 differential metabolites were identified in serum, FF, and ovarian tissues, respectively. 12-Methyltridecanoic acid was consistently upregulated in cows with IO compared to HC cows across all samples ( p < 0.01). Metabolomic pathway analysis identified significant alterations in six, three, and two metabolic pathways related to IO in the serum, FF, and ovarian tissues, respectively, affecting amino acid, energy, carbohydrate, lipid, and nucleotide metabolism. Valine, leucine, and isoleucine biosynthesis showed significant changes ( p < 0.05) in all samples. Conclusion In summary, these metabolic changes in cows with IO reflect a complex response to metabolic, oxidative, and inflammatory stresses. Our study provides the most comprehensive metabolic framework for cows with postpartum IO. Dairy cow Inactive ovary Metabolomics LC–MS/MS Serum Follicular fluid Ovary Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Inactive ovaries (IO) is a prevalent postpartum ovarian condition in high-yielding dairy cows, with an 8.5% incidence rate during lactation in the absence of intervention [ 1 ]. Around 20% of dairy cows in anestrus exhibit IO at the beginning of breeding program [ 2 ] or 63 days in milk (DIM) [ 3 ]. The IO is characterized by transient ovarian dysfunction and loss of periodic ovarian follicular activity [ 4 ]. This condition, in turn, can extend calving to the first service interval, conception interval (days open), and calving interval [ 5 – 7 ]. Affected cows may subsequently face a higher risk of culling owing to poor reproductive performance, resulting in huge losses for the dairy industry [ 7 ]. Historically, IO has been referred to using various terms, including inactive-static ovaries [ 8 ], ovarian inactivity [ 9 , 10 ], ovarian hypofunction [ 11 , 12 ], and ovarian quiescence [ 13 , 14 ]. Currently, the widely accepted definition of IO is that both ovaries have no follicles larger than 8 or 10 mm, no corpus luteum, and the condition lasts longer than seven days [ 6 , 7 , 11 , 15 , 16 ]. Moreover, cows with IO have serum progesterone concentrations less than 1 ng/mL [ 5 , 15 ]. Many factors can contribute to IO in cows, including poor body condition score (BCS), inadequate feed intake, excessive milk yield, and improper management of stress and disease [ 5 , 17 ]. A negative energy balance (NEB), associated with metabolic and hormonal alterations during early lactation, is increasingly recognized as an important contributor to IO [ 18 ]. Our recent study highlighted the contributing role of NEB in the development of IO [ 7 ]. Thus, preventing and treating IO have become crucial challenges for early lactation management in modern dairy farms. Follicular fluid (FF) contains bioactive molecules that play an important role in follicular growth and oocyte maturation [ 19 ]. The composition of FF changes as the follicle develops, reflecting its pivotal role in oocyte meiosis, ovulation, and fertilization [ 20 ]. Similarly, the ovaries provide a localized environment conducive to follicular growth and hormone production [ 21 ]. Granulosa cells in the ovaries are vital for the folliculogenesis and steroidogenesis [ 22 ]. Metabolites in the FF derived from blood and cellular secretions, as well as those in ovarian tissue, necessitate comparative metabolic analyses of FF, serum, and ovarian tissue in cows with IO to understand the reproductive disorder processes underlying cattle infertility [ 23 ]. Recent advances in metabolomics have opened up new avenues for understanding the complex biochemical networks involved in these processes. Metabolomics, the in-depth study of metabolites in biological systems, is a powerful tool for elucidating metabolic alterations associated with specific physiological states or disorders [ 24 ]. The pathophysiology of IO in dairy cows is multifaceted and involves complex interactions between the metabolic, endocrine, and immune pathways [ 25 ]. Energy metabolism, liver function, and reproductive hormone regulation are critical factors that influence ovarian activity [ 26 ]. Previous studies have demonstrated that cows with IO exhibit distinct serum, whey, and FF metabolic profiles compared to their estrus counterparts, highlighting the capability of metabolomics in discovering biomarkers for early diagnosis and intervention [ 9 , 27 ]. Despite the importance of reproductive health in dairy cows, limited information exists regarding the metabolomic changes associated with IO. Previous studies have primarily focused on hormonal and biochemical parameters [ 7 , 28 , 29 ], as well as metabolic profiles based on one or two types of samples [ 4 , 9 , 27 ]; however, comprehensive metabolomic analysis is lacking. Understanding the metabolic basis of IO may contribute to creating diagnostic instruments and treatment approaches aimed at enhancing the reproductive efficiency of dairy cows. Identifying specific metabolic markers associated with IO may facilitate early diagnosis and targeted treatment, thereby enhancing reproductive efficiency and reducing economic losses [ 30 ]. This study aimed to comprehensively analyze serum, FF, and ovarian tissue samples using liquid chromatography-tandem mass spectrometry (LC–MS/MS) to investigate the metabolic differences between postpartum Holstein dairy cows experiencing IO and those that are healthy. Hypothetically, specific biomarkers of IO exist in serum, and changes in their levels reflect those in FF or ovarian tissue. Such biomarkers could be used to develop novel, reliable, and non-invasive diagnostic tools for IO, enhancing early intervention and effective management for affected cows. This study provides the most thorough metabolomic evaluation of IO in cows, offering a broader context for its supposition. Materials and methods Animal handling and management This study was carried out with Holstein cows from a substantial intensive cattle farm in central Heilongjiang Province, China, following the guidelines of the Veterinary Medical Ethics Committee of the Ministry of Agriculture of China. Approval was obtained from the Animal Welfare and Research Ethics Committee of Heilongjiang Bayi Agricultural University (protocol code: DWKJXY2024007; approval date: January 4, 2024). The farm has a population of 3,215 cows, including 1,671 lactating animals. The average parity of the cows was 3.05 ± 1.12, with an average annual milk yield of 12,010 ± 1,548 kg (milk fat, 4.61 ± 0.32%; milk protein, 3.33 ± 0.22%; mean ± standard deviation [SD]). During the experimental period, 701 cows calved, and the incidence rate of postpartum IO was approximately 5.7% after estrus synchronization. Twelve multiparous Holstein cows (age, 3.98 ± 0.62 years; parity, 2.75 ± 0.62; BCS, 2.88 ± 0.39; daily milk yield during the first 63 DIM, 45.54 ± 2.18 kg/d; milk yield during the previous lactation, 13,130.00 ± 1,185.63 kg; mean ± SD) were divided into a healthy control (HC) group ( n = 6) and an IO group ( n = 6) based on two consecutive rectal palpation and ultrasonography examinations at 56 and 63 DIM. Corresponding data (cow identification number, herd code, and calving date) were recorded. The cows were raised in the same environment, fed the same diet, unrestricted access to fresh water, and uniform same management program. During the 60-day dry period, the cows were fed a total mixed ration (TMR) diet twice daily. After calving, the cows were milked three times each day (0500, 1300, and 2200 h) and fed a TMR diet three times each day (0600, 1400, and 2300 h) primarily consisting of corn silage. National Research Council (NRC, 2001) guidelines were followed in the formulation of the TMR for early lactation [ 31 ]. Table 1 presents the components and chemical composition of the TMR diet during early lactation. Specific software (Afifarm; Afimilk, Kibbutz Afikim, Israel) was utilized to document the age, parity, and milk yield of the dairy cows. A five-point scoring method using 0.25-unit intervals was used to evaluate the BCS of the dairy cows by two well-trained veterinarians [ 32 ]. Table 1 Ingredients and chemical composition of the diets for early lactating dairy cows Item Early lactation period Ingredient, % of dry matter Water 11.18 Cottonseed 2.10 Soybean husk 3.06 Oat grass 1.02 Alfalfa 5.11 Silage 58.54 Corn 7.76 Soybean meal 2.66 Flaked corn 4.08 Molasses 2.04 Fat powder 0.82 Premix 1 1.63 Nutrient, % of dry matter Dry matter 55.60 Crude protein 16.00 Acid detergent fiber 20.30 Neutral detergent fiber 39.10 Calcium 0.40 Phosphorus 0.30 1 Concentration per kilogram of premix DM: 40,000 IU of vitamin A, 37,000 IU of vitamin D, 500 IU of vitamin E, 30 mg of copper, 25 mg of iron, 140 mg of manganese, 140 mg of zinc, 0.8 mg of selenium. Estrous synchronization protocol The first artificial insemination was performed using the Presynch-Ovsynch protocol on all cows [ 33 ]. In brief, the procedure began with a 25 mg intramuscular injection of prostaglandin F2 alpha (PGF 2α ) (Lutalyse; dinoprost tromethamine; Pfizer Animal Health, New York, NY, USA) on 30 ± 3 DIM, and was subsequently followed by a second 25 mg injection of PGF 2α after a period of 14 days (on average of 44 ± 3 DIM). Twelve days post the second PGF 2α administration (on average of 56 ± 3 DIM), the ovulation synchronization protocol commenced with the administration of 100 µg of gonadotropin-releasing hormone (GnRH) (Cystorelin; gonadorelin diacetate tetrahydrate; Merial, Duluth, GA, USA), followed by a third PGF 2α (25 mg) injection seven days later (on average of 63 ± 3 DIM). A final dose of GnRH (100 µg) was given 56 hours after the third PGF 2α injection (on average of 65 ± 3 DIM), but no insemination occurred 16 to 18 hours later (on average of 66 ± 3 DIM). Ovarian ultrasonography At 56 and 63 DIM, all cows were examined for ovarian using transrectal ultrasonography (Easi-Scan Curve, 5.0 MHz; BCF Technology Ltd., Bellshill, UK), as described previously [ 7 ]. Briefly, the presence of a typical corpus luteum and/or sizable follicles (> 10 mm) in two ultrasonographic assessments of the ovaries conducted seven days apart, along with a serum progesterone level above 1 ng/mL, were included in the HC group. Cows in the HC group were all in the luteal phase. The IO group included cows that did not have ovarian cysts, persistent corpus luteum, or follicles, as well as those lacking a normal corpus luteum and/or large follicles (> 10 mm). Two ultrasonographic assessments of the ovaries, conducted seven days apart, indicated that there were no significant changes in the follicular structures, and the serum progesterone concentration was < 1 ng/mL. Four cows with other health problems (two had ketosis, one had left displaced abomasum, and one had mastitis), as determined by two experienced veterinarians were excluded from the study. The diameter of the largest follicle (LF) in the ovaries of each cow was recorded simultaneously during both examinations. The final daily growth rate of the follicles was computed by taking the difference between the LF diameters at 63 and 56 DIM and dividing this value by seven. Sample collection At 63 DIM, blood samples were obtained from cows in both the IO and HC groups through the tail vein using disposable vacuum blood collection tubes (5 mL; Kangweishi Medical Technology Co., Ltd, Hebei, China) without anticoagulants. Following a 30-min clotting period at room temperature, a centrifuge was used to centrifuge samples for 10 min at 3,500 × g. The serum obtained was subsequently maintained at -80℃ for later biochemical and LC–MS/MS analyses. Ovarian FF and ovarian cortical tissue samples were independently harvested from the ipsilateral ovaries of both groups of cows using transvaginal ultrasound-guided puncture and aspiration puncture (Easi-Scan Curve, 5.0 MHz), as previously described [ 27 , 34 ]. Briefly, an FF sample (100–200 µL) was aspirated using a puncture needle from the LF in the ovaries of each cow under ultrasound guidance. An ovarian cortical tissue sample (50–100 mg) was subsequently harvested from the specimen notch of the puncture needle. A centrifuge was used to centrifuge FF samples for 10 min at 12,000 × g, and the supernatant was maintained at -80℃ for future LC–MS/MS analysis. The tissues were grouped, labeled, packed into sterile freezing tubes, quickly frozen in liquid nitrogen, and maintained at -80℃ until analysis by LC–MS/MS. Serum biochemical detection Serum concentrations of β-hydroxybutyrate, non-esterified fatty acids (NEFA), glucose, aspartate aminotransferase (AST), alanine aminotransferase (ALT), total protein (TP), calcium, phosphorus, and magnesium were measured using commercial biochemical assay kits provided by Mindray Biomedical Electronics Co., Ltd. (Shenzhen, China) and analyzed with the Mindray BS-830S fully automatic biochemistry analyzer (Mindray Biomedical Electronics Co., Ltd.) at the Biotechnology Centre of Heilongjiang Bayi Agricultural University. All analyses were performed according to the instructions of manufacturer. Serum concentrations of estradiol and progesterone were assessed utilizing two commercial kits from the same batch (Xinfan Biotechnology Co., Ltd., Shanghai, China) and quantified using an XH-6080 radioimmunocounter (Xian Nuclear Instrument Co., Ltd., Xian, China) following the validated radioimmunoassay protocols established by the manufacturer [ 35 ]. There was a sensitivity of 2 pg/mL for estradiol and 0.2 ng/mL for progesterone in the assays. The intra-assay coefficient of variation was < 10% and the inter-assay coefficient of variation was < 15%. Serum insulin-like growth factor 1 (IGF-1) levels were determined using a bovine-specific ELISA kit (Xinfan Biotechnology Co., Ltd.). Quantification was carried out with a Multiskan FC microplate reader (Thermo Fisher Scientific Co., Ltd., Shanghai, China) at a wavelength of 450 nm, following the manufacturer's guidelines. Sample preparation Preparation of samples for LC-MS/MS analysis was carried out according to the previous description [ 27 , 36 ]. In brief, 100 µL of serum or FF was combined with 300 µL of methanol including 1 µg/mL L-2-chlorophenylalanine, serving as the internal standard. The sample was subjected to vortex mixing for 30 s, followed by sonication for 10 min in an ice-water bath. It was then incubated at -40℃ for 1 h to allow protein precipitation, after which it was centrifuged at 12,000 × g for 15 min at 4℃. The supernatant was carefully transferred to a new glass vial for subsequent LC-MS/MS analysis. Ovarian cortical tissue was ground in liquid nitrogen. Fifty milligrams of the ground sample was combined with 1,000 µL of extraction solvent (methanol: acetonitrile: water in a 2:2:1 ratio) including 1 µg/mL L-2-chlorophenylalanine as the internal standard, vortexed for 30 s, homogenized at 35 Hz for 4 min, and sonicated for 5 min on ice. The cycles of homogenization and sonication were carried out three times. The mixture was then incubated for 1 h at -40℃, followed by centrifugation at 12,000 × g at 4℃ for 15 min, and the supernatant obtained was collected into a new sampling bottle for LC–MS/MS analysis. To prepare a quality control sample, equal volumes of supernatants from all samples were combined. LC–MS/MS analysis The LC–MS/MS analyses were conducted utilizing a Vanquish UHPLC system (Thermo Fisher Scientific) equipped with a UPLC BEH Amide column (2.1 mm × 100 mm, 1.7 µm) connected to a Q Exactive HFX mass spectrometer (Orbitrap MS, Thermo Fisher Scientific). The mobile phase comprised 25 mmol/L ammonium acetate and 25 mmol/L ammonia hydroxide in water (pH 9.75) (A) and acetonitrile (B). The elution gradient was as follows: from 0 to 0.5 min, 95% B; from 0.5 to 7.0 min, a linear decrease from 95–65% B; from 7.0 to 8.0 min, a further decrease from 65–40% B; from 8.0 to 9.0 min, maintained at 40% B; from 9.0 to 9.1 min, a rapid increase from 40–95% B; and from 9.1 to 12.0 min, maintained at 95% B. The mobile phase flowed at a rate of 0.5 mL/min, with the column maintained at 35°C and the autosampler at 4°C, while the injection volume was set to 3 µL. A QE HFX mass spectrometer was utilized due to its capability to obtain MS/MS spectra in an information-dependent acquisition mode, managed by the acquisition software (Xcalibur, Thermo Fisher Scientific). In this mode, the software for acquisition consistently assessed the complete mass spectrometry spectrum obtained from full scans. ESI source parameters were configured as follows: sheath gas flow rate, 50 Arb; Aux gas flow rate, 10 Arb; capillary temperature at 320℃. The full MS resolution was established at 60,000, while the MS/MS resolution was set to 7,500. Collision energy values were 10/30/60 in NCE mode, and the spray voltage was configured to 3.5 kV for positive-ion mode or -3.2 kV for negative-ion mode. Data processing and multivariate statistical analyses ProteoWizard was used to convert raw data to mzXML [ 37 ]. R package XCMS was used to align peak areas, adjust retention times, and extract peak areas [ 38 ]. For the data processed with XCMS, ion peak data with more than 50% missing values within a group were deleted. After Pareto-scaling preprocessing, pattern recognition was carried out utilizing SIMCA-P software (version 14.1; Umetrics, Umea, Sweden). This analysis encompassed unsupervised principal component analysis (PCA) as well as supervised orthogonal partial least squares discriminant analysis (OPLS-DA). PCA was employed to assess the aggregation of data within groups and the separation between groups, while OPLS-DA was utilized to investigate inter-group differences in greater detail. Validation of the OPLS-DA models was achieved using variations in Y (R 2 Y) and the model forecasting ability (Q 2 ) using cross-validation and permutation tests with 200 iterations. Models were considered stable and reliable when 1 ≥ R 2 Y and Q 2 ≥ 0.4 [ 39 ]. Additionally, a Q 2 intercept of less than 0.05 in the permutation test was applied to check for overfitting [ 40 ]. Univariate analyses were additionally conducted, incorporating Student’s t-test and fold-change (FC) analysis. Metabolite identification and pathway analysis Metabolites with significantly different levels between groups were identified based on variable importance in the projection (VIP) scores (VIP ≥ 1.5) derived from the OPLS-DA model, along with FC criteria (FC ≥ 2 or FC ≤ 0.5) from univariate analysis, and p -values ( p < 0.05) obtained from the Student’s t-test. Differential metabolites were visualized utilizing bar graphs and Venn diagrams created with GraphPad Prism 9.0 (GraphPad Software Inc., San Diego, CA, USA). An Euclidean distance matrix was established for the mathematical analysis of differential metabolites in each series of analyses. Divergent metabolites were clustered using the complete linkage method, which was illustrated as a heatmap representing hierarchical clustering. The Human Metabolome Database ( https://hmdb.ca/ , last accessed on September 30, 2024) and Kyoto Encyclopedia of Genes and Genomes (KEGG) database ( www.kegg.jp/kegg/pathway.html , last accessed on September 30, 2024) were used to identify the differential metabolites associated with various metabolic pathways. The results of the metabolic pathway analysis were presented in a bubble plot. The clustering heatmap and bubble plot for the differential metabolites were visualized using MetaboAnalyst 6.0 ( https://new.metaboanalyst.ca/ ; last accessed on September 30, 2024). Statistical analysis Each individual cow was considered an experimental unit. Statistical analyses were conducted utilizing IBM SPSS Statistics for Windows (version 26.0; IBM Corp., Armonk, NY, USA). An independent-samples Student’s t-test was utilized to assess differences in clinical and serum biochemical variables between the IO and HC groups. “NormalizeData” function was used to normalize the data. Data points greater than three SDs from the mean were removed as outliers. Data are expressed as mean ± SD. Statistical significance was defined as p < 0.05 and p < 0.01. Results Clinical characteristics and serum biochemical measurements The clinical characteristics and serum biochemical indicators of the IO and HC groups are presented in Table 2 . Although both groups were similar in age, parity, milk yield during the first 63 DIM, and LF diameter at 56 DIM ( p > 0.05), cows in the IO group exhibited a higher milk yield during the previous lactation, along with a lower BCS, a smaller LF diameter at 63 DIM, and a slower LF growth rate ( p < 0.01). Additionally, the IO group exhibited higher serum NEFA and AST levels and lower serum estradiol, progesterone, IGF-1, calcium, and phosphorus levels than the HC group ( p 0.05). Table 2 Clinical characteristics and serum biochemical indicators of the IO and HC groups Item IO group ( n = 6) HC group ( n = 6) p -value Age (years) 4.13 ± 0.78 3.83 ± 0.41 0.426 Parity 2.83 ± 0.75 2.67 ± 0.52 0.664 BCS 2.58 ± 0.13 3.17 ± 0.34 0.007 Milk yield during the first 63 DIM (kg/d) 44.73 ± 1.31 46.35 ± 2.67 0.211 Milk yield during the previous lactation (kg) 14,133.33 ± 744.76 12,126.67 ± 349.09 < 0.001 LF diameter at 56 DIM (mm) 3.50 ± 0.84 4.00 ± 0.89 0.341 LF diameter at 63 DIM (mm) 7.50 ± 1.22 12.83 ± 1.60 < 0.001 LF growth rate (mm/d) 0.57 ± 0.25 1.26 ± 0.10 < 0.001 Estradiol concentration (pg/mL) 7.29 ± 1.08 20.78 ± 3.82 < 0.001 Progesterone concentration (ng/mL) 0.53 ± 0.15 6.37 ± 2.18 0.001 IGF-1 concentration (ng/mL) 27.63 ± 2.47 84.22 ± 8.94 < 0.001 BHB concentration (mmol/L) 0.80 ± 0.39 0.75 ± 0.23 0.793 NEFA concentration (mmol/L) 0.55 ± 0.02 0.39 ± 0.05 < 0.001 Glucose concentration (mmol/L) 3.19 ± 0.02 3.28 ± 0.23 0.409 AST activity (U/L) 81.67 ± 10.46 56.17 ± 10.30 0.002 ALT activity (U/L) 20.00 ± 4.15 24.67 ± 10.38 0.331 TP concentration (g/L) 63.63 ± 14.16 59.32 ± 14.38 0.612 Calcium concentration (mmol/L) 2.04 ± 0.03 2.24 ± 0.07 < 0.001 Phosphorus concentration (mmol/L) 1.55 ± 0.13 2.01 ± 0.25 0.003 Magnesium concentration (mmol/L) 1.14 ± 0.14 1.12 ± 0.07 0.815 IO, inactive ovaries; HC, healthy control; BCS, body condition score; DIM, days in milk; LF, largest follicle; IGF-1, insulin-like growth factor 1; BHB, β-hydroxybutyric acid; NEFA, non-esterified fatty acids; AST, aspartate aminotransferase; ALT, alanine aminotransferase; TP, total protein. Multivariate statistical analysis A total of 12,570, 11,352, and 12,643 peaks were obtained in the serum, FF, and ovarian tissue samples, respectively. Subsequent screening of these peaks led to the identification of 1,542 metabolites in the serum, 1,536 metabolites in the FF, and 1,569 metabolites in the ovarian tissue. Unsupervised PCA was utilized to observe the overall distribution of samples (Fig. 1 A, B, and C). The PCA score plots indicated a partial overlap of the serum, FF, and ovarian tissue samples between the IO and HC groups. To better distinguish between the IO and HC groups and enhance the effectiveness and analytical capability of the model, supervised OPLS-DA analysis was conducted (Fig. 1 D, E, and F). According to the OPLS-DA score plots, the IO and HC groups were significantly different, indicating notable differences in metabolites between the groups. The R 2 Y and Q 2 values of the OPLS-DA score plots for the serum, FF, and ovarian tissue samples were 1 and 0.965, 1 and 0.966, and 1 and 0.792, respectively, all of which were close to 1, suggesting that the model was both stable and reliable. To assess the risk of overfitting in the OPLS-DA model, its quality was examined using the 200-response permutation test. The Q 2 intercept values were less than 0.05, indicating there no overfitting (Fig. 1 G, H, and I). Differential metabolite analysis Differential metabolite screening was conducted using the criteria of VIP ≥ 1.5, FC ≥ 2 or FC ≤ 0.5, and p < 0.05. Table 3 shows the upregulation and downregulation of differential metabolites in the serum, FF, and ovarian tissues of cows in the IO group relative to the HC group. Forty differential metabolites were identified in the serum samples, of which 16 were upregulated and 24 were downregulated (Fig. 2 A). Fifty-one differential metabolites were identified in the FF samples, of which 16 were upregulated and 35 were downregulated (Fig. 2 A). Fourteen differential metabolites were identified in the ovarian tissue, with 12 upregulated and two downregulated metabolites (Fig. 2 A). Moreover, the differential metabolites identified in both the serum and FF samples included ginkgoic acid and 12-methyltridecanoic acid (Fig. 2 B). Only 12-methyltridecanoic acid was detected at differential levels in both serum and ovarian tissues (Fig. 2 B). The ovarian tissue and FF contained differential levels of L-valine, 3-methyl-1,3-dihydroindol-2-one, 12-keto-leukotriene B4 (12-Keto-LTB4), and 12-methyltridecanoic acid (Fig. 2 B). Notably, 12-methyltridecanoic acid was upregulated in the IO group compared to the HC group in serum, FF, and ovarian tissue samples ( p < 0.01, Fig. 2 B). The relative abundances of 40, 51, and 14 differential metabolites in the serum, FF, and ovarian tissue were compared between the IO and HC groups, respectively, revealing a distinct clustering pattern on a heatmap (Fig. 2 C, D, and E). Table 3 Differential metabolites in the serum, FF, and ovarian tissue of the IO and HC groups Category Metabolites names m/z RT (min) FC VIP p -value IO vs. HC Mode Serum 12-Methyltridecanoic acid 227.021 27.006 2.882 2.480 0.0051 up ESI− Ethyl hexadecanoate 283.155 115.383 0.369 1.957 0.0013 down ESI− Indole-3-acetate 173.081 149.562 5.658 2.473 0.0405 up ESI− Palmityl trifluoromethyl ketone 307.055 90.461 2.052 1.852 0.0498 up ESI− Ginkgoic acid 345.244 54.908 2.157 2.150 0.0045 up ESI− (R)-3-Hydroxybutanoate acid 105.100 206.156 2.722 2.100 0.0269 up ESI− 3-alpha,20-alpha-Dihydroxy-5-beta-pregnane 3-Glucuronide PE 495.298 235.048 0.494 2.002 0.0038 down ESI− N-Acetyl-L-tyrosine 222.077 213.200 2.255 2.725 < 0.0001 up ESI− (s)-2-Aminobutanoate 104.120 216.705 2.547 1.688 0.0131 up ESI− O-Desmethylnaproxen 214.076 246.812 0.464 1.822 0.0333 down ESI− L-Ribulose 149.045 315.920 2.090 2.523 0.0086 up ESI− Tegaserod 299.186 296.379 2.974 2.162 0.0031 up ESI− 13-Hpotre 308.621 55.831 2.532 2.083 0.0131 up ESI− 3-Methyl-2-oxobutanate 115.039 64.893 0.458 1.829 0.0300 down ESI− Maslinic acid 472.086 26.180 3.704 1.677 0.0006 up ESI− N1-(2-Hydroxyethyl)flurazepam 333.079 310.260 0.457 1.627 0.0250 down ESI+ L-Glutamine 147.151 54.520 3.110 1.553 0.0227 up ESI+ Citrulline 177.025 429.128 0.470 1.628 0.0060 down ESI+ L-Glutamate 148.131 58.82 2.233 1.529 0.0101 up ESI+ 1,3-Diisopropylbenzene 163.123 5.006 2.882 1.545 0.0261 up ESI+ L-alpha-Aminobutyric acid 104.071 345.354 0.458 1.516 0.0151 down ESI+ Dimethyl dialkyl ammonium chloride 303.309 54.534 0.374 1.517 0.0041 down ESI+ O-decanoyl-R-carnitine 316.161 242.305 0.492 1.522 0.0491 down ESI+ 2-Aminopentanedioic acid 147.612 393.743 0.412 1.810 0.0017 down ESI+ Mangiferdesmethylursanone 429.373 45.972 0.319 1.615 0.0351 down ESI+ Ethylbenzene 107.049 24.836 0.449 1.589 0.0174 down ESI+ PC(22:4(7Z,10Z,13Z,16Z)/17:1(9Z)) 821.627 149.422 0.493 1.551 0.0449 down ESI+ Guanidoinoacetate 118.121 418.864 2.021 1.361 0.0461 up ESI+ Anandamide (20:l, n-9) 354.279 111.179 0.450 1.678 0.0062 down ESI+ 5-Methoxytryptophan 235.108 69.179 0.456 1.537 0.0201 down ESI+ PE(O-16:0/18:4(6Z,9Z,12Z,15Z)) 698.513 160.647 0.460 1.629 0.0051 down ESI+ PC(P-16:0/18:4(6Z,9Z,12Z,15Z)) 737.580 166.753 0.373 1.777 0.0270 down ESI+ PC(O-18:1(9Z)/16:0) 746.603 158.096 0.347 1.898 0.0190 down ESI+ 2-Methylhistamine 126.103 28.055 0.396 1.662 0.0432 down ESI+ 4-Vinylcyclohexene 108.393 51.885 0.430 1.714 0.0041 down ESI+ PC(18:2(9Z,12Z)/18:0) 786.658 198.720 0.483 1.529 0.0455 down ESI+ L-Arginine 175.201 230.124 2.257 1.622 0.0017 up ESI+ Sanguinarine 332.096 314.172 0.314 1.839 0.0174 down ESI+ Fenfuram 201.143 238.682 0.368 1.716 0.0296 down ESI+ Salvianolic acid D 340.085 24.829 0.335 1.792 0.0091 down ESI+ FF 3-Hydroxypentanedioic acid 146.965 343.145 2.233 1.529 0.0102 up ESI− (2S)-2-Amino-3-methylbutanoic acid 116.034 302.698 2.965 1.916 0.0001 up ESI− 12-Methyltridecanoic acid 227.021 27.182 2.248 1.753 0.0010 up ESI− 12-Keto-leukotriene B4 357.249 552.146 2.369 1.922 0.0003 up ESI− 16-Hydroxy hexadecanoic acid 272.097 67.627 0.265 1.732 0.0446 down ESI− (S)-3-Methyl-2-oxopentaoate 129.055 35.895 2.627 1.708 0.0071 up ESI− Hypoxanthine 134.560 179.693 0.049 1.795 0.0385 down ESI− Heptanoic acid 129.496 57.888 2.491 1.772 0.0061 up ESI− 9-Decenoic acid 168.978 76.558 0.288 1.749 0.0004 down ESI− Xanthine 150.041 210.954 0.254 1.880 0.0097 down ESI− Threonic acid 134.046 366.756 0.372 1.763 0.0382 down ESI− 6-Trans-leukotriene B4 282.049 255.888 2.396 2.083 0.0021 up ESI− 4-Methyl-2-oxopentanoate 129.055 57.887 2.364 1.833 0.0019 up ESI− cis-Vaccenic acid 281.099 292.814 0.296 1.998 0.0254 down ESI− Oleoyl glycine 338.042 84.596 2.347 1.652 0.0006 up ESI− 2-Hydroxypentanoic acid 117.038 302.727 3.322 1.683 0.0001 up ESI− Cytosine 110.035 255.589 0.408 2.001 0.0128 down ESI− Ginkgoic acid 344.289 31.545 2.181 1.638 0.0347 up ESI− Bilirubin 583.257 58.751 0.161 1.572 0.0360 down ESI− L-Valine 118.151 426.139 0.305 1.823 0.0289 down ESI− Parachlorophenol 126.904 27.189 0.456 1.871 0.0011 down ESI− Hypogeic acid 253.032 77.404 0.201 1.623 0.0078 down ESI− Deoxyinosine 250.949 178.890 0.187 1.618 0.0119 down ESI− Dopamine 152.064 98.584 0.272 1.674 0.0363 down ESI− Alantolactone 230.964 58.128 2.825 1.823 0.0018 up ESI− Thioguanine 167.076 201.053 0.361 1.636 0.0481 up ESI+ Thymine 127.050 89.752 0.449 1.596 0.0260 down ESI+ Quinoline 130.065 47.975 2.368 1.633 0.0049 down ESI+ Melphalan 436.015 26.312 2.027 1.825 0.0002 up ESI+ Urocanate 138.938 322.528 0.468 1.737 0.0203 down ESI+ 3-Methylguanine 166.072 201.246 0.384 1.560 0.0479 down ESI+ 1-Methyladenosine 282.120 130.661 0.457 1.769 0.0060 down ESI+ Lactulose 366.060 446.669 0.182 1.705 0.0273 down ESI+ Dihydroneopterin phosphate 335.074 269.055 0.145 1.860 0.0034 down ESI+ Oleamide 282.279 75.131 0.167 1.863 0.0182 down ESI+ Obtusilactone A 308.295 31.346 0.363 1.585 0.0271 down ESI+ Lys Pro Leu 357.249 552.146 0.369 1.655 0.012 down ESI+ PE(18:4(6Z,9Z,12Z,15Z)/P-18:1(11Z)) 721.557 58.744 0.303 1.679 0.0154 down ESI+ LysoPC(14:1(9Z)) 465.317 211.144 0.379 1.852 0.0070 down ESI+ 6-Hydroxy-5-methoxyindole glucuronide 340.103 313.810 2.921 1.590 0.0043 up ESI+ PE(18:3(9Z,12Z,15Z)/P-18:1(11Z)) 724.530 157.106 0.391 1.726 0.0376 down ESI+ 3-Indoleacetonitrile 157.076 33.887 2.391 1.530 0.0230 down ESI+ 1,7-Dimethylguanosine 311.146 193.370 0.266 1.782 0.0213 down ESI+ 5-Hydroxyisourate 184.037 398.320 0.228 1.737 0.0471 down ESI+ PE(P-18:1(11Z)/18:4(6Z,9Z,12Z,15Z)) 721.557 169.021 0.356 1.653 0.0310 down ESI+ SM(d18:1/22:0) 788.545 223.441 0.117 1.935 0.0176 down ESI+ PS(18:0/16:1(9Z)) 762.527 224.514 0.082 1.771 0.0452 down ESI+ 4-Hydroxybenzaldehyde 122.925 322.522 0.484 1.828 0.0142 down ESI+ 3-Methyl-1,3-dihydroindol-2-one 148.176 25.922 2.091 1.873 0.0010 up ESI+ 3,5-Digalloylepicatechin 594.139 255.876 0.326 1.889 0.0001 down ESI+ PC(P-18:1(9Z)/16:1(9Z)) 742.392 232.523 0.256 1.652 0.0406 down ESI+ Ovarian tissue Ethyl dodecanoate 227.021 25.595 2.456 2.508 0.0001 up ESI− (2,5-Dioxoimidazolidin-4-yl)urea 156.066 221.761 2.057 2.326 0.0014 up ESI− 13-L-Hydroperoxylinoleic acid 311.224 195.159 2.026 2.322 0.0052 up ESI− Benzene-1,2,4-triol 125.000 419.607 9.128 2.796 0.0245 up ESI− 12-Methyltridecanoic acid 229.371 27.182 2.248 1.753 0.0010 up ESI− 12-Keto-leukotriene B4 335.452 41.107 3.110 1.553 0.0230 up ESI− L-Valine 118.150 414.233 0.305 1.843 0.0073 down ESI− Thr-Gly 176.092 301.131 2.559 2.070 0.0311 up ESI+ 17beta-Acetamidoandrost-4-en-3-one 329.247 30.475 4.113 2.618 0.0084 up ESI+ 3-Methyl-1,3-dihydroindol-2-one 148.076 25.035 2.596 2.508 0.0001 up ESI+ [ 10 ]-Paradol 335.258 35.546 2.475 2.393 0.0132 up ESI+ Zalcitabine 212.105 338.783 49.229 2.887 0.0003 up ESI+ Amidosulfuron 370.209 450.167 2.734 2.071 0.0166 up ESI+ Allantoin 159.120 56.131 0.448 2.177 0.0437 down ESI+ FF, follicular fluid; IO, inactive ovaries; FC, fold change; RT, retention time; FC, fold change; VIP, variable importance for the projection; ESI, electrospray ionization. Pathway analysis Figure 3 illustrates the interaction network of differential metabolites constructed by querying the KEGG pathway database and integrating global metabolic data from published literature. This network highlights the relationships between the differential metabolites identified in the present study. Subsequently, using KEGG pathway analysis, distinct metabolites in the pathways of cows in the IO and HC groups were identified and visualized using a bubble diagram (Fig. 4 ). Significant changes were identified in six, three, and two metabolic pathways related to IO in the serum, FF, and ovarian tissue samples, respectively. In serum samples, the altered pathways included arginine biosynthesis ( p < 0.001); nitrogen metabolism ( p < 0.001); arginine and proline metabolism ( p < 0.001); alanine, aspartate, and glutamate metabolism ( p = 0.008); glyoxylate and dicarboxylate metabolism ( p = 0.010); and valine, leucine, and isoleucine biosynthesis ( p = 0.041). In FF samples, valine, leucine, and isoleucine biosynthesis ( p < 0.001); valine, leucine, and isoleucine degradation ( p = 0.003); and purine metabolism ( p = 0.015) were found to be altered. In ovarian tissue, the altered pathways included linoleic acid metabolism ( p = 0.013) and valine, leucine, and isoleucine biosynthesis ( p = 0.021). Notably, the biosynthesis of valine, leucine, and isoleucine was a metabolic pathway that exhibited significant changes ( p < 0.05) in serum, FF, and ovarian tissue samples. Discussion A comprehensive metabolomic analysis was conducted to elucidate the metabolic alterations in the serum, FF, and ovarian tissues of postpartum Holstein cows with IO compared to those with normal ovarian function. Using LC–MS/MS and sophisticated statistical analyses, notable differences in the metabolite profiles were identified among the serum, FF, and ovarian tissue samples, highlighting alterations in various metabolic pathways. Here, we discuss the implications of our findings in different sample types for postpartum IO, and integrate these results to offers a more comprehensive understanding of the metabolic changes associated with postpartum IO (Fig. 3 ). Nutritional status is widely acknowledged as a key factor influencing IO because of the strong relationship between postpartum ovarian function and energy balance. Our results showed that cows with IO had a significantly lower BCS and a higher milk yield from their previous lactation than healthy cows. These findings align with earlier studies, indicating that poor BCS and excessive milk yield contribute to the onset of IO [ 7 , 41 , 42 ]. Slow LF growth rate, along with low serum levels of estradiol and progesterone, were also observed in cows with IO (~ 0.57 mm/d), but not in healthy cows (~ 1.26 mm/d), which aligns with the known characteristics of follicular dynamics in IO [ 27 , 43 ]. The serum level of NEFA indicates the extent of fat mobilization [ 44 ]. An elevation in circulating NEFA is frequently a reliable marker of NEB in dairy cows [ 45 ]. The IGF-1 is essential for gonadotropin-induced folliculogenesis, ovarian steroidogenesis, and the functioning of the corpus luteum. Additionally, it regulates the functions of both the pituitary and hypothalamus [ 46 ]. Serum AST levels are routinely used in the test of liver function to assess liver damage. Cows with IO generally exhibit poor liver function [ 47 ]. Zulu et al. reported that cows with IO had elevated serum levels of NEFA and AST, along with decreased levels of IGF-1 compared to normal cows [ 48 ]. These findings align with our results in cows with IO. The specific roles of calcium and phosphorus in IO in cows have not yet been reported. In the current study, cows with IO had significantly lower serum calcium and phosphorus levels than healthy cows; however, these levels were still within the normal range: calcium (2.07–2.59 mmol/L) and phosphorus (1.36–2.49 mmol/L) [ 49 ]. A recent study showed that female mice with hypocalcemia and hypophosphatemia experience infertility, characterized by lower levels of estradiol and progesterone, elevated levels of follicle-stimulating hormone (FSH) and luteinizing hormone (LH), impaired follicular development, uterine hypoplasia, and decreased ovarian angiogenic factor expression levels [ 50 ]. Thus, it is reasonable to speculate that insufficient levels of calcium and phosphorus may play a role in the onset of IO. Collectively, IO in postpartum cows may be attributed to multiple factors, such as prolonged and more severe NEB; inadequate BCS and compromised liver function; excessive milk yield and fat mobilization; and low circulating IGF-1, calcium, and phosphorus concentrations during early lactation. Nevertheless, the synergistic mechanisms underlying several factors involved in postpartum IO require further investigation. Multiple metabolomics studies have demonstrated that postpartum IO is associated with alterations in amino acid, lipid, and carbohydrate metabolism in the serum or plasma of dairy cows [ 9 , 10 , 14 , 27 ]. In the present study, six pathways related to amino acid, energy, and carbohydrate metabolism were found to be significantly altered in the serum of cows with IO. Eight metabolites involved in these pathways were identified in the IO group. A previous study showed that circulating L-glutamate and L-glutamine levels are higher in cows with IO than in estrous cows [ 27 ], which is consistent with our results. L-glutamine is the preferred precursor of the major excitatory neurotransmitter, L-glutamate [ 51 ]. Both can be interconverted enzymatically and play crucial roles in amino acid, energy, and carbohydrate metabolism [ 52 ]. A recent study demonstrated that L-glutamate injection promotes follicular development in goats with low BCS [ 53 ]. On one hand, L-glutamate can be transformed into α-ketoglutarate, allowing it to enter the tricarboxylic acid cycle (TCA) to provide energy for follicular development [ 27 ]. On the other hand, L-glutamine is also an important precursor for the synthesis of GSH, a key antioxidant. Here, the elevated levels of serum L-glutamine and L-glutamate suggest that both processes may be inhibited by IO, leading to an insufficient energy supply and a lack of antioxidants. Our findings also support the conclusions of previous studies that cows with IO often experience NEB and oxidative stress [ 7 , 47 ]. Citrulline serves as a precursor in the synthesis of L-arginine in the urea cycle, which produces nitric oxide (NO). In cows with IO, high L-arginine and low citrulline levels in the serum suggest urea cycle dysregulation, which reduces NO synthesis. A reduction in NO levels is not conducive to ovarian blood flow and is linked to steroidogenesis, oxidative stress, and the apoptosis of granulosa cells, which can ultimately damage ovarian activity and function [ 54 , 55 ]. Guanidinoacetate, an intermediate of arginine metabolism, is a direct precursor of creatinine. Elevated serum guanidinoacetate levels in cows with IO may enhance energy storage and decrease fat mobilization, thereby supporting ovarian activity under NEB and low BCS conditions. Recent studies have established links between 3-methyl-2-oxobutanoate (an L-valine metabolite) and both liver and myocardial injury [ 56 , 57 ]. Elevated levels of serum 3-methyl-2-oxobutanoic acid in cows with IO may be related to L-valine degradation in the FF and ovarian tissue, and poor liver function. L-alpha-aminobutyric acid, commonly referred to as L-homoalanine, is a non-proteinogenic α-amino acid that has recently been found to have considerable potential for inhibiting inflammation [ 58 , 59 ]. Cows with IO usually exhibit an increased inflammatory response [ 47 ]. Elevated serum l-alpha-aminobutyric acid levels in cows with IO may be a compensatory response to inflammation. Dietary tryptophan is metabolized into indole-3-acetate by the gut microbiota [ 60 ]. Previous studies have revealed alterations in the levels of various bacterial species from the genera Lactobacillus , Bacteroides , and Streptococcus when exposed to stress conditions [ 61 – 65 ]. Thus, elevated serum indole-3-acetate levels in cows with IO may be a response to stress-induced intestinal floral disorders. This is a notable finding given established the connection between gut microbiota and the development of antral follicles in Holstein cows [ 66 ]. Multiple metabolomic investigations have demonstrated that postpartum IO is linked to changes in the metabolism of amino acid, lipid, and carbohydrate in the FF of dairy cows [ 27 , 67 ]. In the present study, three pathways associated with amino acid and nucleotide metabolism were significantly altered in the FF of cows with IO. Twelve primary metabolites involved in these pathways were identified in the IO group. Branched-chain amino acids (BCAAs) are essential for in protein synthesis, energy metabolism, and hormone regulation [ 68 ]. L-Valine levels in FF decreased, whereas the degradation products (4-methyl-2-oxopentanoate and [S]-3-methyl-2-oxopentanoate) of two other BCAAs were elevated in cows with IO in this study. This aligns with earlier research showing that plasma BCAA levels are significantly decreased in anestrus cows with NEB [ 14 ]. Deoxyinosine, hypoxanthine, and xanthine are integral to purine metabolism, whereas thymine is associated with pyrimidine metabolism. All these compounds are involved in nucleotide metabolism. Reduced levels of these metabolites in the FF of cows with IO reflect a disorder of nucleotide metabolism, which has previously been linked to adverse effects on follicular development and oocyte quality [ 69 ]. Thioguanine, an analog of the physiological purines guanine and hypoxanthine, functions as a purine anti-metabolite. In our study, thioguanine levels in the FF of cows with IO exhibited a trend opposite to that of hypoxanthine levels, supporting a competitive relationship between the two [ 70 ]. Bilirubin, a product of heme catabolism, possesses strong antioxidant and anti-inflammatory effects. In this study, a notable decrease was observed in the bilirubin levels within the FF of cows exhibiting IO. This finding supports the findings of a previous study showing that cows with IO have weakened anti-inflammatory and antioxidant capacities [ 27 ]. Urocanate is a histidine degradation product with local and systemic anti-inflammatory and immunosuppressive properties [ 71 ]. Reduced urocanate levels in the FF of cows with IO may explain their decreased immunosuppressive abilities. 12-Keto-LTB4, a metabolite of arachidonic acid, is converted from the intermediate LTB4. The production of 12-keto-LTB4 signals the inactivation of LTB4, which is an inflammatory mediator [ 72 ]. Thus, the elevated 12-keto-LTB4 levels in the FF of cows with IO reflect the metabolic regulatory capacity of cells in response to inflammation. Dopamine is an important neurotransmitter that regulates the secretion of reproductive hormones, such as prolactin, FSH, and LH [ 73 , 74 ]. When dopamine levels decrease, prolactin secretion increases, further suppressing FSH secretion, which contributes to follicular dysplasia and ultimately leads to IO [ 75 , 76 ]. Thus, low dopamine levels may be a valuable biomarker in the FF of cows with IO. The metabolomic characteristics underlying postpartum IO in the ovarian tissues of cows have not been characterized because of challenges associated with sample acquisition. To our knowledge, this study represents the first investigation into the metabolic profile of ovarian tissue in cows with IO. We found that alterations in lipid, amino acid, nucleotide, cofactor, and vitamin metabolism within the ovarian tissue were associated with IO. Six relevant metabolic pathways were identified. Linoleic acid metabolism (lipid-related) and the biosynthesis of valine, leucine, and isoleucine (amino acid-related) exhibited significant changes in the ovarian tissue of cows with IO. Four major differential metabolites involved in these pathways were identified in the IO group. 13-Hydroperoxyoctadecadienoic acid (13-HpODE), a lipid peroxidation product of linoleic acid, is a potential marker of oxidative stress. Hennet et al. found that the concentration of FF 13-HpODE was notably lower in the first subordinate follicles compared to the second subordinate follicles within each pair of ovaries [ 77 ]. Elevated levels of 13-HpODE indicate significant oxidative stress and lipid damage in the ovarian tissue of cows with IO, inhibiting oocyte maturation and subsequent development [ 78 ]. L-Valine can be metabolized to succinyl-CoA, an intermediate of the TCA cycle, which is crucial for adenosine triphosphate (ATP) production. Adequate ATP levels are essential for maintaining the energy balance in ovarian cells and supporting processes, such as follicular development and oocyte maturation [ 79 ]. Conversely, L-valine deficiency observed in both the FF and ovarian tissues of cows with IO can reduce ATP production, thereby impairing these vital reproductive processes. 12-Keto-LTB4 levels in the ovarian tissue of cows with IO showed a trend similar to that observed in the FF of these cows. As previously discussed, elevated levels of 12-keto-LTB4 in the ovarian tissue of cows with IO indicate that these cells possess a metabolic regulatory capacity in response to inflammation. Allantoin, a product of purine metabolism, has anti-inflammatory and antioxidant properties that promote cell growth. It has recently been reported that allantoin can alleviate cyclophosphamide-induced premature ovarian failure in female rats, associated with a reduction in anestrum; increased estradiol levels; decreased FSH and LH levels; ameliorated apoptosis; and decreased mitochondrial membrane potential, reactive oxygen species levels, and mitophagy in ovarian granulosa cells [ 80 ]. Reduced allantoin levels in the ovarian tissues of cows with IO may be a response to increased oxidative stress. A novel finding of our study was that 12-methyltridecanoic acid was significantly upregulated in the serum, FF, and ovarian tissues of cows with IO. 12-Methyltridecanoic acid is a branched-chain saturated fatty acid and an important component of cow milk fat. While these findings indicate that 12-methyltridecanoic acid emerges as a candidate diagnostic biomarker for IO, its clinical utility mandates rigorous validation through multicenter prospective studies with large heterogeneous cohorts. Another novel finding of our study is that valine, leucine, and isoleucine biosynthesis exhibited significant alterations in the serum, FF, and ovarian tissues of cows with IO. Given the positive relationship between BCAAs and follicular maturation, the selective targeting of the BCAA biosynthetic pathway may be an effective method for treating IO [ 81 ]. One puzzling observation is that ginkgoic acid was significantly upregulated in the serum and FF of cows with IO, despite the fact that ginkgo extract has been sanctioned for use as a feed additive [ 82 ]. 3-Methyl-1,3-dihydroindol-2-one (also known as 3-methyloxindole) is a metabolite of L-tryptophan that is formed in the rumen of cattle [ 83 ]. Elevated levels of 3-methyloxindole in both the FF and ovarian tissues of cows with IO may suggest changes in gut microbes; however, the levels of 3-methyloxindole were not notably elevated in the circulation of the cows with IO. Regrettably, the mechanism underlying this phenomenon remains unclear. Collectively, these metabolic changes underscore the importance of maintaining an energy and nutritional balance for optimal ovarian function. The comprehensive response to metabolic, oxidative, and inflammatory stresses, as evidenced by the altered levels of these metabolites, highlights the complexity of postpartum IO and the need for targeted metabolic support to improve reproductive outcomes in cows under these conditions. Conclusions Untargeted metabolomics facilitates the detection of systemic metabolic changes in dairy cows with postpartum IO. In this study, LC–MS/MS was utilized to analyze serum, FF, and ovarian tissues, identifying 40, 51, and 14 metabolites that differed between cows with IO and healthy cows. Furthermore, nine metabolic pathways associated with amino acid, energy, carbohydrate, lipid, and nucleotide metabolism were notably altered in the cows with IO, indicating a complex response to metabolic, oxidative, and inflammatory stresses. To the best of our knowledge, this represents the most comprehensive metabolic framework for cows with IO. Our findings provide novel insights into the metabolic changes associated with IO and highlight potential biomarkers for early diagnosis and therapeutic targets. Future research should use targeted metabolomics to validate these biomarkers, examine the regulatory enzymes in key metabolic pathways, and explore the underlying molecular mechanisms. Enhancing our knowledge of IO-related metabolic changes may lead to improved prevention and management strategies, improved reproductive performance, and reduced economic losses within the dairy sector. Abbreviations IO inactive ovaries FF follicular fluid HC healthy control LF largest follicle DIM days in milk BCS body condition score NEB negative energy balance LC-MS/MS liquid chromatography-tandem mass spectrometry TMR Total mixed ration PGF 2α prostaglandin F2 alpha GnRH gonadotropin-releasing hormone NEFA non-esterified fatty acids AST aspartate aminotransferase ALT alanine aminotransferase TP total protein IGF-1 insulin-like growth factor 1 PCA principal component analysis OPLS-DA orthogonal partial least squares discriminant analysis VIP variable importance for the projection FC fold change FSH follicle-stimulating hormone LH luteinizing hormone TCA tricarboxylic acid cycle NO nitric oxide BCAAs branched-chain amino acids 13-HpODE 13-Hydroperoxyoctadecadienoic acid ATP adenosine triphosphate Declarations Acknowledgements We thank Elsevier Language Editing Services for their help in English language revision of this manuscript. Authors’ Contributions Y-XS: Conceptualization, Data curation, Methodology, Validation, Writing – original draft. X-JJ: Formal analysis, Writing – original draft. YH: Software, Visualization. RS: Methodology, Validation. Y-LB: Project administration, Supervision. CX: Conceptualization, Resources, Writing – review & editing. CX: Funding acquisition, Resources, Writing – review & editing. Funding This work was supported by the National Key R&D Program of China (grant number 2023YFD1801100), the Heilongjiang Province's 'Unveiling the List and Appointing the Leader' Key Science and Technology Project (grant number 2023ZXJ02B03), the China Postdoctoral Science Foundation (grant number 2024MD753940), and the Heilongjiang Postdoctoral Financial Assistance (grant number LBH-Z23243). Availability of data and materials The data generated during this study are available from the corresponding authors upon reasonable request. 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The effect of prolactin on gene expression and the secretion of reproductive hormones in ewes during the estrus cycle. Animals. 2024;14(13):1873. Hennet ML, Yu HY, Combelles CM. Follicular fluid hydrogen peroxide and lipid hydroperoxide in bovine antral follicles of various sizes, atresia, and dominance status. J Assist Reprod Genet. 2013;30(3):333–40. Khalil WA, Marei WF, Khalid M. Protective effects of antioxidants on linoleic acid-treated bovine oocytes during maturation and subsequent embryo development. Theriogenology. 2013;80(2):161–8. Warzych E, Lipinska P. Energy metabolism of follicular environment during oocyte growth and maturation. J Reprod Dev. 2020;66(1):1–7. Wang XL, Yuan PP, Zeng MN, Sun M, Wang XY, Zheng XK, et al. Allantoin derived from Dioscorea opposita Thunb ameliorates cyclophosphamide-induced premature ovarian failure in female rats by attenuating apoptosis, autophagy and pyroptosis. Cureus. 2023;15(12):e50351. Downing JA, Joss J, Scaramuzzi RJ. 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Cite Share Download PDF Status: Published Journal Publication published 05 Jan, 2026 Read the published version in BMC Veterinary Research → Version 1 posted Editorial decision: Revision requested 13 Jun, 2025 Reviews received at journal 02 Jun, 2025 Reviewers agreed at journal 18 May, 2025 Reviews received at journal 13 May, 2025 Reviewers agreed at journal 12 May, 2025 Reviewers agreed at journal 01 Apr, 2025 Reviewers invited by journal 30 Mar, 2025 Submission checks completed at journal 26 Mar, 2025 First submitted to journal 25 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5526997","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":436033144,"identity":"33d40b16-bc13-4cdd-8a8b-80df2d9b20af","order_by":0,"name":"Yuxi Song","email":"","orcid":"","institution":"Heilongjiang Bayi Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Yuxi","middleName":"","lastName":"Song","suffix":""},{"id":436033145,"identity":"fe55570d-4f1d-4436-8809-c605f3bead91","order_by":1,"name":"Xuejie Jiang","email":"","orcid":"","institution":"Heilongjiang Bayi Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Xuejie","middleName":"","lastName":"Jiang","suffix":""},{"id":436033146,"identity":"36cf78f3-7f59-4f7b-a349-5a8924a8170c","order_by":2,"name":"Yu Hao","email":"","orcid":"","institution":"Heilongjiang Bayi Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Hao","suffix":""},{"id":436033147,"identity":"1868afe7-da7a-49f2-a10e-14ada93aeb27","order_by":3,"name":"Rui Sun","email":"","orcid":"","institution":"Heilongjiang Bayi Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Sun","suffix":""},{"id":436033148,"identity":"648d690d-4228-4ea1-bf27-1413a6fd55b0","order_by":4,"name":"Yunlong Bai","email":"","orcid":"","institution":"Heilongjiang Bayi Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Yunlong","middleName":"","lastName":"Bai","suffix":""},{"id":436033149,"identity":"0d8e75aa-bbb9-4567-a2fa-0aefc66e4a69","order_by":5,"name":"Chuang Xu","email":"","orcid":"","institution":"China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Chuang","middleName":"","lastName":"Xu","suffix":""},{"id":436033150,"identity":"55a07306-f8c0-470d-8432-0d488e285a81","order_by":6,"name":"Cheng Xia","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIie3PvQrCMBSG4VMCcYlkTUF6DQe8oQahk6KTdBAUlDhYcdW76OhYCbRLdHasCM66iIODPwhubUfBvFOG7+EQAJvtN3MSAM+jBGTuh4Nq5kmaTV6DI+YmrU7kauGc3MOElM8xM6jrCp1YkyCUIwp8OvMLiRu1cbNSSFDTYC/XDRBmGxcSLupxclF3ipo9iaGAolNM6ItIhQw1v/akIuXkcwWFO4YAKhE36p43yx0iJ9ASvklZ6V8wS+WR9XGoeCIvt3Dg8em8mLxz6PfNyufv7hV3NpvN9p89APGLSu/WJg0eAAAAAElFTkSuQmCC","orcid":"","institution":"Heilongjiang Bayi Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Cheng","middleName":"","lastName":"Xia","suffix":""}],"badges":[],"createdAt":"2024-11-26 10:38:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5526997/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5526997/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12917-025-05153-5","type":"published","date":"2026-01-05T15:59:25+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79700126,"identity":"0124fcf3-0650-44f4-a60c-7b3fa16476d8","added_by":"auto","created_at":"2025-04-01 16:31:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":306418,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariate statistical analyses of metabolites between the IO group (\u003cem\u003en\u003c/em\u003e = 6) and the HC group (\u003cem\u003en\u003c/em\u003e = 6). The PCA score plots for the serum (\u003cstrong\u003eA\u003c/strong\u003e), FF (\u003cstrong\u003eB\u003c/strong\u003e), and ovarian tissue (\u003cstrong\u003eC\u003c/strong\u003e) samples, respectively. The OPLS-DA score plots for the serum (\u003cstrong\u003eD\u003c/strong\u003e), FF (\u003cstrong\u003eE\u003c/strong\u003e), and ovarian tissue (\u003cstrong\u003eF\u003c/strong\u003e) samples, respectively. Permutation test plots (200 permutation tests) for the OPLS-DA model of the serum (\u003cstrong\u003eG\u003c/strong\u003e), FF (\u003cstrong\u003eH\u003c/strong\u003e), and ovarian tissue (\u003cstrong\u003eI\u003c/strong\u003e) samples, respectively. IO, inactive ovaries;\u003cem\u003e \u003c/em\u003eHC, healthy control; PCA, principal component analysis; FF, follicular fluid; OPLS-DA, orthogonal partial least squares discriminant analysis; R\u003csup\u003e2\u003c/sup\u003eY, goodness-of-fit parameter; Q\u003csup\u003e2\u003c/sup\u003e, predictive ability parameter.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5526997/v1/71eb024ebff53cecea729e51.png"},{"id":79700127,"identity":"ee3f9b56-c9d8-4de7-b686-70a68704bd2b","added_by":"auto","created_at":"2025-04-01 16:31:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":371404,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification and comparison of differential metabolites between the IO group (\u003cem\u003en\u003c/em\u003e = 6) and the HC group (\u003cem\u003en\u003c/em\u003e = 6). (\u003cstrong\u003eA\u003c/strong\u003e) Statistical bar charts of differential metabolites for the serum, FF, and ovarian tissue samples, respectively. (\u003cstrong\u003eB\u003c/strong\u003e) Venn analysis of differential metabolites among the serum, FF, and ovarian tissue samples. Clustering heatmap of the 40, 51, and 14 differential metabolites identified in the serum (\u003cstrong\u003eC\u003c/strong\u003e), FF (\u003cstrong\u003eD\u003c/strong\u003e), and ovarian tissue (\u003cstrong\u003eE\u003c/strong\u003e) samples, respectively. IO, inactive ovaries;\u003cem\u003e \u003c/em\u003eHC, healthy control; FF, follicular fluid.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5526997/v1/50be5f6d109aa7dcbcfaa4a5.png"},{"id":79700604,"identity":"6bf072b0-41ef-45ce-9328-27547470c91f","added_by":"auto","created_at":"2025-04-01 16:39:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":327158,"visible":true,"origin":"","legend":"\u003cp\u003eMetabolic network diagram illustrating differential metabolites in dairy cows with inactive ovaries. The symbols (+) and (-) indicate upregulation and downregulation, respectively. Blue represents serum samples, pink represents FF samples, green represents ovarian tissue samples, yellow represents common metabolites in both the serum and FF samples, purple represents common metabolites in both the FF and ovarian tissue samples, and red represents all. FF, follicular fluid; TCA, tricarboxylic acid; 6-TGN, 6-thioguanine nucleotide; LTB4, leukotriene B4; HPOD, hydroperoxy-octadecadienoic acid; 13-HpODE, 13-hydroperoxylinoleic acid.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5526997/v1/bc9db266ee2fdf1c7df5b334.png"},{"id":79700129,"identity":"8a4b6488-8a50-4129-bbb9-e525900793e1","added_by":"auto","created_at":"2025-04-01 16:31:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":232750,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment bubble map depicting the metabolic pathways associated with differential metabolites identified in serum (\u003cstrong\u003eA\u003c/strong\u003e), follicular fluid (\u003cstrong\u003eB\u003c/strong\u003e), and ovarian tissue (\u003cstrong\u003eC\u003c/strong\u003e) samples. The position and size of the bubble on the abscissa indicate the influence factor of the path in the topological analysis. The larger the bubble, the greater the influence factor. The position and color of each bubble along the x-axis indicate the \u003cem\u003ep\u003c/em\u003e-value from the enrichment analysis (-log10 \u003cem\u003ep\u003c/em\u003e-value, or negative logarithm base 10). A smaller\u003cem\u003ep\u003c/em\u003e-value corresponds to a darker color, indicating more significant enrichment.\u003cstrong\u003e \u003c/strong\u003eKEGG, Kyoto Encyclopedia of Genes and Genomes; Total, the total number of compounds in the pathway; Hits, the corresponding number of metabolites in one pathway; Expect, the expected number of matched metabolites observed in the metabolic pathway under random conditions; Holm-\u003cem\u003ep\u003c/em\u003e, the \u003cem\u003ep\u003c/em\u003e-value modified utilizing Holm-Bonferroni approach; FDR, the false discovery rate, representing the proportion of results incorrectly identified as significant; Impact, the pathway influence value calculated from pathway topology analysis.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5526997/v1/7206f918f7e746468a01ce65.png"},{"id":100070085,"identity":"65fd94c8-8df7-463a-9acf-bc53dc3c8027","added_by":"auto","created_at":"2026-01-12 16:16:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2649271,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5526997/v1/42617fab-faf1-40bf-af71-56c350a32d68.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comprehensive serum, follicular fluid, and ovarian tissue profiling reveals systemic metabolic alterations in high-yielding dairy cows with postpartum inactive ovaries","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInactive ovaries (IO) is a prevalent postpartum ovarian condition in high-yielding dairy cows, with an 8.5% incidence rate during lactation in the absence of intervention [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Around 20% of dairy cows in anestrus exhibit IO at the beginning of breeding program [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] or 63 days in milk (DIM) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The IO is characterized by transient ovarian dysfunction and loss of periodic ovarian follicular activity [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This condition, in turn, can extend calving to the first service interval, conception interval (days open), and calving interval [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Affected cows may subsequently face a higher risk of culling owing to poor reproductive performance, resulting in huge losses for the dairy industry [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHistorically, IO has been referred to using various terms, including inactive-static ovaries [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], ovarian inactivity [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], ovarian hypofunction [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and ovarian quiescence [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Currently, the widely accepted definition of IO is that both ovaries have no follicles larger than 8 or 10 mm, no corpus luteum, and the condition lasts longer than seven days [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Moreover, cows with IO have serum progesterone concentrations less than 1 ng/mL [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Many factors can contribute to IO in cows, including poor body condition score (BCS), inadequate feed intake, excessive milk yield, and improper management of stress and disease [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. A negative energy balance (NEB), associated with metabolic and hormonal alterations during early lactation, is increasingly recognized as an important contributor to IO [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Our recent study highlighted the contributing role of NEB in the development of IO [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Thus, preventing and treating IO have become crucial challenges for early lactation management in modern dairy farms.\u003c/p\u003e \u003cp\u003eFollicular fluid (FF) contains bioactive molecules that play an important role in follicular growth and oocyte maturation [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The composition of FF changes as the follicle develops, reflecting its pivotal role in oocyte meiosis, ovulation, and fertilization [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Similarly, the ovaries provide a localized environment conducive to follicular growth and hormone production [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Granulosa cells in the ovaries are vital for the folliculogenesis and steroidogenesis [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Metabolites in the FF derived from blood and cellular secretions, as well as those in ovarian tissue, necessitate comparative metabolic analyses of FF, serum, and ovarian tissue in cows with IO to understand the reproductive disorder processes underlying cattle infertility [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent advances in metabolomics have opened up new avenues for understanding the complex biochemical networks involved in these processes. Metabolomics, the in-depth study of metabolites in biological systems, is a powerful tool for elucidating metabolic alterations associated with specific physiological states or disorders [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The pathophysiology of IO in dairy cows is multifaceted and involves complex interactions between the metabolic, endocrine, and immune pathways [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Energy metabolism, liver function, and reproductive hormone regulation are critical factors that influence ovarian activity [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Previous studies have demonstrated that cows with IO exhibit distinct serum, whey, and FF metabolic profiles compared to their estrus counterparts, highlighting the capability of metabolomics in discovering biomarkers for early diagnosis and intervention [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the importance of reproductive health in dairy cows, limited information exists regarding the metabolomic changes associated with IO. Previous studies have primarily focused on hormonal and biochemical parameters [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], as well as metabolic profiles based on one or two types of samples [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]; however, comprehensive metabolomic analysis is lacking. Understanding the metabolic basis of IO may contribute to creating diagnostic instruments and treatment approaches aimed at enhancing the reproductive efficiency of dairy cows. Identifying specific metabolic markers associated with IO may facilitate early diagnosis and targeted treatment, thereby enhancing reproductive efficiency and reducing economic losses [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. This study aimed to comprehensively analyze serum, FF, and ovarian tissue samples using liquid chromatography-tandem mass spectrometry (LC\u0026ndash;MS/MS) to investigate the metabolic differences between postpartum \u003cem\u003eHolstein\u003c/em\u003e dairy cows experiencing IO and those that are healthy. Hypothetically, specific biomarkers of IO exist in serum, and changes in their levels reflect those in FF or ovarian tissue. Such biomarkers could be used to develop novel, reliable, and non-invasive diagnostic tools for IO, enhancing early intervention and effective management for affected cows. This study provides the most thorough metabolomic evaluation of IO in cows, offering a broader context for its supposition.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eAnimal handling and management\u003c/h2\u003e\n \u003cp\u003eThis study was carried out with \u003cem\u003eHolstein\u003c/em\u003e cows from a substantial intensive cattle farm in central Heilongjiang Province, China, following the guidelines of the Veterinary Medical Ethics Committee of the Ministry of Agriculture of China. Approval was obtained from the Animal Welfare and Research Ethics Committee of Heilongjiang Bayi Agricultural University (protocol code: DWKJXY2024007; approval date: January 4, 2024). The farm has a population of 3,215 cows, including 1,671 lactating animals. The average parity of the cows was 3.05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12, with an average annual milk yield of 12,010\u0026thinsp;\u0026plusmn;\u0026thinsp;1,548 kg (milk fat, 4.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32%; milk protein, 3.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22%; mean\u0026thinsp;\u003cem\u003e\u0026plusmn;\u003c/em\u003e\u0026thinsp;standard deviation [SD]). During the experimental period, 701 cows calved, and the incidence rate of postpartum IO was approximately 5.7% after estrus synchronization. Twelve multiparous \u003cem\u003eHolstein\u003c/em\u003e cows (age, 3.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62 years; parity, 2.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62; BCS, 2.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39; daily milk yield during the first 63 DIM, 45.54\u0026thinsp;\u0026plusmn;\u0026thinsp;2.18 kg/d; milk yield during the previous lactation, 13,130.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1,185.63 kg; mean\u0026thinsp;\u003cem\u003e\u0026plusmn;\u003c/em\u003e\u0026thinsp;SD) were divided into a healthy control (HC) group (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6) and an IO group (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6) based on two consecutive rectal palpation and ultrasonography examinations at 56 and 63 DIM. Corresponding data (cow identification number, herd code, and calving date) were recorded. The cows were raised in the same environment, fed the same diet, unrestricted access to fresh water, and uniform same management program. During the 60-day dry period, the cows were fed a total mixed ration (TMR) diet twice daily. After calving, the cows were milked three times each day (0500, 1300, and 2200 h) and fed a TMR diet three times each day (0600, 1400, and 2300 h) primarily consisting of corn silage. National Research Council (NRC, 2001) guidelines were followed in the formulation of the TMR for early lactation [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]. Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the components and chemical composition of the TMR diet during early lactation. Specific software (Afifarm; Afimilk, Kibbutz Afikim, Israel) was utilized to document the age, parity, and milk yield of the dairy cows. A five-point scoring method using 0.25-unit intervals was used to evaluate the BCS of the dairy cows by two well-trained veterinarians [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eIngredients and chemical composition of the diets for early lactating dairy cows\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eItem\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEarly lactation period\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIngredient, % of dry matter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWater\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCottonseed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoybean husk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOat grass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlfalfa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSilage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCorn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoybean meal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFlaked corn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMolasses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFat powder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePremix\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNutrient, % of dry matter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDry matter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCrude protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcid detergent fiber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeutral detergent fiber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCalcium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhosphorus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\u003csup\u003e1\u003c/sup\u003e Concentration per kilogram of premix DM: 40,000 IU of vitamin A, 37,000 IU of vitamin D, 500 IU of vitamin E, 30 mg of copper, 25 mg of iron, 140 mg of manganese, 140 mg of zinc, 0.8 mg of selenium.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eEstrous synchronization protocol\u003c/h3\u003e\n\u003cp\u003eThe first artificial insemination was performed using the Presynch-Ovsynch protocol on all cows [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]. In brief, the procedure began with a 25 mg intramuscular injection of prostaglandin F2 alpha (PGF\u003csub\u003e2\u0026alpha;\u003c/sub\u003e) (Lutalyse; dinoprost tromethamine; Pfizer Animal Health, New York, NY, USA) on 30\u0026thinsp;\u0026plusmn;\u0026thinsp;3 DIM, and was subsequently followed by a second 25 mg injection of PGF\u003csub\u003e2\u0026alpha;\u003c/sub\u003e after a period of 14 days (on average of 44\u0026thinsp;\u0026plusmn;\u0026thinsp;3 DIM). Twelve days post the second PGF\u003csub\u003e2\u0026alpha;\u003c/sub\u003e administration (on average of 56\u0026thinsp;\u0026plusmn;\u0026thinsp;3 DIM), the ovulation synchronization protocol commenced with the administration of 100 \u0026micro;g of gonadotropin-releasing hormone (GnRH) (Cystorelin; gonadorelin diacetate tetrahydrate; Merial, Duluth, GA, USA), followed by a third PGF\u003csub\u003e2\u0026alpha;\u003c/sub\u003e (25 mg) injection seven days later (on average of 63\u0026thinsp;\u0026plusmn;\u0026thinsp;3 DIM). A final dose of GnRH (100 \u0026micro;g) was given 56 hours after the third PGF\u003csub\u003e2\u0026alpha;\u003c/sub\u003e injection (on average of 65\u0026thinsp;\u0026plusmn;\u0026thinsp;3 DIM), but no insemination occurred 16 to 18 hours later (on average of 66\u0026thinsp;\u0026plusmn;\u0026thinsp;3 DIM).\u003c/p\u003e\n\u003ch3\u003eOvarian ultrasonography\u003c/h3\u003e\n\u003cp\u003eAt 56 and 63 DIM, all cows were examined for ovarian using transrectal ultrasonography (Easi-Scan Curve, 5.0 MHz; BCF Technology Ltd., Bellshill, UK), as described previously [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e]. Briefly, the presence of a typical corpus luteum and/or sizable follicles (\u0026gt;\u0026thinsp;10 mm) in two ultrasonographic assessments of the ovaries conducted seven days apart, along with a serum progesterone level above 1 ng/mL, were included in the HC group. Cows in the HC group were all in the luteal phase. The IO group included cows that did not have ovarian cysts, persistent corpus luteum, or follicles, as well as those lacking a normal corpus luteum and/or large follicles (\u0026gt;\u0026thinsp;10 mm). Two ultrasonographic assessments of the ovaries, conducted seven days apart, indicated that there were no significant changes in the follicular structures, and the serum progesterone concentration was \u0026lt;\u0026thinsp;1 ng/mL. Four cows with other health problems (two had ketosis, one had left displaced abomasum, and one had mastitis), as determined by two experienced veterinarians were excluded from the study. The diameter of the largest follicle (LF) in the ovaries of each cow was recorded simultaneously during both examinations. The final daily growth rate of the follicles was computed by taking the difference between the LF diameters at 63 and 56 DIM and dividing this value by seven.\u003c/p\u003e\n\u003ch3\u003eSample collection\u003c/h3\u003e\n\u003cp\u003eAt 63 DIM, blood samples were obtained from cows in both the IO and HC groups through the tail vein using disposable vacuum blood collection tubes (5 mL; Kangweishi Medical Technology Co., Ltd, Hebei, China) without anticoagulants. Following a 30-min clotting period at room temperature, a centrifuge was used to centrifuge samples for 10 min at 3,500 \u0026times; g. The serum obtained was subsequently maintained at -80℃ for later biochemical and LC\u0026ndash;MS/MS analyses. Ovarian FF and ovarian cortical tissue samples were independently harvested from the ipsilateral ovaries of both groups of cows using transvaginal ultrasound-guided puncture and aspiration puncture (Easi-Scan Curve, 5.0 MHz), as previously described [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e]. Briefly, an FF sample (100\u0026ndash;200 \u0026micro;L) was aspirated using a puncture needle from the LF in the ovaries of each cow under ultrasound guidance. An ovarian cortical tissue sample (50\u0026ndash;100 mg) was subsequently harvested from the specimen notch of the puncture needle. A centrifuge was used to centrifuge FF samples for 10 min at 12,000 \u0026times; g, and the supernatant was maintained at -80℃ for future LC\u0026ndash;MS/MS analysis. The tissues were grouped, labeled, packed into sterile freezing tubes, quickly frozen in liquid nitrogen, and maintained at -80℃ until analysis by LC\u0026ndash;MS/MS.\u003c/p\u003e\n\u003ch3\u003eSerum biochemical detection\u003c/h3\u003e\n\u003cp\u003eSerum concentrations of \u0026beta;-hydroxybutyrate, non-esterified fatty acids (NEFA), glucose, aspartate aminotransferase (AST), alanine aminotransferase (ALT), total protein (TP), calcium, phosphorus, and magnesium were measured using commercial biochemical assay kits provided by Mindray Biomedical Electronics Co., Ltd. (Shenzhen, China) and analyzed with the Mindray BS-830S fully automatic biochemistry analyzer (Mindray Biomedical Electronics Co., Ltd.) at the Biotechnology Centre of Heilongjiang Bayi Agricultural University. All analyses were performed according to the instructions of manufacturer.\u003c/p\u003e\n\u003cp\u003eSerum concentrations of estradiol and progesterone were assessed utilizing two commercial kits from the same batch (Xinfan Biotechnology Co., Ltd., Shanghai, China) and quantified using an XH-6080 radioimmunocounter (Xian Nuclear Instrument Co., Ltd., Xian, China) following the validated radioimmunoassay protocols established by the manufacturer [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e]. There was a sensitivity of 2 pg/mL for estradiol and 0.2 ng/mL for progesterone in the assays. The intra-assay coefficient of variation was \u0026lt;\u0026thinsp;10% and the inter-assay coefficient of variation was \u0026lt;\u0026thinsp;15%.\u003c/p\u003e\n\u003cp\u003eSerum insulin-like growth factor 1 (IGF-1) levels were determined using a bovine-specific ELISA kit (Xinfan Biotechnology Co., Ltd.). Quantification was carried out with a Multiskan FC microplate reader (Thermo Fisher Scientific Co., Ltd., Shanghai, China) at a wavelength of 450 nm, following the manufacturer\u0026apos;s guidelines.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eSample preparation\u003c/h2\u003e\n \u003cp\u003ePreparation of samples for LC-MS/MS analysis was carried out according to the previous description [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]. In brief, 100 \u0026micro;L of serum or FF was combined with 300 \u0026micro;L of methanol including 1 \u0026micro;g/mL L-2-chlorophenylalanine, serving as the internal standard. The sample was subjected to vortex mixing for 30 s, followed by sonication for 10 min in an ice-water bath. It was then incubated at -40℃ for 1 h to allow protein precipitation, after which it was centrifuged at 12,000 \u0026times; g for 15 min at 4℃. The supernatant was carefully transferred to a new glass vial for subsequent LC-MS/MS analysis. Ovarian cortical tissue was ground in liquid nitrogen. Fifty milligrams of the ground sample was combined with 1,000 \u0026micro;L of extraction solvent (methanol: acetonitrile: water in a 2:2:1 ratio) including 1 \u0026micro;g/mL L-2-chlorophenylalanine as the internal standard, vortexed for 30 s, homogenized at 35 Hz for 4 min, and sonicated for 5 min on ice. The cycles of homogenization and sonication were carried out three times. The mixture was then incubated for 1 h at -40℃, followed by centrifugation at 12,000 \u0026times; g at 4℃ for 15 min, and the supernatant obtained was collected into a new sampling bottle for LC\u0026ndash;MS/MS analysis. To prepare a quality control sample, equal volumes of supernatants from all samples were combined.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eLC\u0026ndash;MS/MS analysis\u003c/h3\u003e\n\u003cp\u003eThe LC\u0026ndash;MS/MS analyses were conducted utilizing a Vanquish UHPLC system (Thermo Fisher Scientific) equipped with a UPLC BEH Amide column (2.1 mm \u0026times; 100 mm, 1.7 \u0026micro;m) connected to a Q Exactive HFX mass spectrometer (Orbitrap MS, Thermo Fisher Scientific). The mobile phase comprised 25 mmol/L ammonium acetate and 25 mmol/L ammonia hydroxide in water (pH 9.75) (A) and acetonitrile (B). The elution gradient was as follows: from 0 to 0.5 min, 95% B; from 0.5 to 7.0 min, a linear decrease from 95\u0026ndash;65% B; from 7.0 to 8.0 min, a further decrease from 65\u0026ndash;40% B; from 8.0 to 9.0 min, maintained at 40% B; from 9.0 to 9.1 min, a rapid increase from 40\u0026ndash;95% B; and from 9.1 to 12.0 min, maintained at 95% B. The mobile phase flowed at a rate of 0.5 mL/min, with the column maintained at 35\u0026deg;C and the autosampler at 4\u0026deg;C, while the injection volume was set to 3 \u0026micro;L. A QE HFX mass spectrometer was utilized due to its capability to obtain MS/MS spectra in an information-dependent acquisition mode, managed by the acquisition software (Xcalibur, Thermo Fisher Scientific). In this mode, the software for acquisition consistently assessed the complete mass spectrometry spectrum obtained from full scans. ESI source parameters were configured as follows: sheath gas flow rate, 50 Arb; Aux gas flow rate, 10 Arb; capillary temperature at 320℃. The full MS resolution was established at 60,000, while the MS/MS resolution was set to 7,500. Collision energy values were 10/30/60 in NCE mode, and the spray voltage was configured to 3.5 kV for positive-ion mode or -3.2 kV for negative-ion mode.\u003c/p\u003e\n\u003ch3\u003eData processing and multivariate statistical analyses\u003c/h3\u003e\n\u003cp\u003eProteoWizard was used to convert raw data to mzXML [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]. R package XCMS was used to align peak areas, adjust retention times, and extract peak areas [\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e]. For the data processed with XCMS, ion peak data with more than 50% missing values within a group were deleted. After Pareto-scaling preprocessing, pattern recognition was carried out utilizing SIMCA-P software (version 14.1; Umetrics, Umea, Sweden). This analysis encompassed unsupervised principal component analysis (PCA) as well as supervised orthogonal partial least squares discriminant analysis (OPLS-DA). PCA was employed to assess the aggregation of data within groups and the separation between groups, while OPLS-DA was utilized to investigate inter-group differences in greater detail. Validation of the OPLS-DA models was achieved using variations in Y (R\u003csup\u003e2\u003c/sup\u003eY) and the model forecasting ability (Q\u003csup\u003e2\u003c/sup\u003e) using cross-validation and permutation tests with 200 iterations. Models were considered stable and reliable when 1\u0026thinsp;\u0026ge;\u0026thinsp;R\u003csup\u003e2\u003c/sup\u003eY and Q\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.4 [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e]. Additionally, a Q\u003csup\u003e2\u003c/sup\u003e intercept of less than 0.05 in the permutation test was applied to check for overfitting [\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e]. Univariate analyses were additionally conducted, incorporating Student\u0026rsquo;s t-test and fold-change (FC) analysis.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eMetabolite identification and pathway analysis\u003c/h2\u003e\n \u003cp\u003eMetabolites with significantly different levels between groups were identified based on variable importance in the projection (VIP) scores (VIP\u0026thinsp;\u0026ge;\u0026thinsp;1.5) derived from the OPLS-DA model, along with FC criteria (FC\u0026thinsp;\u0026ge;\u0026thinsp;2 or FC\u0026thinsp;\u0026le;\u0026thinsp;0.5) from univariate analysis, and \u003cem\u003ep\u003c/em\u003e-values (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) obtained from the Student\u0026rsquo;s t-test. Differential metabolites were visualized utilizing bar graphs and Venn diagrams created with GraphPad Prism 9.0 (GraphPad Software Inc., San Diego, CA, USA). An Euclidean distance matrix was established for the mathematical analysis of differential metabolites in each series of analyses. Divergent metabolites were clustered using the complete linkage method, which was illustrated as a heatmap representing hierarchical clustering. The Human Metabolome Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://hmdb.ca/\u003c/span\u003e\u003c/span\u003e, last accessed on September 30, 2024) and Kyoto Encyclopedia of Genes and Genomes (KEGG) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.kegg.jp/kegg/pathway.html\u003c/span\u003e\u003c/span\u003e, last accessed on September 30, 2024) were used to identify the differential metabolites associated with various metabolic pathways. The results of the metabolic pathway analysis were presented in a bubble plot. The clustering heatmap and bubble plot for the differential metabolites were visualized using MetaboAnalyst 6.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://new.metaboanalyst.ca/\u003c/span\u003e\u003c/span\u003e; last accessed on September 30, 2024).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eEach individual cow was considered an experimental unit. Statistical analyses were conducted utilizing IBM SPSS Statistics for Windows (version 26.0; IBM Corp., Armonk, NY, USA). An independent-samples Student\u0026rsquo;s t-test was utilized to assess differences in clinical and serum biochemical variables between the IO and HC groups. \u0026ldquo;NormalizeData\u0026rdquo; function was used to normalize the data. Data points greater than three SDs from the mean were removed as outliers. Data are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. Statistical significance was defined as \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eClinical characteristics and serum biochemical measurements\u003c/h2\u003e \u003cp\u003eThe clinical characteristics and serum biochemical indicators of the IO and HC groups are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Although both groups were similar in age, parity, milk yield during the first 63 DIM, and LF diameter at 56 DIM (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), cows in the IO group exhibited a higher milk yield during the previous lactation, along with a lower BCS, a smaller LF diameter at 63 DIM, and a slower LF growth rate (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Additionally, the IO group exhibited higher serum NEFA and AST levels and lower serum estradiol, progesterone, IGF-1, calcium, and phosphorus levels than the HC group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). No significant differences were observed in the other serum biochemical parameters between the groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics and serum biochemical indicators of the IO and HC groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIO group (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHC group (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.426\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMilk yield during the first 63 DIM (kg/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e44.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e46.35\u0026thinsp;\u0026plusmn;\u0026thinsp;2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMilk yield during the previous lactation (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e14,133.33\u0026thinsp;\u0026plusmn;\u0026thinsp;744.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e12,126.67\u0026thinsp;\u0026plusmn;\u0026thinsp;349.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLF diameter at 56 DIM (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLF diameter at 63 DIM (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e7.50\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e12.83\u0026thinsp;\u0026plusmn;\u0026thinsp;1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLF growth rate (mm/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEstradiol concentration (pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e7.29\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e20.78\u0026thinsp;\u0026plusmn;\u0026thinsp;3.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProgesterone concentration (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e6.37\u0026thinsp;\u0026plusmn;\u0026thinsp;2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIGF-1 concentration (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e27.63\u0026thinsp;\u0026plusmn;\u0026thinsp;2.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e84.22\u0026thinsp;\u0026plusmn;\u0026thinsp;8.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBHB concentration (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.793\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEFA concentration (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose concentration (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.409\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST activity (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e81.67\u0026thinsp;\u0026plusmn;\u0026thinsp;10.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e56.17\u0026thinsp;\u0026plusmn;\u0026thinsp;10.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT activity (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e20.00\u0026thinsp;\u0026plusmn;\u0026thinsp;4.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e24.67\u0026thinsp;\u0026plusmn;\u0026thinsp;10.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.331\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP concentration (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e63.63\u0026thinsp;\u0026plusmn;\u0026thinsp;14.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e59.32\u0026thinsp;\u0026plusmn;\u0026thinsp;14.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.612\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium concentration (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphorus concentration (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMagnesium concentration (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.815\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eIO, inactive ovaries; HC, healthy control; BCS, body condition score; DIM, days in milk; LF, largest follicle; IGF-1, insulin-like growth factor 1; BHB, β-hydroxybutyric acid; NEFA, non-esterified fatty acids; AST, aspartate aminotransferase; ALT, alanine aminotransferase; TP, total protein.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMultivariate statistical analysis\u003c/h2\u003e \u003cp\u003eA total of 12,570, 11,352, and 12,643 peaks were obtained in the serum, FF, and ovarian tissue samples, respectively. Subsequent screening of these peaks led to the identification of 1,542 metabolites in the serum, 1,536 metabolites in the FF, and 1,569 metabolites in the ovarian tissue. Unsupervised PCA was utilized to observe the overall distribution of samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, B, and C). The PCA score plots indicated a partial overlap of the serum, FF, and ovarian tissue samples between the IO and HC groups. To better distinguish between the IO and HC groups and enhance the effectiveness and analytical capability of the model, supervised OPLS-DA analysis was conducted (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD, E, and F). According to the OPLS-DA score plots, the IO and HC groups were significantly different, indicating notable differences in metabolites between the groups. The R\u003csup\u003e2\u003c/sup\u003eY and Q\u003csup\u003e2\u003c/sup\u003e values of the OPLS-DA score plots for the serum, FF, and ovarian tissue samples were 1 and 0.965, 1 and 0.966, and 1 and 0.792, respectively, all of which were close to 1, suggesting that the model was both stable and reliable. To assess the risk of overfitting in the OPLS-DA model, its quality was examined using the 200-response permutation test. The Q\u003csup\u003e2\u003c/sup\u003e intercept values were less than 0.05, indicating there no overfitting (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG, H, and I).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDifferential metabolite analysis\u003c/h2\u003e \u003cp\u003eDifferential metabolite screening was conducted using the criteria of VIP\u0026thinsp;\u0026ge;\u0026thinsp;1.5, FC\u0026thinsp;\u0026ge;\u0026thinsp;2 or FC\u0026thinsp;\u0026le;\u0026thinsp;0.5, and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the upregulation and downregulation of differential metabolites in the serum, FF, and ovarian tissues of cows in the IO group relative to the HC group. Forty differential metabolites were identified in the serum samples, of which 16 were upregulated and 24 were downregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Fifty-one differential metabolites were identified in the FF samples, of which 16 were upregulated and 35 were downregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Fourteen differential metabolites were identified in the ovarian tissue, with 12 upregulated and two downregulated metabolites (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Moreover, the differential metabolites identified in both the serum and FF samples included ginkgoic acid and 12-methyltridecanoic acid (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Only 12-methyltridecanoic acid was detected at differential levels in both serum and ovarian tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The ovarian tissue and FF contained differential levels of L-valine, 3-methyl-1,3-dihydroindol-2-one, 12-keto-leukotriene B4 (12-Keto-LTB4), and 12-methyltridecanoic acid (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Notably, 12-methyltridecanoic acid was upregulated in the IO group compared to the HC group in serum, FF, and ovarian tissue samples (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The relative abundances of 40, 51, and 14 differential metabolites in the serum, FF, and ovarian tissue were compared between the IO and HC groups, respectively, revealing a distinct clustering pattern on a heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, D, and E).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifferential metabolites in the serum, FF, and ovarian tissue of the IO and HC groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetabolites names\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003em/z\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRT (min)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVIP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIO \u003cem\u003evs.\u003c/em\u003e HC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMode\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"39\" rowspan=\"40\"\u003e \u003cp\u003eSerum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12-Methyltridecanoic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e227.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEthyl hexadecanoate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e283.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e115.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndole-3-acetate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e173.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e149.562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePalmityl trifluoromethyl ketone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e307.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90.461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGinkgoic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e345.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(R)-3-Hydroxybutanoate acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e105.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e206.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3-alpha,20-alpha-Dihydroxy-5-beta-pregnane 3-Glucuronide PE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e495.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e235.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN-Acetyl-L-tyrosine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e222.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e213.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(s)-2-Aminobutanoate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e104.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e216.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eO-Desmethylnaproxen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e214.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e246.812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL-Ribulose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e149.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e315.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTegaserod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e299.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e296.379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13-Hpotre\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e308.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3-Methyl-2-oxobutanate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e115.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64.893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaslinic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e472.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN1-(2-Hydroxyethyl)flurazepam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e333.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e310.260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL-Glutamine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e147.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCitrulline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e177.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e429.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL-Glutamate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e148.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,3-Diisopropylbenzene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e163.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL-alpha-Aminobutyric acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e104.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e345.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDimethyl dialkyl ammonium chloride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e303.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eO-decanoyl-R-carnitine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e316.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e242.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2-Aminopentanedioic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e147.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e393.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMangiferdesmethylursanone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e429.373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45.972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEthylbenzene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e107.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePC(22:4(7Z,10Z,13Z,16Z)/17:1(9Z))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e821.627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e149.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGuanidoinoacetate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e118.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e418.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnandamide (20:l, n-9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e354.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e111.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5-Methoxytryptophan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e235.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePE(O-16:0/18:4(6Z,9Z,12Z,15Z))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e698.513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e160.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePC(P-16:0/18:4(6Z,9Z,12Z,15Z))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e737.580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e166.753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePC(O-18:1(9Z)/16:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e746.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e158.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2-Methylhistamine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e126.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4-Vinylcyclohexene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e108.393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePC(18:2(9Z,12Z)/18:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e786.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e198.720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL-Arginine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e175.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e230.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSanguinarine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e332.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e314.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFenfuram\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e201.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e238.682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSalvianolic acid D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e340.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"50\" rowspan=\"51\"\u003e \u003cp\u003eFF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3-Hydroxypentanedioic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e146.965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e343.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2S)-2-Amino-3-methylbutanoic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e116.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e302.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12-Methyltridecanoic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e227.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12-Keto-leukotriene B4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e357.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e552.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16-Hydroxy hexadecanoic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e272.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e67.627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(S)-3-Methyl-2-oxopentaoate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e129.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypoxanthine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e134.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e179.693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeptanoic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e129.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57.888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9-Decenoic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e168.978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e76.558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eXanthine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e210.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThreonic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e134.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e366.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6-Trans-leukotriene B4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e282.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e255.888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4-Methyl-2-oxopentanoate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e129.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecis-Vaccenic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e281.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e292.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOleoyl glycine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e338.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e84.596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2-Hydroxypentanoic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e117.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e302.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCytosine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e110.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e255.589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGinkgoic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e344.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBilirubin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e583.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58.751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL-Valine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e118.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e426.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParachlorophenol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e126.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypogeic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e253.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77.404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDeoxyinosine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e250.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e178.890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDopamine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e152.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98.584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlantolactone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e230.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThioguanine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e167.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e201.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThymine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e127.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e89.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQuinoline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e130.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMelphalan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e436.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrocanate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e138.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e322.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3-Methylguanine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e166.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e201.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1-Methyladenosine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e282.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e130.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLactulose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e366.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e446.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDihydroneopterin phosphate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e335.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e269.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOleamide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e282.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObtusilactone A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e308.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLys Pro Leu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e357.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e552.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePE(18:4(6Z,9Z,12Z,15Z)/P-18:1(11Z))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e721.557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58.744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLysoPC(14:1(9Z))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e465.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e211.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6-Hydroxy-5-methoxyindole glucuronide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e340.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e313.810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePE(18:3(9Z,12Z,15Z)/P-18:1(11Z))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e724.530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e157.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3-Indoleacetonitrile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e157.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,7-Dimethylguanosine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e311.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e193.370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5-Hydroxyisourate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e184.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e398.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePE(P-18:1(11Z)/18:4(6Z,9Z,12Z,15Z))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e721.557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e169.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSM(d18:1/22:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e788.545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e223.441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePS(18:0/16:1(9Z))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e762.527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e224.514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4-Hydroxybenzaldehyde\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e122.925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e322.522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3-Methyl-1,3-dihydroindol-2-one\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e148.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,5-Digalloylepicatechin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e594.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e255.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePC(P-18:1(9Z)/16:1(9Z))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e742.392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e232.523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"13\" rowspan=\"14\"\u003e \u003cp\u003eOvarian tissue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEthyl dodecanoate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e227.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2,5-Dioxoimidazolidin-4-yl)urea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e156.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e221.761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13-L-Hydroperoxylinoleic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e311.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e195.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBenzene-1,2,4-triol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e419.607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12-Methyltridecanoic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e229.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12-Keto-leukotriene B4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e335.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL-Valine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e118.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e414.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThr-Gly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e176.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e301.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17beta-Acetamidoandrost-4-en-3-one\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e329.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3-Methyl-1,3-dihydroindol-2-one\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e148.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]-Paradol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e335.258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZalcitabine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e212.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e338.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmidosulfuron\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e370.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e450.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAllantoin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e159.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eESI+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eFF, follicular fluid; IO, inactive ovaries; FC, fold change; RT, retention time; FC, fold change; VIP, variable importance for the projection; ESI, electrospray ionization.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003ePathway analysis\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the interaction network of differential metabolites constructed by querying the KEGG pathway database and integrating global metabolic data from published literature. This network highlights the relationships between the differential metabolites identified in the present study. Subsequently, using KEGG pathway analysis, distinct metabolites in the pathways of cows in the IO and HC groups were identified and visualized using a bubble diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Significant changes were identified in six, three, and two metabolic pathways related to IO in the serum, FF, and ovarian tissue samples, respectively. In serum samples, the altered pathways included arginine biosynthesis (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001); nitrogen metabolism (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001); arginine and proline metabolism (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001); alanine, aspartate, and glutamate metabolism (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008); glyoxylate and dicarboxylate metabolism (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010); and valine, leucine, and isoleucine biosynthesis (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041). In FF samples, valine, leucine, and isoleucine biosynthesis (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001); valine, leucine, and isoleucine degradation (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003); and purine metabolism (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015) were found to be altered. In ovarian tissue, the altered pathways included linoleic acid metabolism (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013) and valine, leucine, and isoleucine biosynthesis (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021). Notably, the biosynthesis of valine, leucine, and isoleucine was a metabolic pathway that exhibited significant changes (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in serum, FF, and ovarian tissue samples.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eA comprehensive metabolomic analysis was conducted to elucidate the metabolic alterations in the serum, FF, and ovarian tissues of postpartum \u003cem\u003eHolstein\u003c/em\u003e cows with IO compared to those with normal ovarian function. Using LC\u0026ndash;MS/MS and sophisticated statistical analyses, notable differences in the metabolite profiles were identified among the serum, FF, and ovarian tissue samples, highlighting alterations in various metabolic pathways. Here, we discuss the implications of our findings in different sample types for postpartum IO, and integrate these results to offers a more comprehensive understanding of the metabolic changes associated with postpartum IO (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNutritional status is widely acknowledged as a key factor influencing IO because of the strong relationship between postpartum ovarian function and energy balance. Our results showed that cows with IO had a significantly lower BCS and a higher milk yield from their previous lactation than healthy cows. These findings align with earlier studies, indicating that poor BCS and excessive milk yield contribute to the onset of IO [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Slow LF growth rate, along with low serum levels of estradiol and progesterone, were also observed in cows with IO (~\u0026thinsp;0.57 mm/d), but not in healthy cows (~\u0026thinsp;1.26 mm/d), which aligns with the known characteristics of follicular dynamics in IO [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The serum level of NEFA indicates the extent of fat mobilization [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. An elevation in circulating NEFA is frequently a reliable marker of NEB in dairy cows [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The IGF-1 is essential for gonadotropin-induced folliculogenesis, ovarian steroidogenesis, and the functioning of the corpus luteum. Additionally, it regulates the functions of both the pituitary and hypothalamus [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Serum AST levels are routinely used in the test of liver function to assess liver damage. Cows with IO generally exhibit poor liver function [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Zulu et al. reported that cows with IO had elevated serum levels of NEFA and AST, along with decreased levels of IGF-1 compared to normal cows [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. These findings align with our results in cows with IO.\u003c/p\u003e \u003cp\u003eThe specific roles of calcium and phosphorus in IO in cows have not yet been reported. In the current study, cows with IO had significantly lower serum calcium and phosphorus levels than healthy cows; however, these levels were still within the normal range: calcium (2.07\u0026ndash;2.59 mmol/L) and phosphorus (1.36\u0026ndash;2.49 mmol/L) [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. A recent study showed that female mice with hypocalcemia and hypophosphatemia experience infertility, characterized by lower levels of estradiol and progesterone, elevated levels of follicle-stimulating hormone (FSH) and luteinizing hormone (LH), impaired follicular development, uterine hypoplasia, and decreased ovarian angiogenic factor expression levels [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Thus, it is reasonable to speculate that insufficient levels of calcium and phosphorus may play a role in the onset of IO. Collectively, IO in postpartum cows may be attributed to multiple factors, such as prolonged and more severe NEB; inadequate BCS and compromised liver function; excessive milk yield and fat mobilization; and low circulating IGF-1, calcium, and phosphorus concentrations during early lactation. Nevertheless, the synergistic mechanisms underlying several factors involved in postpartum IO require further investigation.\u003c/p\u003e \u003cp\u003eMultiple metabolomics studies have demonstrated that postpartum IO is associated with alterations in amino acid, lipid, and carbohydrate metabolism in the serum or plasma of dairy cows [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In the present study, six pathways related to amino acid, energy, and carbohydrate metabolism were found to be significantly altered in the serum of cows with IO. Eight metabolites involved in these pathways were identified in the IO group. A previous study showed that circulating L-glutamate and L-glutamine levels are higher in cows with IO than in estrous cows [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], which is consistent with our results. L-glutamine is the preferred precursor of the major excitatory neurotransmitter, L-glutamate [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Both can be interconverted enzymatically and play crucial roles in amino acid, energy, and carbohydrate metabolism [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. A recent study demonstrated that L-glutamate injection promotes follicular development in goats with low BCS [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. On one hand, L-glutamate can be transformed into α-ketoglutarate, allowing it to enter the tricarboxylic acid cycle (TCA) to provide energy for follicular development [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. On the other hand, L-glutamine is also an important precursor for the synthesis of GSH, a key antioxidant. Here, the elevated levels of serum L-glutamine and L-glutamate suggest that both processes may be inhibited by IO, leading to an insufficient energy supply and a lack of antioxidants. Our findings also support the conclusions of previous studies that cows with IO often experience NEB and oxidative stress [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCitrulline serves as a precursor in the synthesis of L-arginine in the urea cycle, which produces nitric oxide (NO). In cows with IO, high L-arginine and low citrulline levels in the serum suggest urea cycle dysregulation, which reduces NO synthesis. A reduction in NO levels is not conducive to ovarian blood flow and is linked to steroidogenesis, oxidative stress, and the apoptosis of granulosa cells, which can ultimately damage ovarian activity and function [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Guanidinoacetate, an intermediate of arginine metabolism, is a direct precursor of creatinine. Elevated serum guanidinoacetate levels in cows with IO may enhance energy storage and decrease fat mobilization, thereby supporting ovarian activity under NEB and low BCS conditions. Recent studies have established links between 3-methyl-2-oxobutanoate (an L-valine metabolite) and both liver and myocardial injury [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Elevated levels of serum 3-methyl-2-oxobutanoic acid in cows with IO may be related to L-valine degradation in the FF and ovarian tissue, and poor liver function. L-alpha-aminobutyric acid, commonly referred to as L-homoalanine, is a non-proteinogenic α-amino acid that has recently been found to have considerable potential for inhibiting inflammation [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Cows with IO usually exhibit an increased inflammatory response [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Elevated serum l-alpha-aminobutyric acid levels in cows with IO may be a compensatory response to inflammation. Dietary tryptophan is metabolized into indole-3-acetate by the gut microbiota [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Previous studies have revealed alterations in the levels of various bacterial species from the genera \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eBacteroides\u003c/em\u003e, and \u003cem\u003eStreptococcus\u003c/em\u003e when exposed to stress conditions [\u003cspan additionalcitationids=\"CR62 CR63 CR64\" citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Thus, elevated serum indole-3-acetate levels in cows with IO may be a response to stress-induced intestinal floral disorders. This is a notable finding given established the connection between gut microbiota and the development of antral follicles in \u003cem\u003eHolstein\u003c/em\u003e cows [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMultiple metabolomic investigations have demonstrated that postpartum IO is linked to changes in the metabolism of amino acid, lipid, and carbohydrate in the FF of dairy cows [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. In the present study, three pathways associated with amino acid and nucleotide metabolism were significantly altered in the FF of cows with IO. Twelve primary metabolites involved in these pathways were identified in the IO group. Branched-chain amino acids (BCAAs) are essential for in protein synthesis, energy metabolism, and hormone regulation [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. L-Valine levels in FF decreased, whereas the degradation products (4-methyl-2-oxopentanoate and [S]-3-methyl-2-oxopentanoate) of two other BCAAs were elevated in cows with IO in this study. This aligns with earlier research showing that plasma BCAA levels are significantly decreased in anestrus cows with NEB [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Deoxyinosine, hypoxanthine, and xanthine are integral to purine metabolism, whereas thymine is associated with pyrimidine metabolism. All these compounds are involved in nucleotide metabolism. Reduced levels of these metabolites in the FF of cows with IO reflect a disorder of nucleotide metabolism, which has previously been linked to adverse effects on follicular development and oocyte quality [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Thioguanine, an analog of the physiological purines guanine and hypoxanthine, functions as a purine anti-metabolite. In our study, thioguanine levels in the FF of cows with IO exhibited a trend opposite to that of hypoxanthine levels, supporting a competitive relationship between the two [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Bilirubin, a product of heme catabolism, possesses strong antioxidant and anti-inflammatory effects. In this study, a notable decrease was observed in the bilirubin levels within the FF of cows exhibiting IO. This finding supports the findings of a previous study showing that cows with IO have weakened anti-inflammatory and antioxidant capacities [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Urocanate is a histidine degradation product with local and systemic anti-inflammatory and immunosuppressive properties [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Reduced urocanate levels in the FF of cows with IO may explain their decreased immunosuppressive abilities. 12-Keto-LTB4, a metabolite of arachidonic acid, is converted from the intermediate LTB4. The production of 12-keto-LTB4 signals the inactivation of LTB4, which is an inflammatory mediator [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. Thus, the elevated 12-keto-LTB4 levels in the FF of cows with IO reflect the metabolic regulatory capacity of cells in response to inflammation. Dopamine is an important neurotransmitter that regulates the secretion of reproductive hormones, such as prolactin, FSH, and LH [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. When dopamine levels decrease, prolactin secretion increases, further suppressing FSH secretion, which contributes to follicular dysplasia and ultimately leads to IO [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. Thus, low dopamine levels may be a valuable biomarker in the FF of cows with IO.\u003c/p\u003e \u003cp\u003eThe metabolomic characteristics underlying postpartum IO in the ovarian tissues of cows have not been characterized because of challenges associated with sample acquisition. To our knowledge, this study represents the first investigation into the metabolic profile of ovarian tissue in cows with IO. We found that alterations in lipid, amino acid, nucleotide, cofactor, and vitamin metabolism within the ovarian tissue were associated with IO. Six relevant metabolic pathways were identified. Linoleic acid metabolism (lipid-related) and the biosynthesis of valine, leucine, and isoleucine (amino acid-related) exhibited significant changes in the ovarian tissue of cows with IO. Four major differential metabolites involved in these pathways were identified in the IO group. 13-Hydroperoxyoctadecadienoic acid (13-HpODE), a lipid peroxidation product of linoleic acid, is a potential marker of oxidative stress. Hennet et al. found that the concentration of FF 13-HpODE was notably lower in the first subordinate follicles compared to the second subordinate follicles within each pair of ovaries [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. Elevated levels of 13-HpODE indicate significant oxidative stress and lipid damage in the ovarian tissue of cows with IO, inhibiting oocyte maturation and subsequent development [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. L-Valine can be metabolized to succinyl-CoA, an intermediate of the TCA cycle, which is crucial for adenosine triphosphate (ATP) production. Adequate ATP levels are essential for maintaining the energy balance in ovarian cells and supporting processes, such as follicular development and oocyte maturation [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. Conversely, L-valine deficiency observed in both the FF and ovarian tissues of cows with IO can reduce ATP production, thereby impairing these vital reproductive processes. 12-Keto-LTB4 levels in the ovarian tissue of cows with IO showed a trend similar to that observed in the FF of these cows. As previously discussed, elevated levels of 12-keto-LTB4 in the ovarian tissue of cows with IO indicate that these cells possess a metabolic regulatory capacity in response to inflammation. Allantoin, a product of purine metabolism, has anti-inflammatory and antioxidant properties that promote cell growth. It has recently been reported that allantoin can alleviate cyclophosphamide-induced premature ovarian failure in female rats, associated with a reduction in anestrum; increased estradiol levels; decreased FSH and LH levels; ameliorated apoptosis; and decreased mitochondrial membrane potential, reactive oxygen species levels, and mitophagy in ovarian granulosa cells [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. Reduced allantoin levels in the ovarian tissues of cows with IO may be a response to increased oxidative stress.\u003c/p\u003e \u003cp\u003eA novel finding of our study was that 12-methyltridecanoic acid was significantly upregulated in the serum, FF, and ovarian tissues of cows with IO. 12-Methyltridecanoic acid is a branched-chain saturated fatty acid and an important component of cow milk fat. While these findings indicate that 12-methyltridecanoic acid emerges as a candidate diagnostic biomarker for IO, its clinical utility mandates rigorous validation through multicenter prospective studies with large heterogeneous cohorts. Another novel finding of our study is that valine, leucine, and isoleucine biosynthesis exhibited significant alterations in the serum, FF, and ovarian tissues of cows with IO. Given the positive relationship between BCAAs and follicular maturation, the selective targeting of the BCAA biosynthetic pathway may be an effective method for treating IO [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. One puzzling observation is that ginkgoic acid was significantly upregulated in the serum and FF of cows with IO, despite the fact that ginkgo extract has been sanctioned for use as a feed additive [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]. 3-Methyl-1,3-dihydroindol-2-one (also known as 3-methyloxindole) is a metabolite of L-tryptophan that is formed in the rumen of cattle [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]. Elevated levels of 3-methyloxindole in both the FF and ovarian tissues of cows with IO may suggest changes in gut microbes; however, the levels of 3-methyloxindole were not notably elevated in the circulation of the cows with IO. Regrettably, the mechanism underlying this phenomenon remains unclear. Collectively, these metabolic changes underscore the importance of maintaining an energy and nutritional balance for optimal ovarian function. The comprehensive response to metabolic, oxidative, and inflammatory stresses, as evidenced by the altered levels of these metabolites, highlights the complexity of postpartum IO and the need for targeted metabolic support to improve reproductive outcomes in cows under these conditions.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eUntargeted metabolomics facilitates the detection of systemic metabolic changes in dairy cows with postpartum IO. In this study, LC\u0026ndash;MS/MS was utilized to analyze serum, FF, and ovarian tissues, identifying 40, 51, and 14 metabolites that differed between cows with IO and healthy cows. Furthermore, nine metabolic pathways associated with amino acid, energy, carbohydrate, lipid, and nucleotide metabolism were notably altered in the cows with IO, indicating a complex response to metabolic, oxidative, and inflammatory stresses. To the best of our knowledge, this represents the most comprehensive metabolic framework for cows with IO. Our findings provide novel insights into the metabolic changes associated with IO and highlight potential biomarkers for early diagnosis and therapeutic targets. Future research should use targeted metabolomics to validate these biomarkers, examine the regulatory enzymes in key metabolic pathways, and explore the underlying molecular mechanisms. Enhancing our knowledge of IO-related metabolic changes may lead to improved prevention and management strategies, improved reproductive performance, and reduced economic losses within the dairy sector.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eIO\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003einactive ovaries\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eFF\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003efollicular fluid\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eHC\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ehealthy control\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eLF\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003elargest follicle\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eDIM\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003edays in milk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eBCS\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ebody condition score\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eNEB\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003enegative energy balance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eLC-MS/MS\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eliquid chromatography-tandem mass spectrometry\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eTMR\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal mixed ration\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePGF\u003csub\u003e2\u0026alpha;\u003c/sub\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eprostaglandin F2 alpha\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eGnRH\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003egonadotropin-releasing hormone\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eNEFA\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003enon-esterified fatty acids\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eAST\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003easpartate aminotransferase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eALT\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ealanine aminotransferase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eTP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003etotal protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eIGF-1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003einsulin-like growth factor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePCA\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eprincipal component analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eOPLS-DA\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eorthogonal partial least squares discriminant analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eVIP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003evariable importance for the projection\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eFC\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003efold change\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eFSH\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003efollicle-stimulating hormone\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eLH\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eluteinizing hormone\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eTCA\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003etricarboxylic acid cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eNO\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003enitric oxide\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eBCAAs\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ebranched-chain amino acids\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e13-HpODE\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13-Hydroperoxyoctadecadienoic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eATP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eadenosine triphosphate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Elsevier Language Editing Services for their help in English language revision of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY-XS: Conceptualization, Data curation, Methodology, Validation, Writing \u0026ndash; original draft. X-JJ: Formal analysis, Writing \u0026ndash; original draft. YH: Software, Visualization. RS: Methodology, Validation. Y-LB: Project administration, Supervision. CX: Conceptualization, Resources, Writing \u0026ndash; review \u0026amp; editing. CX: Funding acquisition, Resources, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Key R\u0026amp;D Program of China (grant number 2023YFD1801100), the Heilongjiang Province\u0026apos;s \u0026apos;Unveiling the List and Appointing the Leader\u0026apos; Key Science and Technology Project (grant number 2023ZXJ02B03), the China Postdoctoral Science Foundation (grant number 2024MD753940), and the Heilongjiang Postdoctoral Financial Assistance (grant number LBH-Z23243).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data generated during this study are available from the corresponding authors upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll experimental animals and procedures were approved by the farm manager and conducted according to the ethical guidelines and regulations approved by the Institutional Animal Care and Use Committee of Heilongjiang Bayi Agricultural University in Daqing, China (protocol code DWKJXY2024007; approval date: 4 January 2024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMarkusfeld O. 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University of Glasgow; 1981.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-veterinary-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Veterinary Research](http://bmcvetres.biomedcentral.com/)","snPcode":"12917","submissionUrl":"https://submission.nature.com/new-submission/12917/3?","title":"BMC Veterinary Research","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Dairy cow, Inactive ovary, Metabolomics, LC–MS/MS, Serum, Follicular fluid, Ovary","lastPublishedDoi":"10.21203/rs.3.rs-5526997/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5526997/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eInactive ovaries (IO) commonly cause postpartum anestrus in high-yielding dairy cows. Despite the widespread reporting of single-metabolic characteristics of postpartum IO, a comprehensive metabolic framework is lacking. Liquid chromatography\u0026ndash;tandem mass spectrometry was used to compare the metabolic changes in the serum, follicular fluid (FF), and ovarian tissue of six multiparous \u003cem\u003eHolstein\u003c/em\u003e cows from each of the IO and healthy control (HC) groups.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCows with IO had a higher milk yield during the previous lactation, poorer BCS, smaller largest follicle (LF) diameter at 63 days in milk, and slower LF growth rate than HC cows (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). They also exhibited higher serum levels of non-esterified fatty acids and aspartate aminotransferase and lower levels of estradiol, progesterone, insulin-like growth factor 1, calcium, and phosphorus (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Under IO conditions, 40, 51, and 14 differential metabolites were identified in serum, FF, and ovarian tissues, respectively. 12-Methyltridecanoic acid was consistently upregulated in cows with IO compared to HC cows across all samples (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Metabolomic pathway analysis identified significant alterations in six, three, and two metabolic pathways related to IO in the serum, FF, and ovarian tissues, respectively, affecting amino acid, energy, carbohydrate, lipid, and nucleotide metabolism. Valine, leucine, and isoleucine biosynthesis showed significant changes (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in all samples.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIn summary, these metabolic changes in cows with IO reflect a complex response to metabolic, oxidative, and inflammatory stresses. Our study provides the most comprehensive metabolic framework for cows with postpartum IO.\u003c/p\u003e","manuscriptTitle":"Comprehensive serum, follicular fluid, and ovarian tissue profiling reveals systemic metabolic alterations in high-yielding dairy cows with postpartum inactive ovaries","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-01 16:31:50","doi":"10.21203/rs.3.rs-5526997/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-13T08:53:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-02T12:41:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"11294707867449478184430193430867115542","date":"2025-05-18T16:40:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-13T16:34:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"271360262581763069788874748091331678961","date":"2025-05-12T16:16:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"234079608298981196578375634449953905107","date":"2025-04-01T07:52:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-31T00:11:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-26T07:11:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Veterinary Research","date":"2025-03-25T10:08:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-veterinary-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Veterinary Research](http://bmcvetres.biomedcentral.com/)","snPcode":"12917","submissionUrl":"https://submission.nature.com/new-submission/12917/3?","title":"BMC Veterinary Research","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"07efd502-702e-4236-8fe9-dda7782682ad","owner":[],"postedDate":"April 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T16:10:19+00:00","versionOfRecord":{"articleIdentity":"rs-5526997","link":"https://doi.org/10.1186/s12917-025-05153-5","journal":{"identity":"bmc-veterinary-research","isVorOnly":false,"title":"BMC Veterinary Research"},"publishedOn":"2026-01-05 15:59:25","publishedOnDateReadable":"January 5th, 2026"},"versionCreatedAt":"2025-04-01 16:31:50","video":"","vorDoi":"10.1186/s12917-025-05153-5","vorDoiUrl":"https://doi.org/10.1186/s12917-025-05153-5","workflowStages":[]},"version":"v1","identity":"rs-5526997","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5526997","identity":"rs-5526997","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}