[Effect of Kansui Radix stir-fried with vinegar on fecal metabolomics in Walker-256 rats with ascites].

Utilizing metabolomics technology, this study explored the change of fecal endogenous metabolites in Walker-256 rats with malignant ascites after the administration with Kansui Radix(KR) stir-fried with vinegar(VKR), sought the potential biomarkers in feces which were related to the treatment of malignant ascites by VKR and revealed the biological mechanism of water-expelling effect of VKR. Ultra-fast liquid chromatography-quadrupole-time-of-flight mass spectrometry(UFLC-Q-TOF-MS) was employed to detect the feces of rats in all groups. Principle component analysis(PCA) and partial least squares discriminant analysis(PLS-DA) were conducted to achieve pattern recognition. Combining t-test and variable importance in the projection(VIP) enabled the screening of potential biomarkers for the malignant ascites. Metabolic pathway analysis was accomplished with MetaboAnalyst. Correlation analysis was finally conducted integrating the sequencing data of gut microbiota to elucidate the mechanism underlying the water-expelling effect of VKR. The results showed that both KR and VKR could restore the abnormal metabolism of model rats to some extent, with VKR being inferior to KR in the regulation. Eleven potential biomarkers were identified to be correlated with the malignant ascites and five metabolic pathways were then enriched. Four kinds of gut microbiota were significantly related to the potential biomarkers. The water-expelling effect of VKR may be associated with the regulation of phenylalanine metabolism, biosynthesis of phenylalanine, tyrosine and tryptophan, tryptophan metabolism, glycerophospholipid metabolism, and glycosylphosphatidylinositol(GPI)-anchor biosynthesis. This study can provide a scientific basis for comprehensive understandings of the interaction between gut microbiota and host which has relation to the water-expelling effect of VKR and guide the reasonable clinical application of VKR.

Modeling coordinated enzymatic control of saccharification and fermentation by Clostridium thermocellum during consolidated bioprocessing of cellulose.

Consolidated bioprocessing (CBP) of cellulose is a cost-effective route to produce valuable biochemicals by integrating saccharification, fermentation and cellulase synthesis in a single step. However, the lack of understanding of governing factors of interdependent saccharification and fermentation in CBP eludes reliable process optimization. Here, we propose a new framework that synergistically couples population balances (to simulate cellulose depolymerization) and cybernetic models (to model enzymatic regulation of fermentation) to enable improved understanding of CBP. The resulting framework, named the unified cybernetic-population balance model (UC-PBM), enables simulation of CBP driven by coordinated control of enzyme synthesis through closed-loop interactions. UC-PBM considers two key aspects in controlling CBP: (1) heterogeneity in cellulose properties and (2) cellular regulation of competing cell growth and cellulase secretion. In a case study on Clostridium thermocellum, UC-PBM not only provides a decent fit with various exometabolomic data, but also reveals that: (i) growth-decoupled cellulase-secreting pathways are only activated during famine conditions to promote the production of growth substrates, and (ii) starting cellulose concentration has a strong influence on the overall flux distribution. Equipped with mechanisms of cellulose degradation and fermentative regulations, UC-PBM is practical to explore phenotypic functions for primary evaluation of microorganisms' potential for metabolic engineering and optimal design of bioprocess.

Systems biology: current status and challenges.

We put together a special issue on current approaches in systems biology with a focus on mathematical modeling of metabolic networks. Mathematical models have increasingly been used to unravel molecular mechanisms of complex dynamic biological processes. We here provide a short introduction into the topics covered in this special issue, highlighting current developments and challenges.

The transcriptome enables the identification of candidate genes behind medicinal value of Drumstick tree (Moringa oleifera).

Moringa oleifera is a plant well-known for its nutrition value, drought resistance and medicinal properties. cDNA libraries from five different tissues (leaf, root, stem, seed and flower) of M. oleifera cultivar Bhagya were generated and sequenced. We developed a bioinformatics pipeline to assemble transcriptome, along with the previously published M. oleifera genome, to predict 17,148 gene models. Few candidate genes related to biosynthesis of secondary metabolites, vitamins and ion transporters were identified. Expressions were further confirmed by real-time quantitative PCR experiments for few promising leads. Quantitative estimation of metabolites, as well as elemental analysis, was also carried out to support our observations. Enzymes in the biosynthesis of vitamins and metabolites like quercetin and kaempferol are highly expressed in leaves, flowers and seeds. The expression of iron transporters and calcium storage proteins were observed in root and leaves. In general, leaves retain the highest amount of small molecules of interest.

Clinical significance of circulating tumor cells and metabolic signatures in lung cancer after surgical removal.

BACKGROUND: Lung cancer (LC) remains the deadliest form of cancer globally. While surgery remains the optimal treatment strategy for individuals with early-stage LC, what the metabolic consequences are of such surgical intervention remains uncertain. METHODS: Negative enrichment-fluorescence in situ hybridization (NE-FISH) was used in an effort to detect circulating tumor cells (CTCs) in pre- and post-surgery peripheral blood samples from 51 LC patients. In addition, targeted metabolomics analyses, multivariate statistical analyses, and pathway analyses were used to explore surgery-associated metabolic changes. RESULTS: LC patients had significantly higher CTC counts relative to healthy controls with 66.67% of LC patients having at least 1 detected CTC before surgery. CTC counts were associated with clinical outcomes following surgery. In a targeted metabolomics analysis, we detected 34 amino acids, 147 lipids, and 24 fatty acids. When comparing LC patients before and after surgery to control patients, metabolic shifts were detected via PLS-DA and pathway analysis. Further surgery-associated metabolic changes were identified when comparing LA (LC patients after surgery) and LB (LC patients before surgery) groups. We identified SM 42:4, Ser, Sar, Gln, and LPC 18:0 for inclusion in a biomarker panel for early-stage LC detection based upon an AUC of 0.965 (95% CI 0.900-1.000). This analysis revealed that SM 42:2, SM 35:1, PC (16:0/14:0), PC (14:0/16:1), Cer (d18:1/24:1), and SM 38:3 may offer diagnostic and prognostic benefits in LC. CONCLUSIONS: These findings suggest that CTC detection and plasma metabolite profiling may be an effective means of diagnosing early-stage LC and identifying patients at risk for disease recurrence.

Metabolic characterisation of THP-1 macrophage polarisation using LC-MS-based metabolite profiling.

INTRODUCTION: Macrophages constitute a heterogeneous population of functionally distinct cells involved in several physiological and pathological processes. They display remarkable plasticity by changing their phenotype and function in response to environmental cues representing a spectrum of different functional phenotypes. The so-called M1 and M2 macrophages are often considered as representative of pro- and anti-inflammatory ends of such spectrum. Metabolomics approach is a powerful tool providing important chemical information about the cellular phenotype of living systems, and the changes in their metabolic pathways in response to various perturbations. OBJECTIVES: This study aimed to characterise M1 and M2 phenotypes in THP-1 macrophages in order to identify characteristic metabolites of each polarisation state. METHODS: Herein, untargeted liquid chromatography (LC)-mass spectrometry (MS)-based metabolite profiling was applied to characterise the metabolic profile of M1-like and M2-like THP-1 macrophages. RESULTS: The results showed that M1 and M2 macrophages have distinct metabolic profiles. Sphingolipid and pyrimidine metabolism was significantly changed in M1 macrophages whereas arginine, proline, alanine, aspartate and glutamate metabolism was significantly altered in M2 macrophages. CONCLUSION: This study represents successful application of LC-MS metabolomics approach to characterise M1 and M2 macrophages providing functional readouts that show unique metabolic signature for each phenotype. These data could contribute to a better understanding of M1 and M2 functional properties and could pave the way for developing new therapeutics targeting different immune diseases.

[Effect of chemical ingredients B and C in Kansui stir-baked with vinegar on fecal metabolomics in rats].

To reveal the toxic mechanism of Kansui stir-baked with vinegar(VEK), conducta comparative study on the metabolites of fecal samples of rats before and after being treated with chemical constituents group B and C(VEKB/VEKC) extracted from VEK by metabolomics approach. The fecal samples of each group were analyzed using ultra performance liquid chromatography-quadrupole-time of flight-mass spectrometry(UFLC-Q-TOF-MS). Then the data was processed by principal component analysis(PCA) and partial least square discriminant analysis(OPLS-DA) to screen and identify biomarkers relating to the toxicity of VEK. Besides, t-test was adopted for univariate statistical analysis, so as to study the changes of these biomarkers in drug groups before and after being treated with VEKB/VEKC and explore the effect of VEKB/VEKC on the metabolism of rat feces. Furthermore, the toxic mechanism of VEKB/VEKC was explored based on the results of the metabolic pathway analysis. The results displayed that compared with control group, the metabolism of fecal samples of VEKB and VEKC treated groups show obvious changes, and the VEKB treated group show more significant changes. A total of 16 potential biomarkers and 5 metabolic pathways relating to the toxicity of VEK were found and identified. And the toxicity of VEK might be associated with the disorder of such metabolic pathways as tryptophan metabolism, primary bile acid biosynthesis, amino sugar and nucleotide sugar metabolism, purine metabolism, and degradation of valine, leucine and isoleucine. This study provides a scientific basis for the clinical safety application of VEK.

Comparison of pathway analysis and constraint-based methods for cell factory design.

BACKGROUND: Computational strain optimisation methods (CSOMs) have been successfully used to exploit genome-scale metabolic models, yielding strategies useful for allowing compound overproduction in metabolic cell factories. Minimal cut sets are particularly interesting since their definition allows searching for intervention strategies that impose strong growth-coupling phenotypes, and are not subject to optimality bias when compared with simulation-based CSOMs. However, since both types of methods have different underlying principles, they also imply different ways to formulate metabolic engineering problems, posing an obstacle when comparing their outputs. RESULTS: In this work, we perform an in-depth analysis of potential strategies that can be obtained with both methods, providing a critical comparison of performance, robustness, predicted phenotypes as well as strategy structure and size. To this end, we devised a pipeline including enumeration of strategies from evolutionary algorithms (EA) and minimal cut sets (MCS), filtering and flux analysis of predicted mutants to optimize the production of succinic acid in Saccharomyces cerevisiae. We additionally attempt to generalize problem formulations for MCS enumeration within the context of growth-coupled product synthesis. Strategies from evolutionary algorithms show the best compromise between acceptable growth rates and compound overproduction. However, constrained MCSs lead to a larger variety of phenotypes with several degrees of growth-coupling with production flux. The latter have proven useful in revealing the importance, in silico, of the gamma-aminobutyric acid shunt and manipulation of cofactor pools in growth-coupled designs for succinate production, mechanisms which have also been touted as potentially useful for metabolic engineering. CONCLUSIONS: The two main groups of CSOMs are valuable for finding growth-coupled mutants. Despite the limitations in maximum growth rates and large strategy sizes, MCSs help uncover novel mechanisms for compound overproduction and thus, analyzing outputs from both methods provides a richer overview on strategies that can be potentially carried over in vivo.

Finding MEMo: minimum sets of elementary flux modes.

Metabolic network reconstructions are widely used in computational systems biology for in silico studies of cellular metabolism. A common approach to analyse these models are elementary flux modes (EFMs), which correspond to minimal functional units in the network. Already for medium-sized networks, it is often impossible to compute the set of all EFMs, due to their huge number. From a practical point of view, this might also not be necessary because a subset of EFMs may already be sufficient to answer relevant biological questions. In this article, we study MEMos or minimum sets of EFMs that can generate all possible steady-state behaviours of a metabolic network. The number of EFMs in a MEMo may be by several orders of magnitude smaller than the total number of EFMs. Using MEMos, we can compute generating sets of EFMs in metabolic networks where the whole set of EFMs is too large to be enumerated.

Inhibition of C298S mutant of human aldose reductase for antidiabetic applications: Evidence from in silico elementary mode analysis of biological network model.

Human aldose reductase (hAR) is the key enzyme in sorbitol pathway of glucose utilization and is implicated in the etiology of secondary complications of diabetes, such as, cardiovascular complications, neuropathy, nephropathy, retinopathy, and cataract genesis. It reduces glucose to sorbitol in the presence of NADPH and the major cause of diabetes complications could be the change in the osmotic pressure due to the accumulation of sorbitol. An activated form of hAR (activated hAR or ahAR) poses a potential obstacle in the development of diabetes drugs as hAR-inhibitors are ineffective against ahAR. The therapeutic efficacy of such drugs is compromised when a large fraction of the enzyme (hAR) undergoes conversion to the activated ahAR form as has been observed in the diabetic tissues. In the present study, attempts have been made to employ systems biology strategies to identify the elementary nodes of human polyol metabolic pathway, responsible for normal metabolic states, followed by the identification of natural potent inhibitors of the activated form of hAR represented by the mutant C298S for possible antidiabetic applications. Quantum Mechanical Molecular Mechanical docking strategy was used to determine the probable inhibitors of ahAR. Rosmarinic acid was found as the most potent natural ahAR inhibitor and warrants for experimental validation in the near future.

Thermodynamic constraints for identifying elementary flux modes.

Metabolic pathway analysis is a key method to study metabolism and the elementary flux modes (EFMs) is one major concept allowing one to analyze the network in terms of minimal pathways. Their practical use has been hampered by the combinatorial explosion of their number in large systems. The EFMs give the possible pathways at steady state, but the real pathways are limited by biological constraints. In this review, we display three different methods that integrate thermodynamic constraints in terms of Gibbs free energy in the EFMs computation.

A mathematical framework for yield (vs. rate) optimization in constraint-based modeling and applications in metabolic engineering.

BACKGROUND: The optimization of metabolic rates (as linear objective functions) represents the methodical core of flux-balance analysis techniques which have become a standard tool for the study of genome-scale metabolic models. Besides (growth and synthesis) rates, metabolic yields are key parameters for the characterization of biochemical transformation processes, especially in the context of biotechnological applications. However, yields are ratios of rates, and hence the optimization of yields (as nonlinear objective functions) under arbitrary linear constraints is not possible with current flux-balance analysis techniques. Despite the fundamental importance of yields in constraint-based modeling, a comprehensive mathematical framework for yield optimization is still missing. RESULTS: We present a mathematical theory that allows one to systematically compute and analyze yield-optimal solutions of metabolic models under arbitrary linear constraints. In particular, we formulate yield optimization as a linear-fractional program. For practical computations, we transform the linear-fractional yield optimization problem to a (higher-dimensional) linear problem. Its solutions determine the solutions of the original problem and can be used to predict yield-optimal flux distributions in genome-scale metabolic models. For the theoretical analysis, we consider the linear-fractional problem directly. Most importantly, we show that the yield-optimal solution set (like the rate-optimal solution set) is determined by (yield-optimal) elementary flux vectors of the underlying metabolic model. However, yield- and rate-optimal solutions may differ from each other, and hence optimal (biomass or product) yields are not necessarily obtained at solutions with optimal (growth or synthesis) rates. Moreover, we discuss phase planes/production envelopes and yield spaces, in particular, we prove that yield spaces are convex and provide algorithms for their computation. We illustrate our findings by a small example and demonstrate their relevance for metabolic engineering with realistic models of E. coli. CONCLUSIONS: We develop a comprehensive mathematical framework for yield optimization in metabolic models. Our theory is particularly useful for the study and rational modification of cell factories designed under given yield and/or rate requirements.

Optimal knockout strategies in genome-scale metabolic networks using particle swarm optimization.

BACKGROUND: Knockout strategies, particularly the concept of constrained minimal cut sets (cMCSs), are an important part of the arsenal of tools used in manipulating metabolic networks. Given a specific design, cMCSs can be calculated even in genome-scale networks. We would however like to find not only the optimal intervention strategy for a given design but the best possible design too. Our solution (PSOMCS) is to use particle swarm optimization (PSO) along with the direct calculation of cMCSs from the stoichiometric matrix to obtain optimal designs satisfying multiple objectives. RESULTS: To illustrate the working of PSOMCS, we apply it to a toy network. Next we show its superiority by comparing its performance against other comparable methods on a medium sized E. coli core metabolic network. PSOMCS not only finds solutions comparable to previously published results but also it is orders of magnitude faster. Finally, we use PSOMCS to predict knockouts satisfying multiple objectives in a genome-scale metabolic model of E. coli and compare it with OptKnock and RobustKnock. CONCLUSIONS: PSOMCS finds competitive knockout strategies and designs compared to other current methods and is in some cases significantly faster. It can be used in identifying knockouts which will force optimal desired behaviors in large and genome scale metabolic networks. It will be even more useful as larger metabolic models of industrially relevant organisms become available.

Advances in network-based metabolic pathway analysis and gene expression data integration.

With the emergence of metabolic networks, novel mathematical pathway concepts were introduced in the past decade, aiming to go beyond canonical maps. However, the use of network-based pathways to interpret 'omics' data has been limited owing to the fact that their computation has, until very recently, been infeasible in large (genome-scale) metabolic networks. In this review article, we describe the progress made in the past few years in the field of network-based metabolic pathway analysis. In particular, we review in detail novel optimization techniques to compute elementary flux modes, an important pathway concept in this field. In addition, we summarize approaches for the integration of metabolic pathways with gene expression data, discussing recent advances using network-based pathway concepts.

1H NMR-Linked Metabolomics Analysis of Liver from a Mouse Model of NP-C1 Disease.

Clinical manifestations of Niemann-Pick type C1 (NP-C1) disease include neonatal hepatosplenomegaly and in some patients progressive liver dysfunction and failure. This study involved a 1H NMR-linked metabolomics analysis of liver samples collected from a NP-C1 disease mutant mouse model in order to explore time-dependent imbalances in metabolic pathways associated with NP-C1 liver dysfunction, including fibrosis. NP-C1 mutant (Npc1-/-; NP-C1), control (Npc1+/+; WT), and NP-C1 heterozygous mice (Npc1+/-; HET) were generated from heterozygote matings. Aqueous extracts of these liver samples collected at time points of 3, 6, 9, and 11 weeks were subjected to high-resolution NMR analysis, and multivariate (MV) metabolomics analyses of data sets acquired were performed. A MV random forests (RFs) model effectively discriminated between NP-C1 and a combined WT/HET hepatic NMR profiles with very high predictive accuracy and reliability. Key distinguishing features included significant upregulations in the hepatic concentrations of phenylalanine, tyrosine, glutamate, lysine/ornithine, valine, threonine, and hypotaurine/methionine, and diminished levels of nicotinate/niacinamide, inosine, phosphoenolpyruvate, and 3-hydroxyphenylacetate. Quantitative pathway topological analysis confirmed that imbalances in tyrosine biosynthesis, and hepatic phenylalanine, tyrosine, glutamate/glutamine, and nicotinate/niacinamide metabolism were involved in the pathogenesis of NP-C1 disease-associated liver dysfunction/damage. 1H NMR-linked metabolomics analysis provides valuable biomarker information regarding hepatic dysfunction or damage in NP-C1 disease.

Metabolic pathways of lung inflammation revealed by high-resolution metabolomics (HRM) of H1N1 influenza virus infection in mice.

Influenza is a significant health concern worldwide. Viral infection induces local and systemic activation of the immune system causing attendant changes in metabolism. High-resolution metabolomics (HRM) uses advanced mass spectrometry and computational methods to measure thousands of metabolites inclusive of most metabolic pathways. We used HRM to identify metabolic pathways and clusters of association related to inflammatory cytokines in lungs of mice with H1N1 influenza virus infection. Infected mice showed progressive weight loss, decreased lung function, and severe lung inflammation with elevated cytokines [interleukin (IL)-1ß, IL-6, IL-10, tumor necrosis factor (TNF)-α, and interferon (IFN)-γ] and increased oxidative stress via cysteine oxidation. HRM showed prominent effects of influenza virus infection on tryptophan and other amino acids, and widespread effects on pathways including purines, pyrimidines, fatty acids, and glycerophospholipids. A metabolome-wide association study (MWAS) of the aforementioned inflammatory cytokines was used to determine the relationship of metabolic responses to inflammation during infection. This cytokine-MWAS (cMWAS) showed that metabolic associations consisted of distinct and shared clusters of 396 metabolites highly correlated with inflammatory cytokines. Strong negative associations of selected glycosphingolipid, linoleate, and tryptophan metabolites with IFN-γ contrasted strong positive associations of glycosphingolipid and bile acid metabolites with IL-1ß, TNF-α, and IL-10. Anti-inflammatory cytokine IL-10 had strong positive associations with vitamin D, purine, and vitamin E metabolism. The detailed metabolic interactions with cytokines indicate that targeted metabolic interventions may be useful during life-threatening crises related to severe acute infection and inflammation.

Metabolic profiling of insect cell lines: Unveiling cell line determinants behind system's productivity.

Baculovirus infection boosts the host biosynthetic activity towards the production of viral components and the recombinant protein of interest, hyper-productive phenotypes being the result of a successful adaptation of the cellular network to that scenario. Spodoptera frugiperda derived Sf9 and Trichoplusia ni derived High Five cell lines have a major track record for the production of recombinant proteins, with High Five cells presenting higher productivities. A metabolic profiling of the two insect cell lines was pursued to underpin specific cellular traits behind productive phenotypes. Multivariate analysis identified cell-line dependent metabolic signatures linked to productivity. Pathway analysis highlighted cellular pathways of paramount importance in supporting infection and protein production. Moreover, better producer phenotypes proved to be correlated with the capacity of cells to shift their metabolism in favor of energy-generating pathways to fuel biosynthesis, a scenario observed in the High Five cell line. Metabolomic profiling allowed us to identify metabolic pathways involved in infection and recombinant protein production, which can be selected as targets for further improvement of the system.

Causal relationships between blood metabolites and diabetic retinopathy: a two-sample Mendelian randomization study.

Background: Diabetic retinopathy (DR) is a microvascular complication of diabetes, severely affecting patients' vision and even leading to blindness. The development of DR is influenced by metabolic disturbance and genetic factors, including gene polymorphisms. The research aimed to uncover the causal relationships between blood metabolites and DR. Methods: The two-sample mendelian randomization (MR) analysis was employed to estimate the causality of blood metabolites on DR. The genetic variables for exposure were obtained from the genome-wide association study (GWAS) dataset of 486 blood metabolites, while the genetic predictors for outcomes including all-stage DR (All DR), non-proliferative DR (NPDR) and proliferative DR (PDR) were derived from the FinnGen database. The primary analysis employed inverse variance weighted (IVW) method, and supplementary analyses were performed using MR-Egger, weighted median (WM), simple mode and weighted mode methods. Additionally, MR-Egger intercept test, Cochran's Q test, and leave-one-out analysis were also conducted to guarantee the accuracy and robustness of the results. Subsequently, we replicated the MR analysis using three additional datasets from the FinnGen database and conducted a meta-analysis to determine blood metabolites associated with DR. Finally, reverse MR analysis and metabolic pathway analysis were performed. Results: The study identified 13 blood metabolites associated with All DR, 9 blood metabolites associated with NPDR and 12 blood metabolites associated with PDR. In summary, a total of 21 blood metabolites were identified as having potential causal relationships with DR. Additionally, we identified 4 metabolic pathways that are related to DR. Conclusion: The research revealed a number of blood metabolites and metabolic pathways that are causally associated with DR, which holds significant importance for screening and prevention of DR. However, it is noteworthy that these causal relationships should be validated in larger cohorts and experiments.

UHPLC/MS-based metabolomics of asthmatic mice reveals metabolic changes in group 2 innate lymphoid cells.

Helper Th2-type immune responses are essential in allergic airway diseases, including asthma and allergic rhinitis. Recent studies have indicated that group 2 innate lymphoid cells (ILC2s) play a crucial role in the occurrence and development of asthma. However, the metabolic profile of ILC2s and their regulatory mechanisms in asthma remain unclear. Therefore, we established two asthma mouse models: an ovalbumin (OVA)-induced asthma model and an IL-33-induced asthma model. We then used ultra-high-performance liquid chromatography/mass spectrometry (UHPLC/MS) to conduct high-throughput untargeted metabolic analysis of ILC2s in the lung tissues of the asthma models. The identified metabolites primarily consisted of lipids, lipid-like molecules, benzene, organic acids, derivatives, and organic oxidation compounds. Specifically, 34 differentially accumulated metabolites influenced the metabolic profiles of the control and OVA-induced asthma model groups. Moreover, the accumulation of 39 metabolites significantly differed between the Interleukin 33 (IL-33) and control groups. These differentially accumulated metabolites were mainly involved in pathways such as sphingolipid, oxidative phosphorylation, and fatty acid metabolism. This metabolomic study revealed, for the first time, the key metabolites and metabolic pathways of ILC2s, revealing new aspects of cellular metabolism in the context of airway inflammation. These findings not only contribute to unraveling the pathogenesis of asthma but also provide a crucial theoretical foundation for the future development of therapeutic strategies targeting ILC2s.

Genetic insights of blood lipid metabolites on polycystic ovary syndrome risk: a bidirectional two-sample Mendelian randomization study.

Background: Pathologically, metabolic disorder plays a crucial role in polycystic ovarian syndrome (PCOS). However, there is no conclusive evidence lipid metabolite levels to PCOS risk. Methods: In this study, genome-wide association study (GWAS) genetic data for 122 lipid metabolites were used to assign instrumental variables (IVs). PCOS GWAS were derived from a large-scale meta-analysis of 10,074 PCOS cases and 103,164 controls. An inverse variance weighted (IVW) analysis was the primary methodology used for Mendelian randomization (MR). For sensitivity analyses, Cochran Q test, MR-Egger intercept, MR-PRESSO, leave-one-out analysis,and Steiger test were performed. Furthermore, we conducted replication analysis, meta-analysis, and metabolic pathway analysis. Lastly, reverse MR analysis was used to determine whether the onset of PCOS affected lipid metabolites. Results: This study detected the blood lipid metabolites and potential metabolic pathways that have a genetic association with PCOS onset. After IVW, sensitivity analyses, replication and meta-analysis, two pathogenic lipid metabolites of PCOS were finally identified: Hexadecanedioate (OR=1.85,95%CI=1.27-2.70, P=0.001) and Dihomo-linolenate (OR=2.45,95%CI=1.30-4.59, P=0.005). Besides, It was found that PCOS may be mediated by unsaturated fatty acid biosynthesis and primary bile acid biosynthesis metabolic pathways. Reverse MR analysis showed the causal association between PCOS and 2-tetradecenoyl carnitine at the genetic level (OR=1.025, 95% CI=1.003-1.048, P=0.026). Conclusion: Genetic evidence suggests a causal relationship between hexadecanedioate and dihomo-linolenate and the risk of PCOS. These compounds could potentially serve as metabolic biomarkers for screening PCOS and selecting drug targets. The identification of these metabolic pathways is valuable in guiding the exploration of the pathological mechanisms of PCOS, although further studies are necessary for confirmation.