MRMPROBS: A Data Assessment and Metabolite Identification Tool for Large-Scale Multiple Reaction Monitoring Based Widely Targeted Metabolomics

Tonic type 2 immunity is a critical tissue checkpoint controlling autoimmunity in the skin

Immunoregulatory mechanisms established in the lymphoid organs are vital for preventing autoimmunity. However, the presence of similar mechanisms in non-lymphoid tissues remains unclear. Through transcriptomic and lipidomic analyses, we find a negative association between psoriasis and fatty acid metabolism, as well as Th2 signature. Homeostatic expression of liver X receptor (LXR) and peroxisome proliferator-activated receptor gamma (PPARγ) is essential for maintaining fatty acid metabolism and for conferring resistance to psoriasis in mice. Perturbation of signal transducer and activator of transcription 6 (STAT6) diminishes the homeostatic levels of LXR and PPARγ. Furthermore, mice lacking STAT6, interleukin 4 receptor alpha (IL-4Rα), or IL-13, but not IL-4, exhibit increased susceptibility to psoriasis. Under steady state, innate lymphoid cells (ILCs) are the primary producers of IL-13. In human skin, inhibiting tonic type 2 immunity exacerbates psoriasis-like inflammation and IL-17A, while activating LXR or PPARγ inhibits them. Hence, we propose that tonic type 2 immunity, driven by IL-13-producing ILCs, represents a crucial tissue checkpoint that represses autoimmunity and maintains lipid homeostasis in the skin.

Recent Developments in Machine Learning for Mass Spectrometry

Statistical analysis and modeling of mass spectrometry (MS) data have a long and rich history with several modern MS-based applications using statistical and chemometric methods. Recently, machine learning (ML) has experienced a renaissance due to advents in computational hardware and the development of new algorithms for artificial neural networks (ANN) and deep learning architectures. Moreover, recent successes of new ANN and deep learning architectures in several areas of science, engineering, and society have further strengthened the ML field. Importantly, modern ML methods and architectures have enabled new approaches for tasks related to MS that are now widely adopted in several popular MS-based subdisciplines, such as mass spectrometry imaging and proteomics. Herein, we aim to provide an introductory summary of the practical aspects of ML methodology relevant to MS. Additionally, we seek to provide an up-to-date review of the most recent developments in ML integration with MS-based techniques while also providing critical insights into the future direction of the field.

Pseudo-targeted lipidomics insights into lipid discrepancies between yak colostrum and mature milk based on UHPLC-Qtrap-MS

Yak milk is essential to maintain the normal physiological functions of herders in Tibetan areas of China. However, the lipid components of yak colostrum (YC) and mature milk (YM) have not been systematically studied. We employed a quantitative lipidomics to comprehensively describe the alterations in the milk lipid profile of lactating yaks. Herein, totally 851 lipids from 28 lipid subclasses in YC and YM were identified and screened for 43 significantly different lipids (SDLs; variable importance in projection > 1, fold change < 0.5 or > 2 with P < 0.05), with cholesterol ester (CE, 16:0) and triacylglycerol (TAG, 54:6 (20:5), 50:1 (16:0), 56:6 (20:5)) were the potential lipid biomarkers. Fourteen SDLs were modulated downwards, and 29 SDLs were modulated upwards in YM. Moreover, by analyzing lipid metabolic pathways in these SDLs, glycerophospholipid metabolism was the most critical. Our results furnish integral lipid details for evaluating yak milk's nutritional quality.

Myricanol improves metabolic profiles in dexamethasone induced lipid and protein metabolism disorders in mice

Myricanol (MY) is one of the main active components from bark of Myrica Rubra. It is demonstrated that MY rescues dexamethasone (DEX)-induced muscle dysfunction via activating silent information regulator 1 (SIRT1) and increasing adenosine 5'-monophosphate-activated protein kinase (AMPK) phosphorylation. Since SIRT1 and AMPK are widely involved in the metabolism of nutrients, we speculated that MY may exert beneficial effects on DEX-induced metabolic disorders. This study for the first time applied widely targeted metabolomics to investigate the beneficial effects of MY on glucose, lipids, and protein metabolism in DEX-induced metabolic abnormality in mice. The results showed that MY significantly reversed DEX-induced soleus and gastrocnemius muscle weight loss, muscle fiber damage, and muscle strength loss. MY alleviated DEX-induced metabolic disorders by increasing SIRT1 and glucose transporter type 4 (GLUT4) expressions. Additionally, myricanol prevented muscle cell apoptosis and atrophy by inhibiting caspase 3 cleavages and muscle ring-finger protein-1 (MuRF1) expression. Metabolomics showed that MY treatment reversed the serum content of carnitine ph-C1, palmitoleic acid, PS (16:0_17:0), PC (14:0_20:5), PE (P-18:1_16:1), Cer (t18:2/38:1(2OH)), four amino acids and their metabolites, and 16 glycerolipids in DEX mice. Kyoto encyclopedia of genes and genomes (KEGG) and metabolic set enrichment analysis (MSEA) analysis revealed that MY mainly affected metabolic pathways, glycerolipid metabolism, lipolysis, fat digestion and absorption, lipid and atherosclerosis, and cholesterol metabolism pathways through regulation of metabolites involved in glutathione, butanoate, vitamin B6, glycine, serine and threonine, arachidonic acid, and riboflavin metabolism. Collectively, MY can be used as an attractive therapeutic agent for DEX-induced metabolic abnormalities.

SCALiR: A Web Application for Automating Absolute Quantification of Mass Spectrometry-Based Metabolomics Data

Quantitative liquid chromatography-mass spectrometry (LC-MS)-based metabolomics is becoming an important approach for studying complex biological systems but presents several technical challenges that limit its widespread use. Computing metabolite concentrations using standard curves generated from standard mixtures of known concentrations is a labor-intensive process that is often performed manually. Currently, there are few options for open-source software tools that can automatically calculate metabolite concentrations. Herein, we introduce SCALiR (standard curve application for determining linear ranges), a new web-based software tool specifically built for this task, which allows users to automatically transform LC-MS signals into absolute quantitative data (https://www.lewisresearchgroup.org/software). SCALiR uses an algorithm that automatically finds the equation of the line of best fit for each standard curve and uses this equation to calculate compound concentrations from the LC-MS signal. Using a standard mix containing 77 metabolites, we show a close correlation between the concentrations calculated by SCALiR and the expected concentrations of each compound (R2 = 0.99 for a y = x curve fitting). Moreover, we demonstrate that SCALiR reproducibly calculates concentrations of midrange standards across ten analytical batches (average coefficient of variation 0.091). SCALiR can be used to calculate metabolite concentrations either using external calibration curves or by using internal standards to correct for matrix effects. This open-source and vendor agnostic software offers users several advantages in that (1) it requires only 10 s of analysis time to compute concentrations of >75 compounds, (2) it facilitates automation of quantitative workflows, and (3) it performs deterministic evaluations of compound quantification limits. SCALiR therefore provides the metabolomics community with a simple and rapid tool that enables rigorous and reproducible quantitative metabolomics studies.

Hydrophilic Interaction Liquid Chromatography-Hydrogen/Deuterium Exchange-Mass Spectrometry (HILIC-HDX-MS) for Untargeted Metabolomics

Liquid chromatography with mass spectrometry (LC-MS)-based metabolomics detects thousands of molecular features (retention time-m/z pairs) in biological samples per analysis, yet the metabolite annotation rate remains low, with 90% of signals classified as unknowns. To enhance the metabolite annotation rates, researchers employ tandem mass spectral libraries and challenging in silico fragmentation software. Hydrogen/deuterium exchange mass spectrometry (HDX-MS) may offer an additional layer of structural information in untargeted metabolomics, especially for identifying specific unidentified metabolites that are revealed to be statistically significant. Here, we investigate the potential of hydrophilic interaction liquid chromatography (HILIC)-HDX-MS in untargeted metabolomics. Specifically, we evaluate the effectiveness of two approaches using hypothetical targets: the post-column addition of deuterium oxide (D2O) and the on-column HILIC-HDX-MS method. To illustrate the practical application of HILIC-HDX-MS, we apply this methodology using the in silico fragmentation software MS-FINDER to an unknown compound detected in various biological samples, including plasma, serum, tissues, and feces during HILIC-MS profiling, subsequently identified as N1-acetylspermidine.

MRMPro: a web-based tool to improve the speed of manual calibration for multiple reaction monitoring data analysis by mass spectrometry

BACKGROUND

As a gold-standard quantitative technique based on mass spectrometry, multiple reaction monitoring (MRM) has been widely used in proteomics and metabolomics. In the analysis of MRM data, as no peak picking algorithm can achieve perfect accuracy, manual inspection is necessary to correct the errors. In large cohort analysis scenarios, the time required for manual inspection is often considerable. Apart from the commercial software that comes with mass spectrometers, the open-source and free software Skyline is the most popular software for quantitative omics. However, this software is not optimized for manual inspection of hundreds of samples, the interactive experience also needs to be improved.

RESULTS

Here we introduce MRMPro, a web-based MRM data analysis platform for efficient manual inspection. MRMPro supports data analysis of MRM and schedule MRM data acquired by mass spectrometers of mainstream vendors. With the goal of improving the speed of manual inspection, we implemented a collaborative review system based on cloud architecture, allowing multiple users to review through browsers. To reduce bandwidth usage and improve data retrieval speed, we proposed a MRM data compression algorithm, which reduced data volume by more than 60% and 80% respectively compared to vendor and mzML format. To improve the efficiency of manual inspection, we proposed a retention time drift estimation algorithm based on similarity of chromatograms. The estimated retention time drifts were then used for peak alignment and automatic EIC grouping. Compared with Skyline, MRMPro has higher quantification accuracy and better manual inspection support.

CONCLUSIONS

In this study, we proposed MRMPro to improve the usability of manual calibration for MRM data analysis. MRMPro is free for non-commercial use. Researchers can access MRMPro through http://mrmpro.csibio.com/ . All major mass spectrometry formats (wiff, raw, mzML, etc.) can be analyzed on the platform. The final identification results can be exported to a common.xlsx format for subsequent analysis.

Can plant hormonomics be built on simple analysis? A review

The field of plant hormonomics focuses on the qualitative and quantitative analysis of the hormone complement in plant samples, akin to other omics sciences. Plant hormones, alongside primary and secondary metabolites, govern vital processes throughout a plant's lifecycle. While active hormones have received significant attention, studying all related compounds provides valuable insights into internal processes. Conventional single-class plant hormone analysis employs thorough sample purification, short analysis and triple quadrupole tandem mass spectrometry. Conversely, comprehensive hormonomics analysis necessitates minimal purification, robust and efficient separation and better-performing mass spectrometry instruments. This review summarizes the current status of plant hormone analysis methods, focusing on sample preparation, advances in chromatographic separation and mass spectrometric detection, including a discussion on internal standard selection and the potential of derivatization. Moreover, current approaches for assessing the spatiotemporal distribution are evaluated. The review touches on the legitimacy of the term plant hormonomics by exploring the current status of methods and outlining possible future trends.

Retinol Saturase Mediates Retinoid Metabolism to Impair a Ferroptosis Defense System in Cancer Cells

Ferroptosis is an iron-dependent form of regulated cell death induced by the lethal overload of lipid peroxides in cellular membranes. In recent years, modulating ferroptosis has gained attention as a potential therapeutic approach for tumor suppression. In the current study, retinol saturase (RETSAT) was identified as a significant ferroptosis mediator using a publicly accessible CRISPR/Cas9 screening dataset. RETSAT depletion protected tumor cells from lipid peroxidation and subsequent cell death triggered by various ferroptosis inducers. Furthermore, exogenous supplementation with retinoids, including retinol (the substrate of RETSAT) and its derivatives retinal and retinoic acid, also suppressed ferroptosis, whereas the product of RETSAT, 13, 14-dihydroretinol, failed to do so. As effective radical-trapping antioxidant, retinoids protected the lipid membrane from autoxidation and subsequent fragmentation, thus terminating the cascade of ferroptosis. Pseudotargeted lipidomic analysis identified an association between retinoid regulation of ferroptosis and lipid metabolism. Retinoic acid, but not 13, 14-dihydroretinoic acid, interacted with its nuclear receptor and activated transcription of stearoyl-CoA desaturase, which introduces the first double bond into saturated fatty acid and thus catalyzes the generation of monounsaturated fatty acid, a known ferroptosis suppressor. Therefore, RETSAT promotes ferroptosis by transforming retinol to 13, 14-dihydroretinol, thereby turning a strong anti-ferroptosis regulator into a relatively weak one.

SIGNIFICANCE

Retinoids have ferroptosis-protective properties and can be metabolized by RETSAT to promote ferroptosis, suggesting the possibility of targeting retinoid metabolism in cancer as a treatment strategy to trigger ferroptosis.

Combination of Structure Databases, In Silico Fragmentation, and MS/MS Libraries for Untargeted Screening of Non-Volatile Migrants from Recycled High-Density Polyethylene Milk Bottles

Chemical contamination is one of the major obstacles for mechanical recycling of plastics. In this article, we built and open-sourced an in-house MS/MS library containing more than 500 plastic-related chemicals and developed mspcompiler, an R package, for the compilation of various libraries. We then proposed a workflow to process untargeted screening data acquired by liquid chromatography high-resolution mass spectrometry. These tools were subsequently employed to data originating from recycled high-density polyethylene (rHDPE) obtained from milk bottles. A total of 83 compounds were identified, with 66 easily annotated by making use of our in-house MS/MS libraries and the mspcompiler R package. In silico fragmentation combined with data obtained from gas chromatography-mass spectrometry and lists of chemicals related to plastics were used to identify those remaining unknown. A pseudo-multiple reaction monitoring method was also applied to sensitively target and screen the identified chemicals in the samples. Quantification results demonstrated that a good sorting of postconsumer materials and a better recycling technology may be necessary for food contact applications. Removal or reduction of non-volatile substances, such as octocrylene and 2-ethylhexyl-4-methoxycinnamate, is still challenging but vital for the safe use of rHDPE as food contact materials.

Metabotyping of Andean pseudocereals and characterization of emerging mycotoxins

Pseudocereals are best known for three crops derived from the Andes: quinoa (Chenopodium quinoa), canihua (C. pallidicaule), and kiwicha (Amaranthus caudatus). Their grains are recognized for their nutritional benefits; however, there is a higher level of polyphenism. Meanwhile, the chemical food safety of pseudocereals remains poorly documented. Here, we applied untargeted and targeted metabolomics approaches by LC-MS to achieve both: i) a comprehensive chemical mapping of pseudocereal samples collected in the Andes; and ii) a quantification of their contents in emerging mycotoxins. An inventory of the fungal community was also realized to better know the fungi present in these grains. Metabotyping permitted to add new insights into the chemotaxonomy of pseudocereals, confirming the previously established phylotranscriptomic clades. Sixteen samples from Peru (out of 27) and one from France (out of one) were contaminated with Beauvericin, an emerging mycotoxin. Several mycotoxigenic fungi were detected, including Aspergillus sp., Penicillium sp., and Alternaria sp.

Quantitation of small molecules from liquid chromatography-mass spectrometric accurate mass datasets using CycloBranch

Gaussian and exponentially modified Gaussian functions were incorporated into integrating algorithms used by an open-source, cross-platform tool called CycloBranch. The quantitation is demonstrated on bacterial pyoverdines separated by fine isotope features. Using our algorithm, we can separate the m/z values 694.25802 and 694.26731 (a 0.009 Da difference), where the former belongs to the most intense peak of pyoverdine D (PvdD), and the latter to the second most intense peak of pyoverdine E (PvdE) in the respective isotopic clusters of [M + Fe-H]²⁺ ions. The areas under chromatographic curves of standards were analyzed for the limit of detection (LOD), limit of quantitation (LOQ), and regression coefficient calculations. The quantitative module returned a LOD and LOQ of 1.4 and 4.3 ng/mL, respectively, for both PvdD and PvdE in human urine. If present and detected in mass spectra, the intensities of user-defined [M + H]⁺, [M + Na]⁺, [M + K]⁺, [M + Fe-H]²⁺, or other ion types, can be accumulated and used for quantitation. The quantitation result is returned by CycloBranch in seconds or minutes, contrary to an hours-long manual approach, prone to user-born errors originating from necessary copying among various software environments. Native Bruker, Waters, Thermo, txt, mgf, mzML, and mzXML data formats are supported in CycloBranch, which is freely available at https://ms.biomed.cas.cz/cyclobranch.

CsMYC2 is involved in the regulation of phenylpropanoid biosynthesis induced by trypsin in cucumber (Cucumis sativus) during storage

Trypsin has a new activity of scavenging superoxide anion and generating hydrogen peroxide. Trypsin can significantly improve the storage quality of C. sativus. To illustrate the mechanism of trypsin-induced resistance in fruits and vegetables, an integrated analysis of widely targeted metabolomics and transcriptomics was carried out. Transcriptomic results showed that 1068 genes highly related to phenylpropanoid biosynthesis gathered in the brown module were obtained by WGCNA. In KEGG analysis, differentially expressed genes (DEGs) were also highly enriched in EIP (Environmental Information Processing) pathways "Plant hormone signal transduction (map04075)" and "MAPK signaling pathway-plant (map04016)". Next, 87 genes were identified as the leading edge by GSEA analysis. So far, CsMYC2 was highlighted as a key transcription factor that regulates phenylpropanoid biosynthesis identified by GSEA and WGCNA. Furthermore, the major route of biosynthesis of phenylpropanoid compounds including coumarins, lignins, chlorogenic acid, flavonoids, and derivatives regulated by trypsin was also illustrated by both transcriptomic and metabolomic data. Results of O2PLS showed that CsMYC2 was positively correlated with Rosmarinic acid-3-O-glucoside, Epigallocatechin, Quercetin-3-O-sophoroside (Baimaside), and so on. Correlation between CsMYC2, phenylpropanoid related genes, and metabolites in C. sativus was illustrated by co-expression networks. Roles of CsMYC2 were further checked in C. sativus by VIGS. The results of this study might give new insight into the exploration of the postharvest resistance mechanism of C. sativus induced by trypsin and provide useful information for the subsequent mining of resistance genes in C. sativus.

PI5P4Kα supports prostate cancer metabolism and exposes a survival vulnerability during androgen receptor inhibition

Phosphatidylinositol (PI)regulating enzymes are frequently altered in cancer and have become a focus for drug development. Here, we explore the phosphatidylinositol-5-phosphate 4-kinases (PI5P4K), a family of lipid kinases that regulate pools of intracellular PI, and demonstrate that the PI5P4Kα isoform influences androgen receptor (AR) signaling, which supports prostate cancer (PCa) cell survival. The regulation of PI becomes increasingly important in the setting of metabolic stress adaptation of PCa during androgen deprivation (AD), as we show that AD influences PI abundance and enhances intracellular pools of PI-4,5-P2. We suggest that this PI5P4Kα-AR relationship is mitigated through mTORC1 dysregulation and show that PI5P4Kα colocalizes to the lysosome, the intracellular site of mTORC1 complex activation. Notably, this relationship becomes prominent in mouse prostate tissue following surgical castration. Finally, multiple PCa cell models demonstrate marked survival vulnerability following stable PI5P4Kα inhibition. These results nominate PI5P4Kα as a target to disrupt PCa metabolic adaptation to castrate resistance.

Lipidomes of Icelandic bryophytes and screening of high contents of polyunsaturated fatty acids by using lipidomics approach

Bryophytes (mosses, liverworts, and hornworts) have interested researchers because of their high chemical diversity and their potential uses in pharmaceutical, food, and cosmetic industries. Specifically, long-chain polyunsaturated fatty acids (l-PUFA) such as arachidonic acid (AA) and eicosapentaenoic acid (EPA) are commonly found in bryophytes, but not in vascular plants. Bryophytes accumulate PUFAs in cold or even freezing temperature to keep the cell fluidity. Iceland has a long history of bryophyte vegetation. These bryophytes are highly adapted to the harsh environment in Iceland and therefore are expected to produce high amounts of PUFAs. However, despite the fact that hundreds of mosses and liverworts have been found in Iceland, their lipid profiles largely remain unknown. In this study, we performed untargeted lipidomics by using UPLC-ESI-QTOF-MS as a rapid screening strategy to examine the lipid compositions of 39 local bryophyte species in Iceland and aimed to find high AA and EPA producers. A total of 280 lipid molecular species from 15 lipid classes were quantified with isotope-labeled internal standards. AA and EPA were abundantly distributed in the phospholipids (mainly PC and PE) and glycerolipids (MGDG and DGDG) in six moss species, namely Racomotrium lanuginosum, R. ericoides, Bryum psedotriquetrium, Plagiomnium ellipticum, Hylocomium splendens, and Rhytidiadelphus triquetrus. Two of the six species (B. psedotriquetrium and H. splendens) also accumulated high concentrations of PUFA-containing-triacylglycerols.

Roles of type II H+-PPases and PPsPase1/PECP2 in early developmental stages and PPi homeostasis of Arabidopsis thaliana

The regulation of intracellular pyrophosphate (PPi) level is crucial for proper morphogenesis across all taxonomic kingdoms. PPi is released as a byproduct from ~200 metabolic reactions, then hydrolyzed by either membrane-bound (H⁺-PPase) or soluble pyrophosphatases (PPases). In Arabidopsis, the loss of the vacuolar H⁺-PPase/FUGU5, a key enzyme in PPi homeostasis, results in delayed growth and a number of developmental defects, pointing to the importance of PPi homeostasis in plant morphogenesis. The Arabidopsis genome encodes several PPases in addition to FUGU5, such as PPsPase1/PECP2, VHP2;1 and VHP2;2, although their significance regarding PPi homeostasis remains elusive. Here, to assess their contribution, phenotypic analyses of cotyledon aspect ratio, palisade tissue cellular phenotypes, adaxial side pavement cell complexity, stomatal distribution, and etiolated seedling length were performed, provided that they were altered due to excess PPi in a fugu5 mutant background. Overall, our analyses revealed that the above five traits were unaffected in ppspase1/pecp2, vhp2;1 and vhp2;2 loss-of-function mutants, as well as in fugu5 mutant lines constitutively overexpressing PPsPase1/PECP2. Furthermore, metabolomics revealed that ppspase1/pecp2, vhp2;1 and vhp2;2 etiolated seedlings exhibited metabolic profiles comparable to the wild type. Together, these results indicate that the contribution of PPsPase1/PECP2, VHP2;1 and VHP2;2 to PPi levels is negligible in comparison to FUGU5 in the early stages of seedling development.

Machine Learning and Hybrid Methods for Metabolic Pathway Modeling

Computational cell metabolism models seek to provide metabolic explanations of cell behavior under different conditions or following genetic alterations, help in the optimization of in vitro cell growth environments, or predict cellular behavior in vivo and in vitro. In the extremes, mechanistic models can include highly detailed descriptions of a small number of metabolic reactions or an approximate representation of an entire metabolic network. To date, all mechanistic models have required details of individual metabolic reactions, either kinetic parameters or metabolic flux, as well as information about extracellular and intracellular metabolite concentrations. Despite the extensive efforts and the increasing availability of high-quality data, required in vivo data are not available for the majority of known metabolic reactions; thus, mechanistic models are based primarily on ex vivo kinetic measurements and limited flux information. Machine learning approaches provide an alternative for derivation of functional dependencies from existing data. The increasing availability of metabolomic and lipidomic data, with growing feature coverage as well as sample set size, is expected to provide new data options needed for derivation of machine learning models of cell metabolic processes. Moreover, machine learning analysis of longitudinal data can lead to predictive models of cell behaviors over time. Conversely, machine learning models trained on steady-state data can provide descriptive models for the comparison of metabolic states in different environments or disease conditions. Additionally, inclusion of metabolic network knowledge in these analyses can further help in the development of models with limited data.This chapter will explore the application of machine learning to the modeling of cell metabolism. We first provide a theoretical explanation of several machine learning and hybrid mechanistic machine learning methods currently being explored to model metabolism. Next, we introduce several avenues for improving these models with machine learning. Finally, we provide protocols for specific examples of the utilization of machine learning in the development of predictive cell metabolism models using metabolomic data. We describe data preprocessing, approaches for training of machine learning models for both descriptive and predictive models, and the utilization of these models in synthetic and systems biology. Detailed protocols provide a list of software tools and libraries used for these applications, step-by-step modeling protocols, troubleshooting, as well as an overview of existing limitations to these approaches.

Metabolomics-Driven Identification of the Rate-Limiting Steps in 1-Propanol Production

The concerted effort for bioproduction of higher alcohols and other commodity chemicals has yielded a consortium of metabolic engineering techniques to identify targets to enhance performance of engineered microbial strains. Here, we demonstrate the use of metabolomics as a tool to systematically identify targets for improved production phenotypes in Escherichia coli. Gas chromatography/mass spectrometry (GC/MS) and ion-pair LC-MS/MS were performed to investigate metabolic perturbations in various 1-propanol producing strains. Two initial strains were compared that differ in the expression of the citramalate and threonine pathways, which hold a synergistic relationship to maximize production yields. While this results in increased productivity, no change in titer was observed when the threonine pathway was overexpressed beyond native levels. Metabolomics revealed accumulation of upstream byproducts, norvaline and 2-aminobutyrate, both of which are derived from 2-ketobutyrate (2KB). Eliminating the competing pathway by gene knockouts or improving flux through overexpression of glycolysis gene effectively increased the intracellular 2KB pool. However, the increase in 2KB intracellular concentration yielded decreased production titers, indicating toxicity caused by 2KB and an insufficient turnover rate of 2KB to 1-propanol. Optimization of alcohol dehydrogenase YqhD activity using an ribosome binding site (RBS) library improved 1-propanol titer (g/L) and yield (g/g of glucose) by 38 and 29% in 72 h compared to the base strain, respectively. This study demonstrates the use of metabolomics as a powerful tool to aid systematic strain improvement for metabolically engineered organisms.

Lipidomics in Understanding Pathophysiology and Pharmacologic Effects in Inflammatory Diseases: Considerations for Drug Development

The lipidome has a broad range of biological and signaling functions, including serving as a structural scaffold for membranes and initiating and resolving inflammation. To investigate the biological activity of phospholipids and their bioactive metabolites, precise analytical techniques are necessary to identify specific lipids and quantify their levels. Simultaneous quantification of a set of lipids can be achieved using high sensitivity mass spectrometry (MS) techniques, whose technological advancements have significantly improved over the last decade. This has unlocked the power of metabolomics/lipidomics allowing the dynamic characterization of metabolic systems. Lipidomics is a subset of metabolomics for multianalyte identification and quantification of endogenous lipids and their metabolites. Lipidomics-based technology has the potential to drive novel biomarker discovery and therapeutic development programs; however, appropriate standards have not been established for the field. Standardization would improve lipidomic analyses and accelerate the development of innovative therapies. This review aims to summarize considerations for lipidomic study designs including instrumentation, sample stabilization, data validation, and data analysis. In addition, this review highlights how lipidomics can be applied to biomarker discovery and drug mechanism dissection in various inflammatory diseases including cardiovascular disease, neurodegeneration, lung disease, and autoimmune disease.

Development of a widely targeted volatilomics method for profiling volatilomes in plants

Volatile organic compounds play essential roles in plant environment interactions as well as determining the fragrance of plants. Although gas chromatography-mass spectrometry-based untargeted metabolomics is commonly used to assess plant volatiles, it suffers from high spectral convolution, low detection sensitivity, a limited number of annotated metabolites, and relatively poor reproducibility. Here, we report a widely targeted volatilomics (WTV) method that involves using a "targeted spectra extraction" algorithm to address spectral convolution, constructing a high-coverage MS² spectral tag library to expand volatile annotation, adapting a multiple reaction monitoring mode to improve sensitivity, and using regression models to adjust for signal drift. The newly developed method was used to profile the volatilome of rice grains. Compared with the untargeted method, the newly developed WTV method shows higher sensitivity (for example, the signal-to-noise ratio of guaicol increased from 4.1 to 18.8), high annotation coverage (the number of annotated volatiles increased from 43 to 132), and better reproducibility (the number of volatiles in quality control samples with relative standard deviation value below 30.0% increased from 14 to 92 after normalization). Using the WTV method, we studied the metabolic responses of tomato to environmental stimuli and profiled the volatilomes of different rice accessions. The results identified benzothiazole as a potential airborne signal priming tomato plants for enhanced defense and 2-nonanone and 2-heptanone as novel aromatic compounds contributing to rice fragrance. These case studies suggest that the widely targeted volatilomics method is more efficient than those currently used and may considerably promote plant volatilomics studies.

Skotomorphogenesis exploits threonine to promote hypocotyl elongation

Mobilisation of seed storage reserves is important for seedling establishment in Arabidopsis. In this process, sucrose is synthesised from triacylglycerol via core metabolic processes. Mutants with defects in triacylglycerol-to-sucrose conversion display short etiolated seedlings. We found that whereas sucrose content in the indole-3-butyric acid response 10 (ibr10) mutant was significantly reduced, hypocotyl elongation in the dark was unaffected, questioning the role of IBR10 in this process. To dissect the metabolic complexity behind cell elongation, a quantitative-based phenotypic analysis combined with a multi-platform metabolomics approach was applied. We revealed that triacylglycerol and diacylglycerol breakdown were disrupted in ibr10, resulting in low sugar content and poor photosynthetic ability. Importantly, batch-learning self-organised map clustering revealed that threonine level was correlated with hypocotyl length. Consistently, exogenous threonine supply stimulated hypocotyl elongation, indicating that sucrose levels are not always correlated with etiolated seedling length, suggesting the contribution of amino acids in this process.

BATL: Bayesian annotations for targeted lipidomics

MOTIVATION

Bioinformatic tools capable of annotating, rapidly and reproducibly, large, targeted lipidomic datasets are limited. Specifically, few programs enable high-throughput peak assessment of liquid chromatography-electrospray ionization tandem mass spectrometry data acquired in either selected or multiple reaction monitoring modes.

RESULTS

We present here Bayesian Annotations for Targeted Lipidomics, a Gaussian naïve Bayes classifier for targeted lipidomics that annotates peak identities according to eight features related to retention time, intensity, and peak shape. Lipid identification is achieved by modeling distributions of these eight input features across biological conditions and maximizing the joint posterior probabilities of all peak identities at a given transition. When applied to sphingolipid and glycerophosphocholine selected reaction monitoring datasets, we demonstrate over 95% of all peaks are rapidly and correctly identified.

AVAILABILITY AND IMPLEMENTATION

BATL software is freely accessible online at https://complimet.ca/batl/ and is compatible with Safari, Firefox, Chrome and Edge.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Oxygen plasma modulates glucosinolate levels without affecting lipid contents and composition in Brassica napus seeds

Rapeseed contains high levels of glucosinolates (GSLs), playing pivotal roles in defense against herbivores and pests. As their presence in rapeseed reduces the value of the meal for animal feeding, intensive efforts to reduce them produced low-seed GSL cultivars. However, there is no such variety suitable for the south part of Japan. Here, we tested the effects of cold oxygen plasma (oxygen CP) on seed germination and GSL and lipid content, in 3 rapeseed cultivars. According to the cultivars, oxygen CP slightly stimulated seed germination and modified the GSL levels, and decreased GSL levels in Kizakinonatane but increased those in Nanashikibu. In contrast, it negligibly affected the lipid content and composition in the 3 cultivars. Thus, oxygen CP modulated seed GSL levels without affecting seed viability and lipid content. Future optimization of this technique may help optimize rapeseed GSL content without plant breeding.

Effects of periostin deficiency on kidney aging and lipid metabolism

Periostin plays a crucial role in fibrosis, which is involved in kidney aging. A few studies have shown that lipid metabolism is involved in kidney aging. We investigated the role of periostin in lipid metabolism during kidney aging. Renal function, fibrosis, and inflammatory markers were studied using urine, blood, and tissue samples from wild-type (WT) C57BL/6 mice and Postn-null mice of 2 and 24 months of age. Lipids were quantitatively profiled using liquid chromatography-tandem mass spectrometry in the multiple reaction monitoring mode. Renal function was worse and tubular atrophy/interstitial fibrosis, periostin expression, and inflammatory and fibrotic markers were more severe in aged WT mice than in young WT mice. In aged Postn-null mice, these changes were mitigated. Thirty-five differentially regulated lipids were identified. Phosphatidylcholines, cholesteryl ester, cholesterol, ceramide-1-phosphate, and CCL5 expression were significantly higher in aged WT mice than in aged Postn-null mice. Particularly, linoleic acid, linolenic acid, arachidonic acid, and docosahexaenoic acid differed strongly between the two groups. Lysophosphatidylcholine acyltransferase 2, which converts lysophosphatidylcholine to phosphatidylcholine, was significantly higher in aged WT mice than in aged Postn-null mice. Periostin expression in the kidneys increased with age, and periostin ablation delayed aging. Changes in lipids and their metabolism were found in Postn-null mice. Further research on the precise mechanisms of and relationships between lipid expression and metabolism, kidney aging, and periostin expression is warranted.

Systematic Evaluation of Liquid Chromatography (LC) Column Combinations for Application in Two-Dimensional LC Metabolomic Studies

In comparison to proteomics, the application of two-dimensional liquid chromatography (2D LC) in the field of metabolomics is still premature. One reason might be the elevated chemical complexity and the associated challenge of selecting proper separation conditions in each dimension. As orthogonality of dimensions is a major issue, the present study aimed for the identification of successful stationary phase combinations. To determine the degree of orthogonality, first, six different metrics, namely, Pearson's correlation coefficient (1 - |R|), the nearest-neighbor distances (H̅_NND), the "asterisk equations" (A_O), and surface coverage by bins (SC_G), convex hulls (SC_CH), and α-convex hulls (SC_αH), were critically assessed by 15 artificial 2D data sets, and a systematic parameter optimization of α-convex hulls was conducted. SG_G, SC_αH with α = 0.1, and H̅_NND generated valid results with sensitivity toward space utilization and data distribution and, therefore, were applied to pairs of experimental retention time sets obtained for >350 metabolites, selected to represent the chemical space of human urine. Normalized retention data were obtained for 23 chromatographic setups, comprising reversed-phase (RP), hydrophilic interaction liquid chromatography (HILIC), and mixed-mode separation systems with an ion exchange (IEX) contribution. As expected, no single LC setting provided separation of all considered analytes, but while conventional RP×HILIC combinations appeared rather complementary than orthogonal, the incorporation of IEX properties into the RP dimension substantially increased the 2D potential. Eventually, one of the most promising column combinations was implemented for an offline 2D LC time-of-flight mass spectrometry analysis of a lyophilized urine sample. Targeted screening resulted in a total of 164 detected metabolites and confirmed the outstanding coverage of the 2D retention space.

Advancing Semen Evaluation Using Lipidomics

Developing a deeper understanding of biological components of sperm is essential to improving cryopreservation techniques and reproductive technologies. To fully ascertain the functional determinants of fertility, lipidomic methods have come to the forefront. Lipidomics is the study of the lipid profile (lipidome) within a cell, tissue, or organism and provides a quantitative analysis of the lipid content in that sample. Sperm cells are composed of various lipids, each with their unique contribution to the overall function of the cell. Lipidomics has already been used to find new and exciting information regarding the fatty acid content of sperm cells from different species. While the applications of lipidomics are rapidly evolving, gaps in the knowledge base remain unresolved. Current limitations of lipidomics studies include the number of available samples to analyze and the total amount of cells within those samples needed to detect changes in the lipid profiles across different subjects. The information obtained through lipidomics research is essential to systems and cellular biology. This review provides a concise analysis of the most recent developments in lipidomic research. This scientific resource is important because these developments can be used to not only combat the reproductive challenges faced when using cryopreserved semen and artificial reproductive technologies in livestock such as cattle, but also other mammals, such as humans or endangered species.

TAILOR-MS, a Python Package that Deciphers Complex Triacylglycerol Fatty Acyl Structures: Applications for Bovine Milk and Infant Formulas

Liquid chromatography tandem mass spectrometry (LC/MS) and other mass spectrometric technologies have been widely applied for triacylglycerol profiling. One challenge for targeted identification of fatty acyl moieties that constitute triacylglycerol species in biological samples is the numerous combinations of 3 fatty acyl groups that can form a triacylglycerol molecule. Manual determination of triacylglycerol structures based on peak intensities and retention time can be highly inefficient and error-prone. To resolve this, we have developed TAILOR-MS, a Python (programming language) package that aims at assisting: (1) the generation of targeted LC/MS methods for triacylglycerol detection and (2) automating triacylglycerol structural determination and prediction. To assess the performance of TAILOR-MS, we conducted LC/MS triacylglycerol profiling of bovine milk and two infant formulas. Our results confirmed dissimilarities between bovine milk and infant formula triacylglycerol composition. Furthermore, we identified 247 triacylglycerol species and predicted the possible existence of another 317 in the bovine milk sample, representing one of the most comprehensive reports on the triacylglycerol composition of bovine milk thus far. Likewise, we presented here a complete infant formula triacylglycerol profile and reported >200 triacylglycerol species. TAILOR-MS dramatically shortened the time required for triacylglycerol structural identification from hours to seconds and performed decent structural predictions in the absence of some triacylglycerol constituent peaks. Taken together, TAILOR-MS is a valuable tool that can greatly save time and improve accuracy for targeted LC/MS triacylglycerol profiling.

Skeletal muscle-specific Keap1 disruption modulates fatty acid utilization and enhances exercise capacity in female mice

Skeletal muscle health is important for the prevention of various age-related diseases. The loss of skeletal muscle mass, which is known as sarcopenia, underlies physical disability, poor quality of life and chronic diseases in elderly people. The transcription factor NRF2 plays important roles in the regulation of the cellular defense against oxidative stress, as well as the metabolism and mitochondrial activity. To determine the contribution of skeletal muscle NRF2 to exercise capacity, we conducted skeletal muscle-specific inhibition of KEAP1, which is a negative regulator of NRF2, and examined the cell-autonomous and non-cell-autonomous effects of NRF2 pathway activation in skeletal muscles. We found that NRF2 activation in skeletal muscles increased slow oxidative muscle fiber type and improved exercise endurance capacity in female mice. We also observed that female mice with NRF2 pathway activation in their skeletal muscles exhibited enhanced exercise-induced mobilization and β-oxidation of fatty acids. These results indicate that NRF2 activation in skeletal muscles promotes communication with adipose tissues via humoral and/or neuronal signaling and facilitates the utilization of fatty acids as an energy source, resulting in increased mitochondrial activity and efficient energy production during exercise, which leads to improved exercise endurance.

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Systemic Keap1 knockdown enhances exercise endurance capacity in mice.

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Keap1 deficiency in skeletal muscle activates NRF2 pathway.

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Keap1 deficiency in skeletal muscle enhances endurance capacity in female mice.

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Keap1 deficiency in skeletal muscle promotes exercise-induced fatty acid utilization.

Development of a Pteris vittata L. compound database by widely targeted metabolomics profiling

The objective of this work was the development of a detailed, extensive and reliable database of the metabolomes of P. vittata. Using an ultra-performance liquid chromatography-triple quadrupole tandem mass spectrometry system (UPLC-QqQ-MS/MS) and based on the knowledge of retention time and mass spectral characteristics of an in-house collection of authentic standards, we screened for the presence of a large collection of natural compounds. The database represents 359 authenticated metabolites, comprising 220 primary and 139 secondary metabolites (70 flavonoids, 16 phenylpropanoic acid derivatives, five coumarins, two stilbenoids, 14 benzoic acids, nine phenols, 20 alkaloids and three terpenoids). Comparison of the accumulation of these compounds in two tissues showed that the aerial parts were enriched in flavonols, whereas the subterranean parts were enriched in anthocyanins. The comprehensive database developed here will be beneficial in improving the understanding of the chemical basis of plant therapeutic profile using multivariate analysis, with a particular example of antioxidant activity.

Medicago sativa and Medicago truncatula Show Contrasting Root Metabolic Responses to Drought

Drought is an environmental stressor that affects crop yield worldwide. Understanding plant physiological responses to stress conditions is needed to secure food in future climate conditions. In this study, we applied a combination of plant physiology and metabolomic techniques to understand plant responses to progressive water deficit focusing on the root system. We chose two legume plants with contrasting tolerance to drought, the widely cultivated alfalfa Medicago sativa (Ms) and the model legume Medicago truncatula (Mt) for comparative analysis. Ms taproot (tapR) and Mt fibrous root (fibR) biomass increased during drought, while a progressive decline in water content was observed in both species. Metabolomic analysis allowed the identification of key metabolites in the different tissues tested. Under drought, carbohydrates, abscisic acid, and proline predominantly accumulated in leaves and tapRs, whereas flavonoids increased in fibRs in both species. Raffinose-family related metabolites accumulated during drought. Along with an accumulation of root sucrose in plants subjected to drought, both species showed a decrease in sucrose synthase (SUS) activity related to a reduction in the transcript level of SUS1, the main SUS gene. This study highlights the relevance of root carbon metabolism during drought conditions and provides evidence on the specific accumulation of metabolites throughout the root system.

Metabolomic Analysis of Response to Nitrogen-Limiting Conditions in Yarrowia spp

Yarrowia is a yeast genus that has been used as a model oleaginous taxon for a wide array of studies. However, information regarding metabolite changes within Yarrowia spp. under different environmental conditions is still limited. Among various factors affecting Yarrowia metabolism, nitrogen-limiting conditions have a profound effect on the metabolic state of yeast. In this study, a time-course LC-MS/MS-based metabolome analysis of Y. lipolytica was performed to determine the optimal cultivation time and carbon-to-nitrogen ratio for studying the effects of nitrogen-limiting conditions on Yarrowia; we found that cultivation time of 36 h and carbon-to-nitrogen ratio of 4:1 and 5:0 was suitable for studying the effects of nitrogen-limiting conditions on Yarrowia and these conditions were applied to six strains of Yarrowia. These six strains of Yarrowia showed similar responses to nitrogen-limiting conditions; however, each strain had a unique metabolomic profile. Purine and pyrimidine metabolism were the most highly affected biological pathways in nitrogen-limiting conditions, indicating that these conditions affect energy availability within cells. This stress leads to a shift in cells to the utilization of a less ATP-dependent biological pathway. This information will be beneficial for the development of Yarrowia strains for further scientific and industrial applications.

Identification of a Unique Type of Isoflavone O-Methyltransferase, GmIOMT1, Based on Multi-Omics Analysis of Soybean under Biotic Stress

Isoflavonoids are commonly found in leguminous plants. Glycitein is one of the isoflavones produced by soybean. The genes encoding the enzymes in the isoflavone biosynthetic pathway have mostly been identified and characterized. However, the gene(s) for isoflavone O-methyltransferase (IOMT), which catalyzes the last step of glycitein biosynthesis, has not yet been identified. In this study, we conducted multi-omics analyses of fungal-inoculated soybean and indicated that glycitein biosynthesis was induced in response to biotic stress. Moreover, we identified a unique type of IOMT, which participates in glycitein biosynthesis. Soybean seedlings were inoculated with Aspergillus oryzae or Rhizopus oligosporus and sampled daily for 8 d. Multi-omics analyses were conducted using liquid chromatography-tandem mass spectrometry and RNA sequencing. Metabolome analysis revealed that glycitein derivatives increased following fungal inoculation. Transcriptome co-expression analysis identified two candidate IOMTs that were co-expressed with the gene encoding flavonoid 6-hydroxylase (F6H), the key enzyme in glycitein biosynthesis. The enzymatic assay of the two IOMTs using respective recombinant proteins showed that one IOMT, named as GmIOMT1, produced glycitein. Unlike other IOMTs, GmIOMT1 belongs to the cation-dependent OMT family and exhibited the highest activity with Zn2+ among cations tested. Moreover, we demonstrated that GmIOMT1 overexpression increased the levels of glycitein derivatives in soybean hairy roots when F6H was co-expressed. These results strongly suggest that GmIOMT1 participates in inducing glycitein biosynthesis in response to biotic stress.

Dynamic Metabolome Analysis Reveals the Metabolic Fate of Medium-Chain Fatty Acids in AML12 Cells

Several studies in hepatocyte cell lines reported that medium-chain fatty acids (MCFAs) with 6-12 carbons showed different metabolic properties from long-chain fatty acids (LCFAs). However, these studies reported unclear effects of different fatty acid molecules on hepatocyte metabolism. This study is aimed to capture the metabolic kinetics of MCFA assimilation in AML12 cells treated with octanoic acid (FA 8:0), decanoic acid (FA 10:0), or lauric acid (FA12:0) [LCFA; oleic acid (FA 18:1)] via metabolic profiling and dynamic metabolome analysis with ¹³C-labeling. The concentrations of total ketone bodies in the media of cells treated with FA 8:0 or FA 10:0 were 3.22- or 3.69-fold higher than those obtained with FA 18:1 treatment, respectively. FA 12:0 treatment did not significantly increase ketone body levels compared to DMSO treatment (control), whereas FA 12:0 treatment increased intracellular triacylglycerol (TG) levels 15.4 times compared to the control. Metabolic profiles of FA 12:0-treated samples differed from those of the FA 8:0-treated and FA 10:0-treated samples, suggesting that metabolic assimilation of MCFAs differed significantly depending on the MCFA type. Furthermore, the dynamic metabolome analysis clearly revealed that FA 8:0 was rapidly and quantitatively oxidized to acetyl-CoA and assimilated into ketone bodies, citrate cycle intermediates, and glucogenic amino acids but not readily into TGs.

SWATH-MS for metabolomics and lipidomics: critical aspects of qualitative and quantitative analysis

INTRODUCTION

While liquid chromatography coupled to mass spectrometric detection in the selected reaction monitoring detection mode offers the best quantification sensitivity for omics, the number of target analytes is limited, must be predefined and specific methods developed. Data independent acquisition (DIA), including SWATH using quadrupole time of flight or orbitrap mass spectrometers and generic acquisition methods, has emerged as a powerful alternative technique for quantitative and qualitative analyses since it can cover a wide range of analytes without predefinition.

OBJECTIVES

Here we review the current state of DIA, SWATH-MS and highlight novel acquisition strategies for metabolomics and lipidomics and opportunities for data analysis tools.

METHOD

Different databases were searched for papers that report developments and applications of DIA and in particular SWATH-MS in metabolomics and lipidomics.

RESULTS

DIA methods generate digital sample records that can be mined retrospectively as further knowledge is gained and, with standardized acquisition schemes, used in multiple studies. The different chemical spaces of metabolites and lipids require different specificities, hence different acquisition and data processing approaches must be considered for their analysis.

CONCLUSIONS

Although the hardware and acquisition modes are well defined for SWATH-MS, a major challenge for routine use remains the lack of appropriate software tools capable of handling large datasets and large numbers of analytes.

Metabolomics Analysis Reveals Global Metabolic Changes in the Evolved E. coli Strain with Improved Growth and 1-Butanol Production in Minimal Medium

Production of 1-butanol from microorganisms has garnered significant interest due to its prospect as a drop-in biofuel and precursor for a variety of commercially relevant chemicals. Previously, high 1-butanol titer has been reported in Escherichia coli strain JCL166, which contains a modified clostridial 1-butanol pathway. Although conventional and metabolomics-based strain improvement strategies of E. coli strain JCL166 have been successful in improving production in rich medium, 1-butanol titer was severely limited in minimal medium. To further improve growth and consequently 1-butanol production in minimal medium, adaptive laboratory evolution (ALE) using mutD5 mutator plasmid was done on JCL166. Comparative metabolomics analysis of JCL166 and BP1 revealed global perturbations in the evolved strain BP1 compared to JCL166 (44 out of 64 metabolites), encompassing major metabolic pathways such as glycolysis, nucleotide biosynthesis, and CoA-related processes. Collectively, these metabolic changes in BP1 result in improved growth and, consequently, 1-butanol production in minimal medium. Furthermore, we found that the mutation in ihfB caused by ALE had a significant effect on the metabolome profile of the evolved strain. This study demonstrates how metabolomics was utilized for characterization of ALE-developed strains to understand the overall effect of mutations acquired through evolution.

Accumulation of sugars and nucleosides in response to high salt and butanol stress in 1-butanol producing Synechococcus elongatus

1-Butanol production using photosynthetic organisms such as cyanobacteria has garnered interest among researchers due to its high potential as a sustainable biofuel. Previously, the cyanobacterium Synechococcus elongatus PCC 7942 was engineered to produce 1-butanol through the introduction of a modified CoA-dependent pathway. S. elongatus strain DC11, a high producer of 1-butanol, was constructed based on metabolomics-assisted strain engineering. DC11 can reach a production titer of 418.7 mg/L in 6 days, cutting the production time in half compared to the previously constructed DC7. Regardless, the final 1-butanol titer of DC11 was still low compared to other microbial hosts. Sensitivity towards 1-butanol of the producing strain has been known as one of main hurdles for improving cyanobacterial production system. Thus, to improve cyanobacterial-based 1-butanol production in the future, we employed the metabolomics approach to study the intrinsic effect of improved 1-butanol productivity in DC11. This study focused on metabolite profiling of DC11 using LC/MS/MS. Results showed that there is an accumulation of disaccharide-P and sucrose/trehalose in DC11 compared to the DC7. These metabolites were previously reported to have a role in salt and alcohol stress response in cyanobacteria and therefore, DC11 was subjected to 0.2 M of NaCl and 1000 mg/L of 1-butanol for further investigation. DC11 with stress treatment showed a more prominent accumulation of sugars and nucleosides compared to control. The results obtained from this study may be beneficial for future strain improvement strategies in S. elongatus, particularly addressing the metabolic response of this strain upon 1-butanol stress.

Cyclic Change of Fatty Acid Composition in Meibum During the Menstrual Cycle

Purpose

To evaluate the fatty acid (FA) composition in the meibum of pre- and postmenopausal women and age-matched men.

Methods

This prospective study involved 24 healthy subjects; six premenopausal women in their 30s with a regular menstrual cycle (young-female [yF] group), six postmenopausal women in their 60s (elderly-female [eF] group), and 12 age-matched men (i.e., young-male [yM] and elderly-male [eM] groups, respectively). The menstrual cycle was divided into six phases (phase I–VI). Meibum was obtained from the meibomian gland orifices via a Daviel spoon, and its FA composition was then analyzed via gas chromatography mass spectrometry (GC-MS). Principal component analysis (PCA) was performed on the GC-MS results.

Results

The mean FA composition of all subjects was 40% saturated FAs (SFA) and 60% unsaturated FAs (UFAs). The PCA results of all groups indicated two categories (PC1 [77.5%] and PC2 [12.4%]); one consisting of yF-group samples of mainly phase II and III and the other consisting of the yF-group samples of the rest of the cycle, as well as from eF-group, yM-group, and eM-group samples. Each group had a distinctive nature. The FAs that most contributed to PC1 were C_14:0, C_16:0, and C_18:0 in a positive correlation, and C_18:1n9 in a negative correlation.

Conclusions

FA composition noticeably changes during the menstrual cycle and is somewhat affected by sex and age. The ratio of SFAs (C_16:0, C_18:0) to mono-UFAs (C_18:1n9) in the FA composition might have an impact on the lipid quality of meibum, thus suggesting alteration of its melting temperature and viscosity.

Comparison of metabolic profiles of yeasts based on the difference of the Crabtree positive and negative

The Crabtree effect involves energy management in which yeasts utilize glycolysis as the terminal electron acceptor instead of oxygen, despite the presence of sufficient dissolved oxygen, when oxygen concentrations exceed a certain limit. The Crabtree effect is detrimental to bakery yeast production, because it results in lower cellular glucose yields. Batch culture of Saccharomyces cerevisiae, a Crabtree positive yeast, decreased the cell yield of glucose and produced large amounts of ethanol despite a high specific glucose consumption rate compared to Candida utilis, a Crabtree negative yeast. This study investigated the effect of these characteristics on metabolite levels. We performed metabolome analysis of both yeasts during each growth phase of batch culture using liquid chromatography-tandem mass spectrometry and gas chromatography-mass spectrometry. Principle component analysis of metabolome data indicated that the Crabtree effect affected metabolites related to NADH synthesis in central metabolism. The amount of these metabolites in S. cerevisiae was lower than that in C. utilis. However, to maintain the specific glucose consumption rate at high levels, yeasts must avoid depletion of NAD⁺, which is essential for glucose utilization. Our results indicated that NADH was oxidized by converting acetaldehyde to ethanol in S. cerevisiae, which is in accordance with previous reports. Therefore, the specific NADH production rates of S. cerevisiae and C. utilis did not show a difference. This study suggested that NAD⁺/NADH ratio is disrupted by the Crabtree effect, which in turn influenced central metabolism and that S. cerevisiae maintained the NAD⁺/NADH ratio by producing ethanol.

Mass Spectrometry Data Repository Enhances Novel Metabolite Discoveries with Advances in Computational Metabolomics

Mass spectrometry raw data repositories, including Metabolomics Workbench and MetaboLights, have contributed to increased transparency in metabolomics studies and the discovery of novel insights in biology by reanalysis with updated computational metabolomics tools. Herein, we reanalyzed the previously published lipidomics data from nine algal species, resulting in the annotation of 1437 lipids achieving a 40% increase in annotation compared to the previous results. Specifically, diacylglyceryl-carboxyhydroxy-methylcholine (DGCC) in Pavlova lutheri and Pleurochrysis carterae, glucuronosyldiacylglycerol (GlcADG) in Euglena gracilis, and P. carterae, phosphatidylmethanol (PMeOH) in E. gracilis, and several oxidized phospholipids (oxidized phosphatidylcholine, OxPC; phosphatidylethanolamine, OxPE; phosphatidylglycerol, OxPG; phosphatidylinositol, OxPI) in Chlorella variabilis were newly characterized with the enriched lipid spectral databases. Moreover, we integrated the data from untargeted and targeted analyses from data independent tandem mass spectrometry (DIA-MS/MS) acquisition, specifically the sequential window acquisition of all theoretical fragment-ion MS/MS (SWATH-MS/MS) spectra, to increase the lipidomic annotation coverage. After the creation of a global library of precursor and diagnostic ions of lipids by the MS-DIAL untargeted analysis, the co-eluted DIA-MS/MS spectra were resolved in MRMPROBS targeted analysis by tracing the specific product ions involved in acyl chain compositions. Our results indicated that the metabolite quantifications based on DIA-MS/MS chromatograms were somewhat inferior to the MS1-centric quantifications, while the annotation coverage outperformed those of the untargeted analysis of the data dependent and DIA-MS/MS data. Consequently, integrated analyses of untargeted and targeted approaches are necessary to extract the maximum amount of metabolome information, and our results showcase the value of data repositories for the discovery of novel insights in lipid biology.

Identifying Double Bond Positions in Phospholipids Using Liquid Chromatography-Triple Quadrupole Tandem Mass Spectrometry Based on Oxygen Attachment Dissociation

Lipids, a class of biomolecules, play a significant role in the physiological system. In this study, gas-phase hydroxyl radicals (OH·) and atomic oxygens (O) were introduced into the collision cell of a triple quadruple mass spectrometer (TQ-MS) to determine the positions of the double bond in unsaturated phospholipids. A microwave-driven compact plasma generator was used as the OH·/O source. The reaction between OH·/O and the precursor ions passing through the collision cell generates product ions that correspond to the double bond positions in the fatty acyl chain. This double bond position specific fragmentation process initiated by the attachment of OH·/O to the double bond of a fatty acyl chain is a characteristic of oxygen attachment dissociation (OAD). A TQ-MS incorporating OAD, in combination with liquid chromatography, permitted a high throughput analysis of the double bond positions in complex biomolecules. It is important to know the precise position of double bonds in lipids, since these molecules can have widely different functionalities based on the position of the double bonds. The assignment of double bond positions in a mixture of eight standard samples of phosphatidylcholines (phospholipids with choline head groups) with multiple saturated fatty acyl chains attached was successfully demonstrated.

A High-Throughput Targeted Metabolomics Workflow for the Detection of 200 Polar Metabolites in Central Carbon Metabolism

Targeted metabolomics aims to analyze a set of pre-selected metabolites from biologically relevant metabolic pathways. The triple quadrupole mass spectrometry (QqQ-MS) based multiple reaction monitoring (MRM) technique is the most widely approach used for targeted metabolomics, and features high selectivity and sensitivity, good reproducibility and wide dynamic range in quantitative analysis. Here, we describe an MRM based targeted metabolomics workflow for the quantitative analysis of 200 polar metabolites in central carbon metabolic pathways, including the data acquisition method and the automated data processing procedures using our in-house R package MRMAnalyzer. The workflow described in this chapter combines a hydrophilic interaction liquid chromatography (HILIC) separation and positive/negative ion polarity switching based MS detection, and is able to acquire data from multiple types of biological samples such as bacteria, cultured mammalian cells, animal tissues and biofluids (e.g., serum and urine). Finally, the MRMAnalyzer software can automatically process the generated large-scale data set with high efficiency. We hope it is a valuable and efficient workflow for researchers to facilitate the respective biological studies using targeted metabolomics.

Strategies for large-scale targeted metabolomics quantification by liquid chromatography-mass spectrometry

Advances in liquid chromatography-mass spectrometry (LC-MS) instruments and analytical strategies have brought about great progress in targeted metabolomics analysis. This methodology is now capable of performing precise targeted measurement of dozens or hundreds of metabolites in complex biological samples. Classic targeted quantification assay using the multiple reaction monitoring (MRM) mode has been the foundation of high-quality metabolite quantitation. However, utilization of this strategy in biological studies has been limited by its relatively low metabolite coverage and throughput capacity. A number of methods for large-scale targeted metabolomics assay which have been developed overcome these limitations. These strategies have enabled extended metabolite coverage which is defined as targeting of large numbers of metabolites, while maintaining reliable quantification performance. These recently developed techniques thus bridge the gap between traditional targeted metabolite quantification and untargeted metabolomics profiling, and have proven to be powerful tools for metabolomics study. Although the LC-MRM-MS strategy has been used widely in large-scale metabolomics quantification analysis due to its fast scan speed and ideal analytic stability, there are still drawbacks which are due to the low resolution of the triple quadrupole instruments used for MRM assays. New approaches have been developed to expand the options for large-scale targeted metabolomics study, using high-resolution instruments such as parallel reaction monitoring (PRM). MRM and PRM-based techniques are now attractive strategies for quantitative metabolomics analysis and high-throughput biomarker discovery. Here we provide an overview of the major developments in LC-MS-based strategies for large-scale targeted metabolomics quantification in biological samples. The advantages of LC-MRM/PRM-MS based analytical strategies which may be used in multiplexed and high throughput quantitation for a wide range of metabolites are highlighted. In particular, PRM and MRM strategies are compared, and we summarize the work flow commonly used for large-scale targeted metabolomics analysis including sample preparation, LC separation and data analysis, as well as recent applications in biological studies.

Directed strain evolution restructures metabolism for 1-butanol production in minimal media

Engineering a microbial strain for production sometimes entails metabolic modifications that impair essential physiological processes for growth or production. Restoring these functions may require amending a variety of non-obvious physiological networks, and thus, rational design strategies may not be practical. Here we demonstrate that growth and production may be restored by evolution that repairs impaired metabolic function. Furthermore, we use genomics, metabolomics and proteomics to identify several underlying mutations and metabolic perturbations that allow metabolism to repair. Previously, high titers of butanol production were achieved by Escherichia coli using a growth-coupled, modified Clostridial CoA-dependent pathway after all native fermentative pathways were deleted. However, production was only observed in rich media. Native metabolic function of the host was unable to support growth and production in minimal media. We use directed cell evolution to repair this phenotype and observed improved growth, titers and butanol yields. We found a mutation in pcnB which resulted in decreased plasmid copy numbers and pathway enzymes to balance resource utilization. Increased protein abundance was measured for biosynthetic pathways, glycolytic enzymes have increased activity, and adenosyl energy charge was increased. We also found mutations in the ArcAB two-component system and integration host factor (IHF) that tune redox metabolism to alter byproduct formation. These results demonstrate that directed strain evolution can enable systematic adaptations to repair metabolic function and enhance microbial production. Furthermore, these results demonstrate the versatile repair capabilities of cell metabolism and highlight important aspects of cell physiology that are required for production in minimal media.