Compound annotation in liquid chromatography/high-resolution mass spectrometry based metabolomics: robust adduct ion determination as a prerequisite to structure prediction in electrospray ionization mass spectra

Application of Weighted Gene Co-Expression Network Analysis to Metabolomic Data from an ApoA-I Knockout Mouse Model

As the ability to collect profiling data in metabolomics increases substantially with the advances in Liquid Chromatography-Mass Spectrometry (LC-MS) instruments, it is urgent to develop new and powerful data analysis approaches to match the big data collected and to extract as much meaningful information as possible from tens of thousands of molecular features. Here, we applied weighted gene co-expression network analysis (WGCNA), an algorithm popularly used in microarray or RNA sequencing, to plasma metabolomic data and demonstrated several advantages of WGCNA over conventional statistical approaches such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). By using WGCNA, a large number of molecular features were clustered into a few modules to reduce the dimension of a dataset, the impact of phenotypic traits such as diet type and genotype on the plasma metabolome was evaluated quantitatively, and hub metabolites were found based on the network graph. Our work revealed that WGCNA is a very powerful tool to decipher, interpret, and visualize metabolomic datasets.

Secreted metabolome of porcine blastocysts encapsulated within in vitro 3D alginate hydrogel culture systems undergoing morphological changes provides insights into specific mechanisms involved in the initiation of porcine conceptus elongation

CONTEXT

The exact mechanisms regulating the initiation of porcine conceptus elongation are not known due to the complexity of the uterine environment.

AIMS

To identify contributing factors for initiation of conceptus elongation in vitro , this study evaluated differential metabolite abundance within media following culture of blastocysts within unmodified alginate (ALG) or Arg-Gly-Asp (RGD)-modified alginate hydrogel culture systems.

METHODS

Blastocysts were harvested from pregnant gilts, encapsulated within ALG or RGD or as non-encapsulated control blastocysts (CONT), and cultured. At the termination of 96h culture, media were separated into blastocyst media groups: non-encapsulated control blastocysts (CONT); ALG and RGD blastocysts with no morphological change (ALG- and RGD-); ALG and RGD blastocysts with morphological changes (ALG+ and RGD+) and evaluated for non-targeted metabolomic profiling by liquid chromatography (LC)-mass spectrometry (MS) techniques and gas chromatography-(GC-MS).

KEY RESULTS

Analysis of variance identified 280 (LC-MS) and 1 (GC-MS) compounds that differed (P <0.05), of which 134 (LC-MS) and 1 (GC-MS) were annotated. Metabolites abundance between ALG+ vs ALG-, RGD+ vs RGD-, and RGD+ vs ALG+ were further investigated to identify potential differences in metabolic processes during the initiation of elongation.

CONCLUSIONS

This study identified changes in phospholipid, glycosphingolipid, lipid signalling, and amino acid metabolic processes as potential RGD-independent mechanisms of elongation and identified changes in lysophosphatidylcholine and sphingolipid secretions during RGD-mediated elongation.

IMPLICATIONS

These results illustrate changes in phospholipid and sphingolipid metabolic processes and secretions may act as mediators of the RGD-integrin adhesion that promotes porcine conceptus elongation.

Towards Unbiased Evaluation of Ionization Performance in LC-HRMS Metabolomics Method Development

As metabolomics increasingly finds its way from basic science into applied and regulatory environments, analytical demands on nontargeted mass spectrometric detection methods continue to rise. In addition to improved chemical comprehensiveness, current developments aim at enhanced robustness and repeatability to allow long-term, inter-study, and meta-analyses. Comprehensive metabolomics relies on electrospray ionization (ESI) as the most versatile ionization technique, and recent liquid chromatography-high resolution mass spectrometry (LC-HRMS) instrumentation continues to overcome technical limitations that have hindered the adoption of ESI for applications in the past. Still, developing and standardizing nontargeted ESI methods and instrumental setups remains costly in terms of time and required chemicals, as large panels of metabolite standards are needed to reflect biochemical diversity. In this paper, we investigated in how far a nontargeted pilot experiment, consisting only of a few measurements of a test sample dilution series and comprehensive statistical analysis, can replace conventional targeted evaluation procedures. To examine this potential, two instrumental ESI ion source setups were compared, reflecting a common scenario in practical method development. Two types of feature evaluations were performed, (a) summary statistics solely involving feature intensity values, and (b) analyses additionally including chemical interpretation. Results were compared in detail to a targeted evaluation of a large metabolite standard panel. We reflect on the advantages and shortcomings of both strategies in the context of current harmonization initiatives in the metabolomics field.

Spontaneous Water Radical Cation Oxidation at Double Bonds in Microdroplets

Spontaneous oxidation of compounds containing diverse X=Y moieties (e.g., sulfonamides, ketones, esters, sulfones) occurs readily in organic-solvent microdroplets. This surprising phenomenon is proposed to be driven by the generation of an intermediate species [M+H₂O]^+·: a covalent adduct of water radical cation (H₂O^+·) with the reactant molecule (M). The adduct is observed in the positive ion mass spectrum while its formation in the interfacial region of the microdroplet (i.e., at the air-droplet interface) is indicated by the strong dependence of the oxidation product formation on the spray distance (which reflects the droplet size and consequently the surface-to-volume ratio) and the solvent composition. Importantly, based on the screening of a ca. 21,000-compound library and the detailed consideration of six functional groups, the formation of a molecular adduct with the water radical cation is a significant route to ionization in positive ion mode electrospray, where it is favored in those compounds with X=Y moieties which lack basic groups. A set of model monofunctional systems was studied and in one case, benzyl benzoate, evidence was found for oxidation driven by hydroxyl radical adduct formation followed by protonation in addition to the dominant water radical cation addition process. Significant implications of molecular ionization by water radical cations for oxidation processes in atmospheric aerosols, analytical mass spectrometry and small-scale synthesis are noted.

Untargeted metabolomics analysis identifies creatine, myo-inositol, and lipid pathway modulation in a murine model of tendinopathy

Tendinopathy has been broadly characterized as alterations in cell proliferation, extracellular matrix turnover/synthesis, and inflammatory alterations. However, the underlying glucose metabolism pathways which contribute to these responses have not been well explored. The potential link between glucose metabolism and tendon pathology is interesting from a global standpoint since the development of spontaneous tendinopathy is associated with systemic metabolic disorders including diabetes mellitus. Therefore, the overarching goal of this study was to understand the potential pathogenic role of glucose metabolism-driven mechanisms in the development of tendinopathy. To test this, we have utilized an untargeted metabolomics approach to discover pathways which may be altered following tendinopathic injury and treadmill running in an established murine model of TGF-β1 induced tendinopathy. While specific tendon glucose alterations were not observed via metabolomics or 18 F-fluoroeoxyglucose (FDG) positron emission tomography/microcomputed tomography imaging (18 F-FDG PET/CT), metabolites including creatinine, D-chiro-inositol, and lipids were dysregulated following tendon injury. As novel pathways for manipulation, the creatine pathway, myo-inositol pathway, and lipid signaling may lead to the development of enhanced preventative strategies and therapeutic options for all patients who suffer from tendon-related injuries.

Assessing Drought and Heat Stress-Induced Changes in the Cotton Leaf Metabolome and Their Relationship With Hyperspectral Reflectance

The study of phenotypes that reveal mechanisms of adaptation to drought and heat stress is crucial for the development of climate resilient crops in the face of climate uncertainty. The leaf metabolome effectively summarizes stress-driven perturbations of the plant physiological status and represents an intermediate phenotype that bridges the plant genome and phenome. The objective of this study was to analyze the effect of water deficit and heat stress on the leaf metabolome of 22 genetically diverse accessions of upland cotton grown in the Arizona low desert over two consecutive years. Results revealed that membrane lipid remodeling was the main leaf mechanism of adaptation to drought. The magnitude of metabolic adaptations to drought, which had an impact on fiber traits, was found to be quantitatively and qualitatively associated with different stress severity levels during the two years of the field trial. Leaf-level hyperspectral reflectance data were also used to predict the leaf metabolite profiles of the cotton accessions. Multivariate statistical models using hyperspectral data accurately estimated (R ² > 0.7 in ∼34% of the metabolites) and predicted (Q ² > 0.5 in 15-25% of the metabolites) many leaf metabolites. Predicted values of metabolites could efficiently discriminate stressed and non-stressed samples and reveal which regions of the reflectance spectrum were the most informative for predictions. Combined together, these findings suggest that hyperspectral sensors can be used for the rapid, non-destructive estimation of leaf metabolites, which can summarize the plant physiological status.

Ion identity molecular networking for mass spectrometry-based metabolomics in the GNPS environment

Molecular networking connects mass spectra of molecules based on the similarity of their fragmentation patterns. However, during ionization, molecules commonly form multiple ion species with different fragmentation behavior. As a result, the fragmentation spectra of these ion species often remain unconnected in tandem mass spectrometry-based molecular networks, leading to redundant and disconnected sub-networks of the same compound classes. To overcome this bottleneck, we develop Ion Identity Molecular Networking (IIMN) that integrates chromatographic peak shape correlation analysis into molecular networks to connect and collapse different ion species of the same molecule. The new feature relationships improve network connectivity for structurally related molecules, can be used to reveal unknown ion-ligand complexes, enhance annotation within molecular networks, and facilitate the expansion of spectral reference libraries. IIMN is integrated into various open source feature finding tools and the GNPS environment. Moreover, IIMN-based spectral libraries with a broad coverage of ion species are publicly available.

Comprehensive Evaluation of Metabolites and Minerals in 6 Microgreen Species and the Influence of Maturity

BACKGROUND

Microgreens are the young leafy greens of many vegetables, herbs, grains, and flowers with potential to promote human health and sustainably diversify the global food system. For successful further integration into the global food system and evaluation of their health impacts, it is critical to elucidate and optimize their nutritional quality.

OBJECTIVES

We aimed to comprehensively evaluate the metabolite and mineral contents of 6 microgreen species, and the influence of maturity on their contents.

METHODS

Plant species evaluated were from the Brassicaceae (arugula, broccoli, and red cabbage), Amaranthaceae (red beet and red amaranth), and Fabaceae (pea) plant families. Nontargeted metabolomics and ionomics analyses were performed to examine the metabolites and minerals, respectively, in each microgreen species and its mature counterpart.

RESULTS

Nontargeted metabolomics analysis detected 3321 compounds, 1263 of which were annotated and included nutrients and bioactive compounds. Ionomics analysis detected and quantified 26 minerals including macrominerals, trace minerals, ultratrace minerals, and other metals. Principal component analysis indicated that microgreens have distinct metabolite and mineral profiles compared with one another and with their mature counterparts. Several compounds were higher (P  < 0.05; fold change ≥2) in microgreens compared with their mature counterparts, whereas some were not different or lower. In many cases, compounds that were higher in microgreens compared with the mature counterpart were also unique to that microgreen species.

CONCLUSIONS

These data provide evidence for the nutritional quality of microgreens, and can inform future research and development aimed at characterizing and optimizing microgreen nutritional quality and health impacts.

Discrimination of meat from fur-producing and food-providing animals using mass spectrometry-based proteomics

Non-edible meat from fur-producing animals entering into meat consumption chain could pose a serious threat to public health. For the purpose of risk prevention and control of meat safety, in this study, marker peptides for discriminating non-edible meat of fur-producing animals (including fox, silver fox, blue fox, raccoon dog, ussuri raccoon dog, mink and American mink) from meat of food-providing animals (including pig, cattle, sheep and donkey) were explored by shot-gun proteomics and verified by target approach. Two mass spectrometry platforms combined with bioinformatic and chemometric tools were integratedly emloyed for method development. Meat samples were first subjected to in-solution protein digestion and the subsequently tryptic peptides were profiled and quantitated by ultra-high pressure liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-Q/TOF MS) with sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH-MS) mode. Candidate marker peptides screened by chemometric tools were further filtered for their biological specificity and detectability through bioinformatics analysis as well as multiple reaction monitoring (MRM) verification with UHPLC-triple quadrupole mass spectrometry (UHPLC-QQQ MS). As a result, 9 peptides, out of 104 candidates, were selected as markers for discriminating analysis, of which DQTLQEELAR was validated as primary indicator of non-edible meat from the concerned fur-producing animals. An MRM method based on the developed marker peptides for routine use was finally proposed for risk alarming of non-edible meat from fur-producing animals in food safety control.

Exploiting adduct formation through an auxiliary spray in liquid chromatography-electrospray ionization mass spectrometry to improve charge-carrier identification

We describe a simple and effective approach to probe adduct formation in liquid chromatography - electrospray ionization mass spectrometry (LC-ESI-MS) and help designate and/or confirm which particular analyte is leading to which particular charged species that is detected. A compound tends to form similar adducts with adduct-forming analogs, at various abundance levels, of course. It is based on this understanding that in this work we probed adduct formation by modulating the adduct-forming analogs and observing the adducts formed with these analogs to lend experimental evidence to adduct annotation. The approach was implemented through an auxiliary spray and made possible thanks to the interaction between the plumes of the sample spray or main spray and the auxiliary spray. Changing adduct-forming analogs by switching the auxiliary spray solution, or simply turning on and off the auxiliary spray facilitated the observation of the adducts corresponding to these analogs or lack thereof, giving rise to a simple and effective approach to probe adduct formation and thus help annotate the analyte ions.

Adduct annotation in liquid chromatography/high-resolution mass spectrometry to enhance compound identification

Annotation and interpretation of full scan electrospray mass spectra of metabolites is complicated by the presence of a wide variety of ions. Not only protonated, deprotonated, and neutral loss ions but also sodium, potassium, and ammonium adducts as well as oligomers are frequently observed. This diversity challenges automatic annotation and is often poorly addressed by current annotation tools. In many cases, annotation is integrated in metabolomics workflows and is based on specific chromatographic peak-picking tools. We introduce mzAdan, a nonchromatography-based multipurpose standalone application that was developed for the annotation and exploration of convolved high-resolution ESI-MS spectra. The tool annotates single or multiple accurate mass spectra using a customizable adduct annotation list and outputs a list of [M+H]⁺ candidates. MzAdan was first tested with a collection of 408 analytes acquired with flow injection analysis. This resulted in 402 correct [M+H]⁺ identifications and, with combinations of sodium, ammonium, and potassium adducts and water and ammonia losses within a tolerance of 10 mmu, explained close to 50% of the total ion current. False positives were monitored with mass accuracy and bias as well as chromatographic behavior which led to the identification of adducts with calcium instead of the expected potassium. MzAdan was then integrated in a workflow with XCMS for the untargeted LC-MS data analysis of a 52 metabolite standard mix and a human urine sample. The results were benchmarked against three other annotation tools, CAMERA, findMAIN, and CliqueMS: findMAIN and mzAdan consistently produced higher numbers of [M+H]⁺ candidates compared with CliqueMS and CAMERA, especially with co-eluting metabolites. Detection of low-intensity ions and correct grouping were found to be essential for annotation performance.

Identification of Plasma Glycosphingolipids as Potential Biomarkers for Prostate Cancer (PCa) Status

Prostate cancer (PCa) is the most common male cancer and the second leading cause of cancer death in United States men. Controversy continues over the effectiveness of prostate-specific antigen (PSA) for distinguishing aggressive from indolent PCa. There is a critical need for more specific and sensitive biomarkers to detect and distinguish low- versus high-risk PCa cases. Discovery metabolomics were performed utilizing ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS) on plasma samples from 159 men with treatment naïve prostate cancer participating in the North Carolina-Louisiana PCa Project to determine if there were metabolites associated with aggressive PCa. Thirty-five identifiable plasma small molecules were associated with PCa aggressiveness, 15 of which were sphingolipids; nine common molecules were present in both African-American and European-American men. The molecules most associated with PCa aggressiveness were glycosphingolipids; levels of trihexosylceramide and tetrahexosylceramide were most closely associated with high-aggressive PCa. The Cancer Genome Atlas was queried to determine gene alterations within glycosphingolipid metabolism that are associated with PCa and other cancers. Genes that encode enzymes associated with the metabolism of glycosphingolipids were altered in 12% of PCa and >30% of lung, uterine, and ovarian cancers. These data suggest that the identified plasma (glyco)sphingolipids should be further validated for their association with aggressive PCa, suggesting that specific sphingolipids may be included in a diagnostic signature for PCa.

Alteration of plasma metabolites associated with chemoradiosensitivity in esophageal squamous cell carcinoma via untargeted metabolomics approach

BACKGROUND

To investigate the differences in plasma metabolomic characteristics between pathological complete response (pCR) and non-pCR patients and identify biomarker candidates for predicting the response to neoadjuvant chemoradiotherapy (nCRT) in esophageal squamous cell carcinoma (ESCC).

METHODS

A total of 46 ESCC patients were included in this study. Gas chromatography time-of- flight mass spectrometry (GC-TOF/MS) technology was applied to detect the plasma samples collected before nCRT via untargeted metabolomics analysis.

RESULTS

Five differentially expressed metabolites (out of 109) was found in plasma between pCR and non-pCR groups. Compared with non-pCR group, isocitric acid (p = 0.0129), linoleic acid (p = 0.0137), citric acid (p = 0.0473) were upregulated, while L-histidine (p = 0.0155), 3'4 dihydroxyhydrocinnamic acid (p = 0.0339) were downregulated in the pCR plasma samples. Pathway analyses unveiled that citrate cycle (TCA cycle), glyoxylate and dicarboxylate metabolic pathway were associated with ESCC chemoradiosensitivity.

CONCLUSION

The present study provided supporting evidence that GC-TOF/MS based metabolomics approach allowed identification of metabolite differences between pCR and non-pCR patients in plasma levels, and the systemic metabolic status of patients may reflect the response of ESCC patient to neoadjuvant chemoradiotherapy.

Addressing the batch effect issue for LC/MS metabolomics data in data preprocessing

With the growth of metabolomics research, more and more studies are conducted on large numbers of samples. Due to technical limitations of the Liquid Chromatography–Mass Spectrometry (LC/MS) platform, samples often need to be processed in multiple batches. Across different batches, we often observe differences in data characteristics. In this work, we specifically focus on data generated in multiple batches on the same LC/MS machinery. Traditional preprocessing methods treat all samples as a single group. Such practice can result in errors in the alignment of peaks, which cannot be corrected by post hoc application of batch effect correction methods. In this work, we developed a new approach that address the batch effect issue in the preprocessing stage, resulting in better peak detection, alignment and quantification. It can be combined with down-stream batch effect correction methods to further correct for between-batch intensity differences. The method is implemented in the existing workflow of the apLCMS platform. Analyzing data with multiple batches, both generated from standardized quality control (QC) plasma samples and from real biological studies, the new method resulted in feature tables with better consistency, as well as better down-stream analysis results. The method can be a useful addition to the tools available for large studies involving multiple batches. The method is available as part of the apLCMS package. Download link and instructions are at https://mypage.cuhk.edu.cn/academics/yutianwei/apLCMS/.

CliqueMS: a computational tool for annotating in-source metabolite ions from LC-MS untargeted metabolomics data based on a coelution similarity network

Motivation

The analysis of biological samples in untargeted metabolomic studies using LC-MS yields tens of thousands of ion signals. Annotating these features is of the utmost importance for answering questions as fundamental as, e.g. how many metabolites are there in a given sample.

Results

Here, we introduce CliqueMS, a new algorithm for annotating in-source LC-MS1 data. CliqueMS is based on the similarity between coelution profiles and therefore, as opposed to most methods, allows for the annotation of a single spectrum. Furthermore, CliqueMS improves upon the state of the art in several dimensions: (i) it uses a more discriminatory feature similarity metric; (ii) it treats the similarities between features in a transparent way by means of a simple generative model; (iii) it uses a well-grounded maximum likelihood inference approach to group features; (iv) it uses empirical adduct frequencies to identify the parental mass and (v) it deals more flexibly with the identification of the parental mass by proposing and ranking alternative annotations. We validate our approach with simple mixtures of standards and with real complex biological samples. CliqueMS reduces the thousands of features typically obtained in complex samples to hundreds of metabolites, and it is able to correctly annotate more metabolites and adducts from a single spectrum than available tools.

Availability and implementation

https://CRAN.R-project.org/package=cliqueMS and https://github.com/osenan/cliqueMS.

Supplementary information

Supplementary data are available at Bioinformatics online.

In-Source CID Ramping and Covariant Ion Analysis of Hydrophilic Interaction Chromatography Metabolomics

A large proportion of the complexity and redundancy of LC-MS metabolomics data comes from adduct formation. To reduce such redundancy, many tools have been developed to recognize and annotate adduct ions. These tools rely on predefined adduct lists that are generated empirically from reversed-phase LC-MS studies. In addition, hydrophilic interaction chromatography (HILIC) is gaining popularity in metabolomics studies due to its enhanced performance over other methods for polar compounds. HILIC methods typically use high concentrations of buffer salts to improve chromatographic performance. Therefore, it is necessary to analyze adduct formation in HILIC metabolomics. To this end, we developed covariant ion analysis (COVINA) to investigate metabolite adduct formation. Using this tool, we completely annotated 201 adduct and fragment ions from 10 metabolites. Many of the metabolite adduct ions were found to contain cluster ions corresponding to mobile phase additives. We further utilized COVINA to find the major ionized forms of metabolites. Our results show that for some metabolites, the adduct ion signals can be >200-fold higher than the signals from the deprotonated form, offering better sensitivity for targeted metabolomics analysis. Finally, we developed an in-source CID ramping (InCIDR) method to analyze the intensity changes of the adduct and fragment ions from metabolites. Our analysis demonstrates a promising method to distinguish the protonated and deprotonated ions of metabolites from the adduct and fragment ions.

Metabolomic profile of diabetic retinopathy: a GC-TOFMS-based approach using vitreous and aqueous humor

AIM

To identify the potential metabolite markers in diabetic retinopathy (DR) by using gas chromatography coupled with time-of-flight mass spectrometry (GC-TOFMS).

METHODS

GC-TOFMS spectra were acquired from vitreous and aqueous humor (AH) samples of patients with DR and non-diabetic participants. Comparative analysis was used to elucidate the distinct metabolites of DR. Metabolic pathway was employed to explicate the metabolic reprogramming pathways involved in DR. Logistic regression and receiver-operating characteristic analyses were carried out to select and validate the biomarker metabolites and establish a therapeutic model.

RESULTS

Comparative analysis showed a clear separation between disease and control groups. Eight differentiating metabolites from AH and 15 differentiating metabolites from vitreous were highlighted. Out of these 23 metabolites, 11 novel metabolites have not been detected previously. Pathway analysis identified nine pathways (three in AH and six in vitreous) as the major disturbed pathways associated with DR. The abnormal of gluconeogenesis, ascorbate-aldarate metabolism, valine-leucine-isoleucine biosynthesis, and arginine-proline metabolism might weigh the most in the development of DR. The AUC of the logistic regression model established by D-2,3-Dihydroxypropanoic acid, isocitric acid, fructose 6-phosphate, and L-Lactic acid in AH was 0.965. The AUC established by pyroglutamic acid and pyruvic acid in vitreous was 0.951.

CONCLUSIONS

These findings have expanded our understanding of identified metabolites and revealed for the first time some novel metabolites in DR. These results may provide useful information to explore the mechanism and may eventually allow the development of metabolic biomarkers for prognosis and novel therapeutic strategies for the management of DR.

Metabolic compounds within the porcine uterine environment are unique to the type of conceptus present during the early stages of blastocyst elongation

The objective of this study was to identify metabolites within the porcine uterine milieu during the early stages of blastocyst elongation. At Days 9, 10, or 11 of gestation, reproductive tracts of White cross‐bred gilts (n = 38) were collected immediately following harvest and flushed with Roswell Park Memorial Institute‐1640 medium. Conceptus morphologies were assessed from each pregnancy and corresponding uterine flushings were assigned to one of five treatment groups based on these morphologies: (a) uniform spherical (n = 8); (b) heterogeneous spherical and ovoid (n = 8); (c) uniform ovoid (n = 8); (d) heterogeneous ovoid and tubular (n = 8); and (e) uniform tubular (n = 6). Uterine flushings from these pregnancies were submitted for nontargeted profiling by gas chromatography–mass spectrometry (GC–MS) and ultra performance liquid chromatography (UPLC)–MS techniques. Unsupervised multivariate principal component analysis (PCA) was performed using pcaMethods and univariate analysis of variance was performed in R with false discovery rate (FDR) adjustment. PCA analysis of the GC–MS and UPLC–MS data identified 153 and 104 metabolites, respectively. After FDR adjustment of the GC–MS and UPLC–MS data, 38 and 59 metabolites, respectively, differed (p < .05) in uterine flushings from pregnancies across the five conceptus stages. Some metabolites were greater (p  < .05) in abundance for uterine flushings containing earlier stage conceptuses (i.e., spherical), such as uric acid, tryptophan, and tyrosine. In contrast, some metabolites were greater (p < .05) in abundance for uterine flushings containing later stage conceptuses (i.e., tubular), such as creatinine, serine, and urea. These data illustrate several putative metabolites that change within the uterine milieu during early porcine blastocyst elongation.

Metabolomic Profile of Posner-Schlossman Syndrome: A Gas Chromatography Time-of-Flight Mass Spectrometry-Based Approach Using Aqueous Humor

The Posner-Schlossman syndrome (PSS) is a disease with clinically recurrent unilateral anterior uveitis with markedly elevated intraocular pressure (IOP) and subsequent progression to optic neuropathy. Retrospective studies have reported increased annual incidence of PSS, especially in China. While currently, the clinical management of PSS is still challenging. Metabolomics is considered to be a sensitive approach for the development of novel targeted therapeutics because of its direct elucidation of pathophysiological mechanisms. Therefore, we adopted gas chromatography time-of-flight mass spectrometry (GC-TOF-MS) technology-based non-targeted metabolomics approach to measure comprehensive metabolic profiles of aqueous humor (AH) samples obtained from patients with PSS, with an aim to demonstrate the underlying pathophysiology, identify potential biomarkers specific to PSS, and develop effective treatment strategies. A comparative analysis was used to indicate the distinct metabolites of PSS. Pathway analysis was conducted using MetaboAnalyst 4.0 to explore the metabolic reprogramming pathways involved in PSS. Logistic regression and receiver-operating characteristic (ROC) analyses were employed to evaluate the diagnostic capability of selected metabolites. Comparative analysis revealed a clear separation between PSS and control groups. Fourteen novel differentiating metabolites from AH samples obtained from patients with PSS were highlighted. Pathway analysis identified 11 carbohydrate, amino acid metabolism and energy metabolism pathways as the major disturbed pathways associated with PSS. The abnormal lysine degradation metabolism, valine-leucine-isoleucine biosynthesis, and citrate circle were considered to weigh the most in the development of PSS. The ROC analysis implied that the combination of glycine and homogentisic acid could serve as potential biomarkers for the discrimination of control and PSS groups. In conclusion, these results revealed for the first time the identity of important metabolites and pathways contributing to the development/progression of PSS, enabled the better understanding of the mechanism of PSS, and might lead to the development of metabolic biomarkers and novel therapeutic strategies to restrict the development/progression of PSS.

The metaRbolomics Toolbox in Bioconductor and beyond

Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub.

Resource heterogeneity structures aquatic bacterial communities

Microorganisms are strongly influenced by the bottom-up effects of resource supply. While many species respond to fluctuations in the concentration of resources, microbial diversity may also be affected by the heterogeneity of the resource pool, which often reflects a mixture of distinct molecules. To test this hypothesis, we examined resource-diversity relationships for bacterioplankton in a set of north temperate lakes that varied in their concentration and composition of dissolved organic matter (DOM), which is an important resource for heterotrophic bacteria. Using 16S rRNA transcript sequencing and ecosystem metabolomics, we documented strong relationships between bacterial alpha-diversity (richness and evenness) and the bulk concentration and the number of molecules in the DOM pool. Similarly, bacterial community beta-diversity was related to both DOM concentration and composition. However, in some lakes the relative abundance of resource generalists, which was inversely related to the DOM concentration, may have reduced the effect of DOM heterogeneity on community composition. Together, our results demonstrate the potential metabolic interactions between bacteria and organic matter and suggest that changes in organic matter composition may alter the structure and function of bacterial communities.

Non-invasive metastasis prognosis from plasma metabolites in stage II colorectal cancer patients: The DACHS study

Metastasis is the main cause of death from colorectal cancer (CRC). About 20% of stage II CRC patients develop metastasis during the course of disease. We performed metabolic profiling of plasma samples from non-metastasized and metachronously metastasized stage II CRC patients to assess the potential of plasma metabolites to serve as biomarkers for stratification of stage II CRC patients according to metastasis risk. We compared the metabolic profiles of plasma samples prospectively obtained prior to metastasis formation from non-metastasized vs. metachronously metastasized stage II CRC patients of the German population-based case-control multicenter DACHS study retrospectively. Plasma samples were analyzed from stage II CRC patients for whom follow-up data including the information on metachronous metastasis were available. To identify metabolites distinguishing non-metastasized from metachronously metastasized stage II CRC patients robust supervised classifications using decision trees and support vector machines were performed and verified by 10-fold cross-validation, by nested cross-validation and by traditional validation using training and test sets. We found that metabolic profiles distinguish non-metastasized from metachronously metastasized stage II CRC patients. Classification models from decision trees and support vector machines with 10-fold cross-validation gave average accuracy of 0.75 (sensitivity 0.79, specificity 0.7) and 0.82 (sensitivity 0.85, specificity 0.77), respectively, correctly predicting metachronous metastasis in stage II CRC patients. Taken together, plasma metabolic profiles distinguished non-metastasized and metachronously metastasized stage II CRC patients. The classification models consisting of few metabolites stratify non-invasively stage II CRC patients according to their risk for metachronous metastasis.

Nontargeted Identification of Tracer Incorporation in High-Resolution Mass Spectrometry

"Fluxomics" refers to the systematic analysis of metabolic fluxes in a biological system and may uncover novel dynamic properties of metabolism that remain undetected in conventional metabolomic approaches. In labeling experiments, tracer molecules are used to track changes in the isotopologue distribution of metabolites, which allows one to estimate fluxes in the metabolic network. Because unidentified compounds cannot be mapped on pathways, they are often neglected in labeling experiments. However, using recent developments in de novo annotation may allow to harvest the information present in these compounds if they can be identified. Here, we present a novel tool (HiResTEC) to detect tracer incorporation in high-resolution mass spectrometry data sets. The software automatically extracts a comprehensive, nonredundant list of all compounds showing more than 1% tracer incorporation in a nontargeted fashion. We explain and show in an example data set how mass precision and other filter heuristics, calculated on the raw data, can efficiently be used to reduce redundancy and noninformative signals by 95%. Ultimately, this allows to quickly investigate any labeling experiment for a complete set of labeled compounds (here 149) with acceptable false positive rates. We further re-evaluate a published data set from liquid chromatography-electrospray ionization (LC-ESI) to demonstrate broad applicability of our tool and emphasize importance of quality control (QC) tests. HiResTEC is provided as a package in the open source software framework R and is freely available on CRAN.

Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics

The annotation of small molecules remains a major challenge in untargeted mass spectrometry-based metabolomics. We here critically discuss structured elucidation approaches and software that are designed to help during the annotation of unknown compounds. Only by elucidating unknown metabolites first is it possible to biologically interpret complex systems, to map compounds to pathways and to create reliable predictive metabolic models for translational and clinical research. These strategies include the construction and quality of tandem mass spectral databases such as the coalition of MassBank repositories and investigations of MS/MS matching confidence. We present in silico fragmentation tools such as MS-FINDER, CFM-ID, MetFrag, ChemDistiller and CSI:FingerID that can annotate compounds from existing structure databases and that have been used in the CASMI (critical assessment of small molecule identification) contests. Furthermore, the use of retention time models from liquid chromatography and the utility of collision cross-section modelling from ion mobility experiments are covered. Workflows and published examples of successfully annotated unknown compounds are included.

Ion-neutral Clustering of Bile Acids in Electrospray Ionization Across UPLC Flow Regimes

Bile acid authentic standards were used as model compounds to quantitatively evaluate complex in-source phenomenon on a UPLC-ESI-TOF-MS operated in the negative mode. Three different diameter columns and a ceramic-based microfluidic separation device were utilized, allowing for detailed descriptions of bile acid behavior across a wide range of flow regimes and instantaneous concentrations. A custom processing algorithm based on correlation analysis was developed to group together all ion signals arising from a single compound; these grouped signals produce verified compound spectra for each bile acid at each on-column mass loading. Significant adduction was observed for all bile acids investigated under all flow regimes and across a wide range of bile acid concentrations. The distribution of bile acid containing clusters was found to depend on the specific bile acid species, solvent flow rate, and bile acid concentration. Relative abundancies of each cluster changed non-linearly with concentration. It was found that summing all MS level (low collisional energy) ions and ion-neutral adducts arising from a single compound improves linearity across the concentration range (0.125-5 ng on column) and increases the sensitivity of MS level quantification. The behavior of each cluster roughly follows simple equilibrium processes consistent with our understanding of electrospray ionization mechanisms and ion transport processes occurring in atmospheric pressure interfaces. Graphical Abstract ᅟ.

Investigation of metabolic profile of pimavanserin in rats by ultrahigh-performance liquid chromatography combined with Fourier transform ion cyclotron resonance mass spectrometry

RATIONALE

Pimavanserin, a selective serotonin 2A receptor inverse agonist, is a promising candidate for treating Parkinson's disease psychosis. Our previous study revealed that there might be the presence of extensive metabolites of pimavanserin in rats. However, the metabolic fate of pimavanserin in vivo remains unknown. Thus, it is essential to develop an efficient method to investigate the metabolic profile of pimavanserin in rats. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) to date has the highest mass measurement accuracy and resolution of any mass spectrometry platform.

METHODS

After a single intragastric administration of pimavanserin at a dose of 50 mg kg^-1 , plasma, bile, urine and feces were collected from rats. A novel and efficient strategy was developed to analyze the metabolic profile of pimavanserin in vivo based on ultrahigh-performance liquid chromatography (UHPLC) coupled with FT-ICR-MS.

RESULTS

A total of 23 metabolites were detected and tentatively identified through comparing their mass spectrometry profiles with those of pimavanserin. These metabolites were found in feces (22), bile (21), rat urine (16) and plasma (15). Results demonstrated that metabolic pathways of pimavanserin in rats included dehydrogenation, demethylation, deethylation, depropylation, debutylation, hydroxylation, dihydroxylation and trihydroxylation.

CONCLUSIONS

A total of 22 phase I metabolites of pimavanserin were detected and tentatively identified. This report presents the first study of screening and identification of the metabolites of pimavanserin. The UHPLC/FT-ICR-MS method is a powerful tool for exploring and identifying metabolites in complex biological samples.

Stacked Injections of Biphasic Extractions for Improved Metabolomic Coverage and Sample Throughput

Omics technologies attempt to provide comprehensive coverage of their target analytes. Comprehensive coverage of metabolites, the aim of nontargeted metabolomics applications, is hindered by the extreme diversity in physiochemical properties of the metabolome. One approach to deal with this challenge is the use of biphasic extractions. These methods generate two largely complementary extracts from a single sample, with an organic lipid-rich fraction and an aqueous fraction containing largely primary and secondary metabolites. To improve metabolite coverage, these two fractions are then independently analyzed resulting in a doubling of the experimental time. In this manuscript, we describe a novel injection approach, stacked injections of a biphasic extraction (SIBE), which enables simultaneous analysis of the two fractions. We demonstrate that SIBE offers nearly 3-fold more total peak area than a monophasic extract without dramatically increasing instrumentation time required for the analysis. The analytical variance is very slightly increased; however, significant improvements in retention time stability are obtained with SIBE vs monophasic injections. Collectively, these data indicate that SIBE is a viable injection approach whenever comprehensive metabolomic coverage is desired.