Mass spectrometry-based metabolomics

Associations of the Lipidome with Ageing, Cognitive Decline and Exercise Behaviours

One of the most recognisable features of ageing is a decline in brain health and cognitive dysfunction, which is associated with perturbations to regular lipid homeostasis. Although ageing is the largest risk factor for several neurodegenerative diseases such as dementia, a loss in cognitive function is commonly observed in adults over the age of 65. Despite the prevalence of normal age-related cognitive decline, there is a lack of effective methods to improve the health of the ageing brain. In light of this, exercise has shown promise for positively influencing neurocognitive health and associated lipid profiles. This review summarises age-related changes in several lipid classes that are found in the brain, including fatty acyls, glycerolipids, phospholipids, sphingolipids and sterols, and explores the consequences of age-associated pathological cognitive decline on these lipid classes. Evidence of the positive effects of exercise on the affected lipid profiles are also discussed to highlight the potential for exercise to be used therapeutically to mitigate age-related changes to lipid metabolism and prevent cognitive decline in later life.

The emerging roles of next-generation metabolomics in critical care nutrition

Critical illness leads to millions of deaths worldwide each year, with a significant surge due to the COVID-19 pandemic. Patients with critical illness are frequently associated with systemic metabolic disorders and malnutrition. The idea of intervention for critically ill patients through enteral and parenteral nutrition has been paid more and more attention gradually. However, current nutritional therapies focus on evidence-based practice, and there have been lacking holistic approaches for nutritional support assessment. Metabolomics is a well-established omics technique in system biology that enables comprehensive profiling of metabolites in a biological system and thus provides the underlying information expressed and modulated by all other omics layers. In recent years, with the development of high-resolution and accurate mass spectrometry, metabolomics entered a new "generation", promoting its broader applications in critical care nutrition. In this review, we first described the technological development and milestones of next-generation metabolomics in the past 20 years. We then discussed the emerging roles of next-generation metabolomics in advancing our understanding of critical care nutrition, such as nutritional deficiency risk evaluation, metabolic mechanisms of nutritional therapies, and novel nutrition target identification.

Central carbon metabolism remodeling as a mechanism to develop drug tolerance and drug resistance in Mycobacterium tuberculosis

Suboptimal efficacy of the current antibiotic regimens and frequent emergence of antibiotic-resistant Mycobacterium tuberculosis (Mtb), an etiological agent of tuberculosis (TB), render TB the world’s deadliest infectious disease before the COVID-19 outbreak. Our outdated TB treatment method is designed to eradicate actively replicating populations of Mtb. Unfortunately, accumulating evidence suggests that a small population of Mtb can survive antimycobacterial pressure of antibiotics by entering a “persister” state (slowly replicating or non-replicating and lacking a stably heritable antibiotic resistance, termed drug tolerance). The formation of drug-tolerant Mtb persisters is associated with TB treatment failure and is thought to be an adaptive strategy for eventual development of permanent genetic mutation-mediated drug resistance. Thus, the molecular mechanisms behind persister formation and drug tolerance acquisition are a source of new antibiotic targets to eradicate both Mtb persisters and drug-resistant Mtb. As Mtb persisters are genetically identical to antibiotic susceptible populations, metabolomics has emerged as a vital biochemical tool to differentiate these populations by determining phenotypic shifts and metabolic reprogramming. Metabolomics, which provides detailed insights into the molecular basis of drug tolerance and resistance in Mtb, has unique advantages over other techniques by its ability to identify specific metabolic differences between the two genetically identical populations. This review summarizes the recent advances in our understanding of the metabolic adaptations used by Mtb persisters to achieve intrinsic drug tolerance and facilitate the emergence of drug resistance. These findings present metabolomics as a powerful tool to identify previously unexplored antibiotic targets and improved combinations of drug regimens against drug-resistant TB infection.

Isotope Ratio Outlier Analysis (IROA) for HPLC-TOFMS-Based Metabolomics of Human Urine

Metabolic fingerprinting by mass spectrometry aims at the comprehensive, semiquantitative analysis of metabolites. Isotope dilution, if successfully implemented, may provide a more reliable, relative quantification. Therefore, the 13C labeled yeast extract of the IROA TruQuant kit was added as an internal standard (IS) to human urine samples measured in full-scan mode on a high-performance liquid chromatography-time-of-flight mass spectrometer (HPLC-TOFMS) system. The isotope ratio approach enabled the analysis of 112 metabolites. The correlation with reference data did not improve significantly using 12C/13C ratios compared to absolute 12C peak areas. Moreover, using an intricate 13C-labeled standard increased the complexity of the mass spectra, which made correct signal annotation more challenging. On the positive side, the ratio approach helps to reduce batch effects, but it does not perform better than computational methods such as the "removebatcheffect" function in the R package Limma.

Metabolomics-Guided Comparison of Pollen and Microalgae-Based Artificial Diets in Honey Bees

Managed honey bee colonies used for crop pollination are fed artificial diets to offset nutritional deficiencies related to land-use intensification and climate change. In this study, we formulated novel microalgae diets using Chlorella vulgaris and Arthrospira platensis (spirulina) biomass and fed them to young adult honey bee workers. Diet-induced changes in bee metabolite profiles were studied relative to a natural pollen diet using liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS) metabolomics. Untargeted analyses of pollen- and microalgae-fed bees revealed significant overlap, with 248 shared features determined by LC-MS and 87 shared features determined by GC-MS. Further metabolomic commonalities were evident upon subtraction of unique diet features. Twenty-five identified metabolites were influenced by diet, which included complex lipids, essential fatty acids, vitamins, and phytochemicals. The metabolomics results are useful to understand mechanisms underlying favorable growth performance as well as increased antioxidant and heat shock protein gene expression in bees fed the microalgae diets. We conclude that the tested microalgae have potential as sustainable feed additives and as a source of bee health-modulating natural products. Metabolomics-guided diet development could eventually help tailor feed interventions to achieve precision nutrition in honey bees and other livestock animals.

Comprehensive metabolomics analysis of key taste components in different varieties of table grapes

Grapes are one of the world's largest fruit crops, which are rich in nutrients and taste. Summer Black, Gui Fei, Kyoho Grape, Giant Rose, Shine Muscat, and Rosario Bianco are the six most popular table grapes in Wuxi city, Jiangsu province. Owing to the lack of comprehensive investigations of metabolites in table grapes, the metabolic causes of differences in their taste are unknown. In this study, metabolites of six table grapes were profiled using ultra-high-performance liquid chromatography-Q-Exactive Orbitrap tandem mass spectrometry combined with a multivariate analysis. Orthogonal partial least squares discriminant analysis clearly discriminated among the metabolites of these varieties. Metabolic pathway analysis revealed that carbohydrate and amino acid metabolisms was highly conserved among these varieties. Our results suggest that the taste differences in the six table grape varieties can be explained by variations in composition and abundance of carbohydrates, organic acids, amino acids, and polyphenols. This study provides comprehensive insights into the underlying metabolic causes of taste variation in table grapes. This article is protected by copyright. All rights reserved.

GC-TOF/MS-Based Metabolomics for Comparison of Volar and Non-Volar Skin Types

Skin has heterogenous identities on different body sites despite similar cellular compositions. There are two types of skin, volar (palmoplantar) and non-volar (dorsal), which are characterized by epidermal thickness, pigmentation, and presence of hair follicles. However, the mechanisms underlying the development of these different skin types remain unclear. To investigate these, we profiled the cellular metabolites of volar and non-volar skin in mice using gas chromatography-time-of-flight/mass spectrometry (GC-TOF/MS), and further assessed the metabolic differences between them. In total, 96 metabolites from both volar and non-volar skin of mice were identified using the BinBase database system. Metabolomics analysis revealed important differences associated with amino acid metabolism (phenylalanine, tyrosine, and tryptophan biosynthesis; aspartate and glutamate metabolism), sugar metabolism (pentose phosphate pathway), and nucleotide metabolism (pyrimidine metabolism) in volar skin. Fifty metabolites were identified as potential biomarkers differentiating the physiological characteristics of these skin types. Of these, nine were highly increased whereas 41 were significantly decreased in volar skin compared with those in non-volar skin. Overall, these results provide valuable information for understanding the metabolic differences between volar and non-volar skin.

Combined multi-omics analysis reveals oil mist particulate matter-induced lung injury in rats: Pathological damage, proteomics, metabolic disturbances, and lung dysbiosis

Oil mist particulate matter (OMPM) causes acute and chronic diseases and exacerbations. Owing to the characteristics of poor ventilation, high oil mist concentration, and a relatively closed working environment, the existence of OMPM in the cabin is inevitable, and its impact on the health of occupations on ships cannot be ignored. However, compared with several studies that summarized the health effects of OMPM from traditional sources, few studies have focused on the occupational exposure risk of OMPM from oil pollution sources in ships. In this study, we collected OMPM from oil pollution in cabins and assessed the exposure to OMPM from oil pollution and the corresponding health risks through acute exposure experiments in rats. OMPM exposure induces protein regulation in the extracellular matrix and immune responses, leading to severe inflammatory responses. The abundance and composition of the lung microbial community changed significantly. It interferes with the lung metabolite levels. However, more research is needed to fully understand the extent of health risks associated with OMPM exposure. Further research on vulnerable groups exposed to OMPM from ships is needed to inform public health interventions.

Recent advances in proteomics and metabolomics in plants

Over the past decade, systems biology and plant-omics have increasingly become the main stream in plant biology research. New developments in mass spectrometry and bioinformatics tools, and methodological schema to integrate multi-omics data have leveraged recent advances in proteomics and metabolomics. These progresses are driving a rapid evolution in the field of plant research, greatly facilitating our understanding of the mechanistic aspects of plant metabolisms and the interactions of plants with their external environment. Here, we review the recent progresses in MS-based proteomics and metabolomics tools and workflows with a special focus on their applications to plant biology research using several case studies related to mechanistic understanding of stress response, gene/protein function characterization, metabolic and signaling pathways exploration, and natural product discovery. We also present a projection concerning future perspectives in MS-based proteomics and metabolomics development including their applications to and challenges for system biology. This review is intended to provide readers with an overview of how advanced MS technology, and integrated application of proteomics and metabolomics can be used to advance plant system biology research.

Cell Metabolomics Study on Synergistic anti-Hepatocellular Carcinoma Effect of Aidi Injection Combined with Doxorubicin

Hepatocellular carcinoma (HCC) is the sixth most common cancer worldwide and the second most common cause of cancer deaths. This study aimed to explore the inhibitory effect and mechanism of Aidi injection (ADI) combined with doxorubicin (DOX) in HCC treatment. The drug concentrations in combined threapy was determined by investigating the effect of various concentrations of ADI and DOX on the viability of H22 cells. The combination index (CI) was also calculated. A metabolomic strategy based on ultrahigh performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) platform was established to analyze the metabolites. As a result, the CI values were less than 1, indicating that the combination of ADI and DOX exerted a synergistic effect on HCC treatment. The combination of 40‰ ADI and 1 μmol/L DOX had the strongest inhibitory effect and was used for subsequent metabolomic analysis. A total of 19 metabolic markers were obtained in metabolomic analysis, including amino acids (L-glutamic acid, L-arginine, and L-tyrosine), organic acids (succinic acid and citric acid), adenosine, and hypoxanthine , etc. Compared with the treatment using DOX or ADI alone, the combined therapy further regulated the levels of metabolic markers in HCC, which may be the reason for the synergistic effect. Seven metabolic pathways were significantly enriched, including phenylalanine, tyrosine and tryptophan biosynthesis, D-glutamine and D-glutamate metabolism, alanine, aspartate and glutamate metabolism, phenylalanine metabolism, arginine biosynthesis, tricarboxylic acid (TCA) cycle, and purine metabolism. These findings demonstrated that ADI combined with DOX can effectively inhibit the viability of H22 cells, which may exert a synergistic anti-tumor effect by balancing the metabolism of amino acids and energy-related substances.

Alleviation of liver cirrhosis and associated portal-hypertension by Astragalus species in relation to their UPLC-MS/MS metabolic profiles: a mechanistic study

Liver cirrhosis is a late-stage liver disease characterized by excessive fibrous deposition triggering portal-hypertension (PH); the prime restrainer for cirrhosis-related complications. Remedies that can dually oppose hepatic fibrosis and lower PH, may prevent progression into decompensated-cirrhosis. Different Astragalus-species members have shown antifibrotic and diuretic actions with possible subsequent PH reduction. However, A.spinosus and A.trigonus were poorly tested for eliciting these actions. Herein, A.spinosus and A.trigonus roots and aerial parts extracts were subjected to comprehensive metabolic-fingerprinting using UHPLC-MS/MS resulting in 56 identified phytoconstituents, followed by chemometric untargeted analysis that revealed variable metabolic profiles exemplified by different species and organ types. Consequently, tested extracts were in-vivo evaluated for potential antifibrotic/anticirrhotic activity by assessing specific markers. The mechanistic prospective to induce diuresis was investigated by analyzing plasma aldosterone and renal-transporters gene-expression. Serum apelin and dimethylarginine-dimethylaminohydrolase-1 were measured to indicate the overall effect on PH. All extracts amended cirrhosis and PH to varying extents and induced diuresis via different mechanisms. Further, An OPLS model was built to generate a comprehensive metabolic-profiling of A.spinosus and A.trigonus secondary-metabolites providing a chemical-based evidence for their efficacious consistency. In conclusion, A.spinosus and A.trigonus organs comprised myriad pharmacologically-active constituents that act synergistically to ameliorate cirrhosis and associated PH.

Coral-like Magnetic Particles for Chemoselective Extraction of Anionic Metabolites

To date, advanced chemical biology tools for chemoselective extraction of metabolites are limited. In this study, unique coral-like polymer particles were synthesized via high concentrations of 1-ethyl-3-(3-(dimethylamino)propyl) carbodiimide hydrochloride (EDC)/N-hydroxysuccinimide (NHS), which are usually used as condensation agents. The polymers can wrap or adhere Fe3O4 nanoparticles (Fe3O4-NPs) to form polymer magnetic microparticles (PMMPs). With abundant NHS-activated moieties on their surface, the coral-like PMMPs could be modified by cystamine for the chemoselective extraction of phosphate/carboxylate anion metabolites from complex biological samples. Finally, 97 metabolites including nucleotides, phosphates, phosphate sugars, carboxylate sugars, and organic acids were extracted and identified from serum, tissues, and cells. These metabolites are involved in four major metabolic pathways including glycolysis, the tricarboxylic acid cycle, the pentose phosphate pathway, and nucleotide metabolism. This study has provided a cost-effective and easy-to-implement preparation of PMMPs with a robust chemoselective extraction ability and versatile applications.

Marine Bacteria and Omic Approaches: A Novel and Potential Repository for Bioremediation Assessment

Marine environments accommodating diverse assortments of life constitute a great pool of differentiated natural resources. The cumulative need to remedy unpropitious effects of anthropogenic activities on estuaries, and coastal marine ecosystems has propelled the development of effective bioremediation strategies. Marine bacteria producing biosurfactants are promising agents for bio-remediating oil pollution in marine environments, making them prospective candidates for enhancing oil recovery. Molecular omics technologies are considered an emerging field of research in ecological and diversity assessment owing to their utility in environmental surveillance and bioremediation of polluted sites. A thorough literature review was undertaken to understand the applicability of different omic techniques employed for bioremediation assessment using marine bacteria. This review further establishes that for bioremediation of environmental pollutants (i.e., heavy metals, hydrocarbons, xenobiotic and numerous recalcitrant compounds), organisms isolated from marine environments can be better utilized for their removal. The literature survey shows that omics approaches can provide exemplary knowledge about microbial communities and their role in the bioremediation of environmental pollutants. This review centres on applications of marine bacteria in enhanced bioremediation, utilizing the omics approaches that can be a vital biological contrivance in environmental monitoring to tackle environmental degradation. The paper aims to identify the gaps in investigations involving marine bacteria to help researchers, ecologists, and decision-makers to develop a holistic understanding regarding their utility in bioremediation assessment.

Metabolomics: A New Approach in the Evaluation of Effects in Human Beings and Wildlife Associated with Environmental Exposition to POPs

Human beings and wild organisms are exposed daily to a broad range of environmental stressors. Among them are the persistent organic pollutants that can trigger adverse effects on these organisms due to their toxicity properties. There is evidence that metabolomics can be used to identify biomarkers of effect by altering the profiles of endogenous metabolites in biological fluids or tissues. This approach is relatively new and has been used in vitro studies mainly. Therefore, this review addresses those that have used metabolomics as a key tool to identify metabolites associated with environmental exposure to POPs in wildlife and human populations and that can be used as biomarkers of effect. The published results suggest that the metabolic pathways that produce energy, fatty acids, and amino acids are commonly affected by POPs. Furthermore, these pathways can be promoters of additional effects. In the future, metabolomics combined with other omics will improve understanding of the origin, development, and progression of the effects caused by environmental exposure.

MetHoS: a platform for large-scale processing, storage and analysis of metabolomics data

BACKGROUND

Modern mass spectrometry has revolutionized the detection and analysis of metabolites but likewise, let the data skyrocket with repositories for metabolomics data filling up with thousands of datasets. While there are many software tools for the analysis of individual experiments with a few to dozens of chromatograms, we see a demand for a contemporary software solution capable of processing and analyzing hundreds or even thousands of experiments in an integrative manner with standardized workflows.

RESULTS

Here, we introduce MetHoS as an automated web-based software platform for the processing, storage and analysis of great amounts of mass spectrometry-based metabolomics data sets originating from different metabolomics studies. MetHoS is based on Big Data frameworks to enable parallel processing, distributed storage and distributed analysis of even larger data sets across clusters of computers in a highly scalable manner. It has been designed to allow the processing and analysis of any amount of experiments and samples in an integrative manner. In order to demonstrate the capabilities of MetHoS, thousands of experiments were downloaded from the MetaboLights database and used to perform a large-scale processing, storage and statistical analysis in a proof-of-concept study.

CONCLUSIONS

MetHoS is suitable for large-scale processing, storage and analysis of metabolomics data aiming at untargeted metabolomic analyses. It is freely available at: https://methos.cebitec.uni-bielefeld.de/ . Users interested in analyzing their own data are encouraged to apply for an account.

Integrated Metabolomic, Molecular Networking, and Genome Mining Analyses Uncover Novel Angucyclines From Streptomyces sp. RO-S4 Strain Isolated From Bejaia Bay, Algeria

Multi-omic approaches have recently made big strides toward the effective exploration of microorganisms, accelerating the discovery of new bioactive compounds. We combined metabolomic, molecular networking, and genomic-based approaches to investigate the metabolic potential of the Streptomyces sp. RO-S4 strain isolated from the polluted waters of Bejaia Bay in Algeria. Antagonistic assays against methicillin-resistant Staphylococcus aureus with RO-S4 organic extracts showed an inhibition zone of 20 mm by using the agar diffusion method, and its minimum inhibitory concentration was 16 μg/ml. A molecular network was created using GNPS and annotated through the comparison of MS/MS spectra against several databases. The predominant compounds in the RO-S4 extract belonged to the angucycline family. Three compounds were annotated as known metabolites, while all the others were putatively new to Science. Notably, all compounds had fridamycin-like aglycones, and several of them had a lactonized D ring analogous to that of urdamycin L. The whole genome of Streptomyces RO-S4 was sequenced to identify the biosynthetic gene cluster (BGC) linked to these angucyclines, which yielded a draft genome of 7,497,846 bp with 72.4% G+C content. Subsequently, a genome mining analysis revealed 19 putative biosynthetic gene clusters, including a grincamycin-like BGC with high similarity to that of Streptomyces sp. CZN-748, that was previously reported to also produce mostly open fridamycin-like aglycones. As the ring-opening process leading to these compounds is still not defined, we performed a comparative analysis with other angucycline BGCs and advanced some hypotheses to explain the ring-opening and lactonization, possibly linked to the uncoupling between the activity of GcnE and GcnM homologs in the RO-S4 strain. The combination of metabolomic and genomic approaches greatly improved the interpretation of the metabolic potential of the RO-S4 strain.

Metabolomics of bronchoalveolar lavage in children with persistent wheezing

Background

Recent studies have demonstrated the important role of metabolomics in the pathogenesis of asthma. However, the role of lung metabolomics in childhood persistent wheezing (PW) or wheezing recurrence remains poorly understood.

Methods

In this prospective observational study, we performed a liquid chromatography/mass spectrometry-based metabolomic survey on bronchoalveolar lavage samples collected from 30 children with PW and 30 age-matched infants (control group). A 2-year follow-up study on these PW children was conducted.

Results

Children with PW showed a distinct characterization of respiratory metabolome compared with control group. Children with PW had higher abundances of choline, oleamide, nepetalactam, butyrylcarnitine, l-palmitoylcarnitine, palmitoylethanolamide, and various phosphatidylcholines. The glycerophospholipid metabolism pathway was the most relevant pathway involving in PW pathophysiologic process. Additionally, different gender, prematurity, and systemic corticoids use demonstrated a greater impact in airway metabolite compositions. Furthermore, for PW children with recurrence during the follow-up period, children who were born prematurely had an increased abundance of butyrylcarnitine relative to those who were carried to term.

Conclusions

This study suggests that the alterations of lung metabolites could be associated with the development of wheezing, and this early alteration could also be correlated with wheezing recurrence later in life.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-022-02087-6.

A general procedure for rounding m/z values in low-resolution mass spectra

It was revealed that nominal mass spectra extracted from the same NetCDF file using different gas chromatography/mass spectrometry (GC/MS) software products are not identical. This phenomenon is caused by differences in algorithms used for rounding floating-point m/z values to integers. It was found that all programs under consideration (AMDIS, ChemStation, ChromaTOF, MS Search, OpenChrom) use different procedures. It is necessary to know how fractional parts of accurate m/z values of ions are distributed to determine which algorithm yields more robust results. We estimated the respective distribution using two databases (PubChem and NIST). As a result, we came up with a procedure that minimizes the influence of random errors on rounding to integer m/z values. The procedure we suggest is to sum intensities of all floating-point m/z values in a bin [MZ - 0.38; MZ + 0.62] and assign MZ as a nominal m/z value, where MZ is an integer m/z value.

Precision Medicine: An Optimal Approach to Patient Care in Renal Cell Carcinoma

Renal cell cancer (RCC) is a heterogeneous tumor that shows both intra- and inter-heterogeneity. Heterogeneity is displayed not only in different patients but also among RCC cells in the same tumor, which makes treatment difficult because of varying degrees of responses generated in RCC heterogeneous tumor cells even with targeted treatment. In that context, precision medicine (PM), in terms of individualized treatment catered for a specific patient or groups of patients, can shift the paradigm of treatment in the clinical management of RCC. Recent progress in the biochemical, molecular, and histological characteristics of RCC has thrown light on many deregulated pathways involved in the pathogenesis of RCC. As PM-based therapies are rapidly evolving and few are already in current clinical practice in oncology, one can expect that PM will expand its way toward the robust treatment of patients with RCC. This article provides a comprehensive background on recent strategies and breakthroughs of PM in oncology and provides an overview of the potential applicability of PM in RCC. The article also highlights the drawbacks of PM and provides a holistic approach that goes beyond the involvement of clinicians and encompasses appropriate legislative and administrative care imparted by the healthcare system and insurance providers. It is anticipated that combined efforts from all sectors involved will make PM accessible to RCC and other patients with cancer, making a tremendous positive leap on individualized treatment strategies. This will subsequently enhance the quality of life of patients.

Chemical profiling and unraveling of anti-COVID-19 biomarkers of red sage (Lantana camara L.) cultivars using UPLC-MS/MS coupled to chemometric analysis, in vitro study and molecular docking

ETHNOPHARMACOLOGICAL RELEVANCE

Red sage (Lantana camara L.) (Verbenaceae) is a widely spread plant that was traditionally used in Brazil, India, Kenya, Thailand, Mexico, Nigeria, Australia and Southeast Asia for treating several ailments including rheumatism and leprosy. Despite its historical role in relieving respiratory diseases, limited studies progressed to the plant's probable inhibition to respiratory viruses especially after the striking spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections.

AIM OF THE STUDY

This study aimed to investigate the inhibitory activity of different L. camara cultivars to SARS-CoV-2, that was not previously inspected, and clarify their mechanisms of action in the metabolomics viewpoint, and to determine the biomarkers that are related to such activity using UPLC-MS/MS coupled to in vitro-studies and chemometric analysis.

MATERIALS AND METHODS

Chemical profiling of different cultivars was accomplished via UPLC-MS/MS. Principle component analysis (PCA) and orthogonal projection to latent structures (OPLS) models were built using SIMCA® (multivariate data analysis software). Cytotoxicity and COVID-19 inhibitory activity testing were done followed by TaqMan Real-time RT-PCR (Reverse transcription polymerase chain reaction) assay that aimed to study extracts' effects on RNA-dependent RNA polymerase (RdRp) and E-genes expression levels. Detected biomarkers from OPLS analysis were docked into potential targets pockets to investigate their possible interaction patterns using Schrodinger® suite.

RESULTS

UPLC-MS/MS analysis of different cultivars yielded 47 metabolites, most of them are triterpenoids and flavonoids. PCA plots revealed that inter-cultivar factor has no pronounced effect on the chemical profiles of extracts except for L. camara, cultivar Drap d'or flowers and leaves extracts as well as for L. camara cv Chelsea gem leaves extract. Among the tested extracts, flowers and leaves extracts of L. camara cv Chelsea gem, flowers extracts of L. camara cv Spreading sunset and L. camara cv Drap d'or showed the highest selectivity indices scoring 12.3, 10.1, 8.6 and 7.8, respectively, indicating their relative high safety and efficacy. Leaves and flowers extracts of L. camara cv Chelsea gem, flowers extracts of L. camara cv Spreading sunset and L. camara cv Drap d'or were the most promising inhibitors to viral plaques exhibiting IC₅₀ values of 3.18, 3.67, 4.18 and 5.01 μg/mL, respectively. This was incremented by OPLS analysis that related their promising COVID-19 inhibitory activities to the presence of twelve biomarkers. Inhibiting the expression of RdRp gene is the major mechanism behind the antiviral activity of most extracts at almost all concentration levels. Molecular docking of the active biomarkers against RdRp revealed that isoverbascoside, luteolin-7,4'-O-diglucoside, camarolic acid and lantoic acid exhibited higher docking scores of -11.378, -10.64, -6.72 and -6.07 kcal/mol, respectively, when compared to remdesivir (-5.75 kcal/mol), thus these four compounds can serve as promising anti-COVID-19 candidates.

CONCLUSION

Flowers and leaves extracts of four L. camara cultivars were recognized as rich sources of phytoconstituents possessing anti-COVID-19 activity. Combination of UPLC-MS/MS and chemometrics is a promising approach to detect chemical composition differences among the cultivars and correlate them to COVID-19 inhibitory activities allowing to pinpoint possible biomarkers. Further in-vitro and in-vivo studies are required to verify their activity.

Targeted Microchip Capillary Electrophoresis-Orbitrap Mass Spectrometry Metabolomics to Monitor Ovarian Cancer Progression

The lack of effective screening strategies for high-grade serous carcinoma (HGSC), a subtype of ovarian cancer (OC) responsible for 70–80% of OC related deaths, emphasizes the need for new diagnostic markers and a better understanding of disease pathogenesis. Capillary electrophoresis (CE) coupled with high-resolution mass spectrometry (HRMS) offers high selectivity and sensitivity for ionic compounds, thereby enhancing biomarker discovery. Recent advances in CE-MS include small, chip-based CE systems coupled with nanoelectrospray ionization (nanoESI) to provide rapid, high-resolution analysis of biological specimens. Here, we describe the development of a targeted microchip (µ) CE-HRMS method, with an acquisition time of only 3 min and sample injection volume of 4nL, to analyze 40 target metabolites in serum samples from a triple-mutant (TKO) mouse model of HGSC. Extracted ion electropherograms showed sharp, baseline resolved peak shapes, even for structural isomers such as leucine and isoleucine. All calibration curves of the analytes maintained good linearity with an average R² of 0.994, while detection limits were in the nM range. Thirty metabolites were detected in mouse serum with recoveries ranging from 78 to 120%, indicating minimal ionization suppression and good accuracy. We applied the µCE-HRMS method to biweekly-collected serum samples from TKO and TKO control mice. A time-resolved analysis revealed characteristic temporal trends for amino acids, nucleosides, and amino acid derivatives. These metabolic alterations are indicative of altered nucleotide biosynthesis and amino acid metabolism in HGSC development and progression. A comparison of the µCE-HRMS dataset with non-targeted ultra-high performance liquid chromatography (UHPLC)–MS results showed identical temporal trends for the five metabolites detected with both platforms, indicating the µCE-HRMS method performed satisfactorily in terms of capturing metabolic reprogramming due to HGSC progression while reducing the total data collection time three-fold.

Metabolomic Analysis of Lactobacillus acidophilus, L. gasseri, L. crispatus, and Lacticaseibacillus rhamnosus Strains in the Presence of Pomegranate Extract

Lactobacillus species are prominent inhabitants of the human gastrointestinal tract that contribute to maintaining a balanced microbial environment that positively influences host health. These bacterial populations can be altered through use of probiotic supplements or via dietary changes which in turn affect the host health. Utilizing polyphenolic compounds to selectively stimulate the growth of commensal bacteria can have a positive effect on the host through the production of numerous metabolites that are biologically active. Four Lactobacillus strains were grown in the presence of pomegranate (POM) extract. Two strains, namely, L. acidophilus NCFM and L. rhamnosus GG, are commonly used probiotics, while the other two strains, namely, L. crispatus NCK1351 and L. gasseri NCK1342, exhibit probiotic potential. To compare and contrast the impact of POM on the strains' metabolic capacity, we investigated the growth of the strains with and without the presence of POM and identified their carbohydrate utilization and enzyme activity profiles. To further investigate the differences between strains, an untargeted metabolomic approach was utilized to quantitatively and qualitatively define the metabolite profiles of these strains. Several metabolites were produced significantly and/or exclusively in some of the strains, including mevalonate, glutamine, 5-aminoimidazole-4-carboxamide, phenyllactate, and fumarate. The production of numerous discrete compounds illustrates the unique characteristics of and diversity between strains. Unraveling these differences is essential to understand the probiotic function and help inform strain selection for commercial product formulation.

Scan-Centric, Frequency-Based Method for Characterizing Peaks from Direct Injection Fourier Transform Mass Spectrometry Experiments

We present a novel, scan-centric method for characterizing peaks from direct injection multi-scan Fourier transform mass spectra of complex samples that utilizes frequency values derived directly from the spacing of raw m/z points in spectral scans. Our peak characterization method utilizes intensity-independent noise removal and normalization of scan-level data to provide a much better fit of relative intensity to natural abundance probabilities for low abundance isotopologues that are not present in all of the acquired scans. Moreover, our method calculates both peak- and scan-specific statistics incorporated within a series of quality control steps that are designed to robustly derive peak centers, intensities, and intensity ratios with their scan-level variances. These cross-scan characterized peaks are suitable for use in our previously published peak assignment methodology, Small Molecule Isotope Resolved Formula Enumeration (SMIRFE).

Reprogramming of Plant Central Metabolism in Response to Abiotic Stresses: A Metabolomics View

Abiotic stresses rewire plant central metabolism to maintain metabolic and energy homeostasis. Metabolites involved in the plant central metabolic network serve as a hub for regulating carbon and energy metabolism under various stress conditions. In this review, we introduce recent metabolomics techniques used to investigate the dynamics of metabolic responses to abiotic stresses and analyze the trend of publications in this field. We provide an updated overview of the changing patterns in central metabolic pathways related to the metabolic responses to common stresses, including flooding, drought, cold, heat, and salinity. We extensively review the common and unique metabolic changes in central metabolism in response to major abiotic stresses. Finally, we discuss the challenges and some emerging insights in the future application of metabolomics to study plant responses to abiotic stresses.

Mass Spectrometry-Based Metabolomics of Phytocannabinoids from Non-Cannabis Plant Origins

Phytocannabinoids are isoprenylated resorcinyl polyketides produced mostly in glandular trichomes of Cannabis sativa L. These discoveries led to the identification of cannabinoid receptors, which modulate psychotropic and pharmacological reactions and are found primarily in the human central nervous system. As a result of the biogenetic process, aliphatic ketide phytocannabinoids are exclusively found in the cannabis species and have a limited natural distribution, whereas phenethyl-type phytocannabinoids are present in higher plants, liverworts, and fungi. The development of cannabinomics has uncovered evidence of new sources containing various phytocannabinoid derivatives. Phytocannabinoids have been isolated as artifacts from their carboxylated forms (pre-cannabinoids or acidic cannabinoids) from plant sources. In this review, the overview of the phytocannabinoid biosynthesis is presented. Different non-cannabis plant sources are described either from those belonging to the angiosperm species and bryophytes, together with their metabolomic structures. Lastly, we discuss the legal framework for the ingestion of these biological materials which currently receive the attention as a legal high.

A Targeted Metabolomics Approach to Study Secondary Metabolites and Antioxidant Activity in 'Kinnow Mandarin' during Advanced Fruit Maturity

In this study, we investigated the impact of harvest maturity stages and contrasting growing climates on secondary metabolites in Kinnow mandarin. Fruit samples were harvested at six harvest maturity stages (M1−M6) from two distinct growing locations falling under subtropical−arid (STA) and subtropical−humid (STH) climates. A high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) technique was employed to identify and quantify secondary metabolites in the fruit juice. A total of 31 polyphenolics and 4 limonoids, with significant differences (p < 0.05) in their concentration, were determined. With advancing maturity, phenolic acids and antioxidant activity were found to increase, whereas flavonoids and limonoids decreased in concentration. There was a transient increase in the concentration of some polyphenolics such as hesperidin, naringin, narirutin, naringenin, neoeriocitrin, rutin, nobiletin and tangeretin, and limonoid aglycones such as limonin and nomilin at mid-maturity stage (M3) which coincided with prevailing low temperature and frost events at growing locations. A higher concentration of limonin and polyphenolics was observed for fruit grown under STH climates in comparison to those grown under STA climates. The data indicate that fruit metabolism during advanced stages of maturation under distinct climatic conditions is fundamental to the flavor, nutrition and processing quality of Kinnow mandarin. This information can help in understanding the optimum maturity stage and preferable climate to source fruits with maximum functional compounds, less bitterness and high consumer acceptability.

Effect of Supplementation on Levels of Homovanilic and Vanillylmandelic Acids in Children with Autism Spectrum Disorders

Autism Spectrum Disorders (ASD) are characterized by numerous comorbidities, including various metabolic and nutritional abnormalities. In many children with ASD, problems with proper nutrition can often lead to inadequate nutrient intake and some disturbances in metabolic profiles, which subsequently correlate with impaired neurobehavioural function. The purpose of this study was to investigate and compare the relationship between supplementation, levels of homovanillic acid (HVA) and vanillylmandelic acid (VMA) and the behaviour of children with ASD using quantitative urinary acid determination and questionnaires provided by parents/caregivers. The study was carried out on 129 children between 3 and 18 years of age. HVA and VMA were extracted and derivatized from urinary samples and simultaneously analyzed by gas chromatography-mass spectrometry (GC-MS). In addition, parents/caregivers of children with ASD were asked to complete questionnaires containing information about their diet and intake/non-intake of supplements. The application of the Mann–Whitney U test showed a statistically significant difference between the level of HVA and vitamin B supplementation (p = 1.64 × 10⁻²) and also omega-6 fatty acids supplementation and the levels of HVA (p = 1.50 × 10⁻³) and VMA (p = 2.50 × 10⁻³). In some children, a reduction in the severity of autistic symptoms (better response to own name or better reaction to change) was also observed. These results suggest that supplementation affects the levels of HVA and VMA and might also affect the children’s behaviour. Further research on these metabolites and the effects of supplementation on their levels, as well as the effects on the behaviour and physical symptoms among children with ASD is needed.

Mass spectrometry in food authentication and origin traceability

Food authentication and origin traceability are popular research topics, especially as concerns about food quality continue to increase. Mass spectrometry (MS) plays an indispensable role in food authentication and origin traceability. In this review, the applications of MS in food authentication and origin traceability by analyzing the main components and chemical fingerprints or profiles are summarized. In addition, the characteristic markers for food authentication are also reviewed, and the advantages and disadvantages of MS-based techniques for food authentication, as well as the current trends and challenges, are discussed. The fingerprinting and profiling methods, in combination with multivariate statistical analysis, are more suitable for the authentication of high-value foods, while characteristic marker-based methods are more suitable for adulteration detection. Several new techniques have been introduced to the field, such as proton transfer reaction mass spectrometry, ambient ionization mass spectrometry (AIMS), and ion mobility mass spectrometry, for the determination of food adulteration due to their fast and convenient analysis. As an important trend, the miniaturization of MS offers advantages, such as small and portable instrumentation and fast and nondestructive analysis. Moreover, many applications in food authentication are using AIMS, which can help food authentication in food inspection/field analysis. This review provides a reference and guide for food authentication and traceability based on MS.

Ambient mass spectrometry for rapid authentication of milk from Alpine or lowland forage

Metabolomics approaches, such as direct analysis in real time-high resolution mass spectrometry (DART-HRMS), allow characterising many polar and non-polar compounds useful as authentication biomarkers of dairy chains. By using both a partial least squares discriminant analysis (PLS-DA) and a linear discriminant analysis (LDA), this study aimed to assess the capability of DART-HRMS, coupled with a low-level data fusion, discriminate among milk samples from lowland (silages vs. hay) and Alpine (grazing; APS) systems and identify the most informative biomarkers associated with the main dietary forage. As confirmed also by the LDA performed against the test set, DART-HRMS analysis provided an accurate discrimination of Alpine samples; meanwhile, there was a limited capacity to correctly recognise silage- vs. hay-milks. Supervised multivariate statistics followed by metabolomics hierarchical cluster analysis allowed extrapolating the most significant metabolites. Lowland milk was characterised by a pool of energetic compounds, ketoacid derivates, amines and organic acids. Seven informative DART-HRMS molecular features, mainly monoacylglycerols, could strongly explain the metabolomic variation of Alpine grazing milk and contributed to its classification. The misclassification between the two lowland groups confirmed that the intensive dairy systems would be characterised by a small variation in milk composition.

In Silico Collision Cross Section Calculations to Aid Metabolite Annotation

The interpretation of ion mobility coupled to mass spectrometry (IM-MS) data to predict unknown structures is challenging and depends on accurate theoretical estimates of the molecular ion collision cross section (CCS) against a buffer gas in a low or atmospheric pressure drift chamber. The sensitivity and reliability of computational prediction of CCS values depend on accurately modeling the molecular state over accessible conformations. In this work, we developed an efficient CCS computational workflow using a machine learning model in conjunction with standard DFT methods and CCS calculations. Furthermore, we have performed Traveling Wave IM-MS (TWIMS) experiments to validate the extant experimental values and assess uncertainties in experimentally measured CCS values. The developed workflow yielded accurate structural predictions and provides unique insights into the likely preferred conformation analyzed using IM-MS experiments. The complete workflow makes the computation of CCS values tractable for a large number of conformationally flexible metabolites with complex molecular structures.

Untargeted Metabolomics Sensitively Differentiates Gut Bacterial Species in Single Culture and Co-Culture Systems

Gut microbiome plays a vital role in human health, and its characteristic has been widely identified through next-generation sequencing techniques. Although with great genomic insights into gut microbiome, its functional information is not clearly elaborated through metagenomic techniques. On the other hand, it is suggested that fecal metabolome can be used as a functional readout of the microbiome composition; therefore, we designed a proof-of-concept study to first characterize the metabolome of different gut microbes and then investigate the relationship between bacterial metabolomes and their compositions in co-culture systems. We selected eight representative bacteria species from Bifidobacterium (2), Bacteroides (1), Lactobacillus (4), and Akkermansia (1) genera as our model microbes. Liquid chromatography coupled mass spectrometry-based untargeted metabolomics was utilized to explore the microbial metabolome of bacteria single cultures and co-culture systems. Through spectral comparisons, our results showed that untargeted metabolomics could capture the similarity and differences in metabolic profiles from eight representative gut bacteria. Also, untargeted metabolomics could sensitively differentiate gut bacterial species based on our statistical analyses. For example, citrulline and histamine levels were significantly different among four Lactobacillus species. In addition, in the co-culture systems with different bacteria population ratios, gut bacterial metabolomes can be used to quantitatively reflect bacterial population in a mixed culture. For instance, the relative abundance of 2-hydroxybutyric acid changed proportionately with the changed population ratio of Lactobacillus reuteri in the co-culture system. In summary, we proposed a workflow that could demonstrate the capability of untargeted metabolomics in differentiating gut bacterial species and detecting their characteristic metabolites proportionally to the microbial population in co-culture systems.

Metabolomics in clinical and forensic toxicology, sports anti-doping and veterinary residues

Metabolomics is a multidisciplinary field providing workflows for complementary approaches to conventional analytical determinations. It allows for the study of metabolically related groups of compounds or even the study of novel pathways within the biological system. The procedural stages of metabolomics; experimental design, sample preparation, analytical determinations, data processing and statistical analysis, compound identification and validation strategies are explored in this review. The selected approach will depend on the type of study being conducted. Experimental design influences the whole metabolomics workflow and thus needs to be properly assessed to ensure sufficient sample size, minimal introduced and biological variation and appropriate statistical power. Sample preparation needs to be simple, yet potentially global in order to detect as many compounds as possible. Analytical determinations need to be optimised either for the list of targeted compounds or a universal approach. Data processing and statistical analysis approaches vary widely and need to be better harmonised for review and interpretation. This includes validation strategies that are currently deficient in many presented workflows. Common compound identification approaches have been explored in this review. Metabolomics applications are discussed for clinical and forensic toxicology, human and equine sports anti-doping and veterinary residues.

Understanding Inborn Errors of Metabolism through Metabolomics

Inborn errors of metabolism (IEMs) are rare diseases caused by a defect in a single enzyme, co-factor, or transport protein. For most IEMs, no effective treatment is available and the exact disease mechanism is unknown. The application of metabolomics and, more specifically, tracer metabolomics in IEM research can help to elucidate these disease mechanisms and hence direct novel therapeutic interventions. In this review, we will describe the different approaches to metabolomics in IEM research. We will discuss the strengths and weaknesses of the different sample types that can be used (biofluids, tissues or cells from model organisms; modified cell lines; and patient fibroblasts) and when each of them is appropriate to use.

Plant metabolomics: a new strategy and tool for quality evaluation of Chinese medicinal materials

The present quality control method of Chinese medicinal materials (CMM) has obvious deficiency, which cannot be compatible with the multi-target and multi-component characteristics and production process of CMM. Plant metabolomics with a huge impetus to comprehensively characterize the metabolites and clarify the complexity and integrity of CMM, has been widely used in the research of CMM. This article comprehensively reviewed the application of plant metabolomics in the quality control of CMM. It introduced the concept, technique, and application examples, discussed the prospects, limitations, improvements of plant metabolomics. MS and NMR, as important techniques for plant metabolomics, are mainly highlighted in the case references. The purpose of this article is to clarify the advantage of plants metabolomics for promoting the optimization of the CMM quality control system and proposing a system approach to realize the overall quality control of CMM based on plant metabolomics combined with multidisciplinary method.

PyFragMS-A Web Tool for the Investigation of the Collision-Induced Fragmentation Pathways

Dissociation induced by the accumulation of internal energy via collisions of ions with neutral molecules is one of the most important fragmentation techniques in mass spectrometry (MS), and the identification of small singly charged molecules is based mainly on the consideration of the fragmentation spectrum. Many research studies have been dedicated to the creation of databases of experimentally measured tandem mass spectrometry (MS/MS) spectra (such as MzCloud, Metlin, etc.) and developing software for predicting MS/MS fragments in silico from the molecular structure (such as MetFrag, CFM-ID, CSI:FingerID, etc.). However, the fragmentation mechanisms and pathways are still not fully understood. One of the limiting obstacles is that protomers (positive ions protonated at different sites) produce different fragmentation spectra, and these spectra overlap in the case of the presence of different protomers. Here, we are proposing to use a combination of two powerful approaches: computing fragmentation trees that carry information of all consecutive fragmentations and consideration of the MS/MS data of isotopically labeled compounds. We have created PyFragMS-a web tool consisting of a database of annotated MS/MS spectra of isotopically labeled molecules (after H/D and/or ¹⁶O/¹⁸O exchange) and a collection of instruments for computing fragmentation trees for an arbitrary molecule. Using PyFragMS, we investigated how the site of protonation influences the fragmentation pathway for small molecules. Also, PyFragMS offers capabilities for performing database search when MS/MS data of the isotopically labeled compounds are taken into account.

Metabolic Footprinting of Microbial Systems Based on Comprehensive In Silico Predictions of MS/MS Relevant Data

Metabolic footprinting represents a holistic approach to gathering large-scale metabolomic information of a given biological system and is, therefore, a driving force for systems biology and bioprocess development. The ongoing development of automated cultivation platforms increases the need for a comprehensive and rapid profiling tool to cope with the cultivation throughput. In this study, we implemented a workflow to provide and select relevant metabolite information from a genome-scale model to automatically build an organism-specific comprehensive metabolome analysis method. Based on in-house literature and predicted metabolite information, the deduced metabolite set was distributed in stackable methods for a chromatography-free dilute and shoot flow-injection analysis multiple-reaction monitoring profiling approach. The workflow was used to create a method specific for Saccharomyces cerevisiae, covering 252 metabolites with 7 min/sample. The method was validated with a commercially available yeast metabolome standard, identifying up to 74.2% of the listed metabolites. As a first case study, three commercially available yeast extracts were screened with 118 metabolites passing quality control thresholds for statistical analysis, allowing to identify discriminating metabolites. The presented methodology provides metabolite screening in a time-optimised way by scaling analysis time to metabolite coverage and is open to other microbial systems simply starting from genome-scale model information.

Paramounter: Direct Measurement of Universal Parameters To Process Metabolomics Data in a "White Box"

Choosing appropriate data processing parameters is critical in processing liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics data. The conventional design of experiments (DOE) approach is time-consuming and provides no intuitive explanation why the selected parameters generate the best results. After studying commonly used metabolomics data processing software, this work summarized a set of universal parameters, including mass tolerance, peak height, peak width, and instrumental shift. These universal parameters are shared among different feature extraction programs and are critical to metabolic feature extraction. We then developed Paramounter, an R program that automatically measures these universal parameters from raw LC-MS-based metabolomics data prior to metabolic feature extraction. This is made possible through novel concepts of rank-based intensity sorting, zone of interest, and many others. Paramounter also translates universal parameters to software-specific parameters for data processing in different programs. Applying Paramounter is demonstrated to provide a threefold increase in the extracted metabolites compared to using default parameters in MS-DIAL-based feature extraction. Furthermore, the comparison between Paramounter, AutoTuner, and IPO showed that Paramounter generates 3.7- and 1.6-fold more true positive features than AutoTuner and IPO, respectively. Further validation of Paramounter on 11 datasets covering different sample types, data acquisition modes, and MS vendors proved that Paramounter is a convenient and robust program. Overall, the proposed universal parameters and the development of Paramounter address a critical need in metabolomics data processing, transforming metabolomics feature extraction from a "black box" to a "white box." Paramounter is freely available on GitHub (https://github.com/HuanLab/Paramounter).

Comparative metabolite fingerprinting of chia, flax and sesame seeds using LC-MS untargeted metabolomics

Chia, flax, and sesame seeds are well known for their nutritional quality and are commonly included in bakery products. So far, the development of methods to verify their presence and authenticity in foods is a requisite and a raised need. In this work we applied untargeted metabolomics to propose authenticity markers. Seeds were analyzed by HPLC-MS/MS and 9938 features in negative mode and 9044 in positive mode were obtained by Mzmine. After isotopes grouping, alignment, gap-filling, filtering adducts, and normalization, PCA was applied to explore the dataset and recognize pre-existent classification patterns. OPLS-DA analysis and S-Plots were used as supervised methods. Twenty-five molecules (12 in negative mode and 13 in positive mode) were selected as discriminant for the three seeds, polyphenols and lignans were identified among them. To the best of our knowledge, this is the first approach using non-target HPLC-MS/MS for the authentication of chia, flax and sesame seeds.

The Potential of Metabolomics in Biomedical Applications

The metabolome offers a dynamic, comprehensive, and precise picture of the phenotype. Current high-throughput technologies have allowed the discovery of relevant metabolites that characterize a wide variety of human phenotypes with respect to health, disease, drug monitoring, and even aging. Metabolomics, parallel to genomics, has led to the discovery of biomarkers and has aided in the understanding of a diversity of molecular mechanisms, highlighting its application in precision medicine. This review focuses on the metabolomics that can be applied to improve human health, as well as its trends and impacts in metabolic and neurodegenerative diseases, cancer, longevity, the exposome, liquid biopsy development, and pharmacometabolomics. The identification of distinct metabolomic profiles will help in the discovery and improvement of clinical strategies to treat human disease. In the years to come, metabolomics will become a tool routinely applied to diagnose and monitor health and disease, aging, or drug development. Biomedical applications of metabolomics can already be foreseen to monitor the progression of metabolic diseases, such as obesity and diabetes, using branched-chain amino acids, acylcarnitines, certain phospholipids, and genomics; these can assess disease severity and predict a potential treatment. Future endeavors should focus on determining the applicability and clinical utility of metabolomic-derived markers and their appropriate implementation in large-scale clinical settings.

An ensemble approach to the structure-function problem in microbial communities

The metabolic activity of microbial communities plays a primary role in the flow of essential nutrients throughout the biosphere. Molecular genetics has revealed the metabolic pathways that model organisms utilize to generate energy and biomass, but we understand little about how the metabolism of diverse, natural communities emerges from the collective action of its constituents. We propose that quantifying and mapping metabolic fluxes to sequencing measurements of genomic, taxonomic, or transcriptional variation across an ensemble of diverse communities, either in the laboratory or in the wild, can reveal low-dimensional descriptions of community structure that can explain or predict their emergent metabolic activity. We survey the types of communities for which this approach might be best suited, review the analytical techniques available for quantifying metabolite fluxes in communities, and discuss what types of data analysis approaches might be lucrative for learning the structure-function mapping in communities from these data.

Unique volatile metabolite signature of sinonasal inverted papilloma detectible in plasma and nasal secretions

BACKGROUND

Sinonasal inverted papilloma (SNIP) is a benign neoplasm with aggressive features, including a high recurrence rate and a propensity for malignant transformation. Accurate diagnosis with complete resection and the need for close long-term surveillance is widely accepted as standard management. In this study, we investigate whether SNIP produces a unique volatile metabolite signature, which may ultimately lead to a novel approach to diagnose and monitor SNIP.

METHODS

Whole blood and nasal secretions from patients with SNIP and healthy age, sex, and smoking-status matched controls, were collected. There were 56 blood samples and 42 nasal secretion samples collected. The volatile metabolite signature of SNIP plasma and nasal secretion samples were compared to those of healthy controls using chromatograms.

RESULTS

Seventy-two volatiles were identified in plasma samples. MANOVA results, even when controlled for smoking-status, indicated toluene as a significant univariate result with lower levels of toluene identified in SNIP plasma samples than healthy control plasma samples. A linear discriminant analysis (LDA) model for plasma volatiles correctly classified 23/24 SNIP patients and 26/27 control patients, with a cross-validation error rate of 6.02%. Sixty-nine volatiles were identified in nasal samples. For nasal secretion samples, no single univariate response was significant. The LDA model correctly classified 21/21 SNIP patients and 11/12 control patients, with a cross-validation error rate of 6.55%.

CONCLUSIONS

This study suggests that SNIP produces a unique, detectible volatile metabolite signature. With further investigation, this can have dramatic clinical implication for diagnosis and monitoring. While most volatile metabolite studies have investigated solid organ malignancy, this novel study investigates a benign sinonasal neoplasm utilizing nasal secretions and plasma as an analysis medium, representing the first such study. This article is protected by copyright. All rights reserved.

Metabolomics: An Emerging Approach to Understand Pathogenesis and to Assess Diagnosis and Response to Treatment in Spondyloarthritis

Spondyloarthritis (SpA) is a group of rheumatic diseases whose pathogenesis relies on a complex interplay between genetic and environmental factors. Over the last several years, the importance of the alteration of the gut microbiota, known as dysbiosis, and the interaction of bacterial products with host immunity have been highlighted as intriguing key players in SpA development. The recent advent of the so called “-omics” sciences, that include metabolomics, opened the way to a new approach to SpA through a deeper characterisation of the pathogenetic mechanisms behind the disease. In addition, metabolomics can reveal potential new biomarkers to diagnose and monitor SpA patients. The aim of this review is to highlight the most recent advances concerning the application of metabolomics to SpA, in particular focusing attention on Ankylosing Spondylitis and Psoriatic Arthritis.