Metabolomics: Definitions and Significance in Systems Biology

Quasi-targeted metabolomics revealed isoliquiritigenin and lauric acid associated with resistance to tobacco black shank

Tobacco black shank (TBS), caused by Phytophthora nicotianae, is a severe disease. Plant root exudates play a crucial role in mediating plant-pathogen interactions in the rhizosphere. However, the specific interaction between key secondary metabolites present in root exudates and the mechanisms of disease resistance remains poorly understood. This study conducted a comprehensive comparison via quasi-targeted metabolomic analysis on the root exudate metabolites from the tobacco cultivar Yunyan87 and K326, both before and after inoculation with P. nicotianae. The results showed that the root exudate metabolites changed after P. nicotianae inoculation, and the root exudate metabolites of different tobacco cultivar was significantly different. Furthermore, homovanillic acid, lauric acid, and isoliquiritigenin were identified as potential key compounds for TBS resistance based on their impact on the mycelium growth of the pathogens. The pot experiment showed that isoliquiritigenin reduced the incidence by 55.2%, while lauric acid reduced it by 45.8%. This suggests that isoliquiritigenin and lauric acid have potential applications in the management of TBS. In summary, this study revealed the possible resistance mechanisms of differential metabolites in resistance of commercial tobacco cultivar, and for the first time discovered the inhibitory effects of isoliquiritigenin and homovanillic acid on P. nictianae, and attempt to use plants secondary metabolites of for plant protection.

Polyvinylpyrrolidone-curcumin nanoparticles with immune regulatory and metabolism regulatory effects for the treatment of experimental autoimmune uveitis

Uveitis comprises a cluster of intraocular inflammatory disorders characterized by uncontrolled autoimmune responses and excessive oxidative stress leading to vision loss worldwide. In the present study, curcumin (CUR) was conjugated with polyvinylpyrrolidone (PVP) to form PVP-CUR nanoparticles with significantly elevated solubility and outstanding multiple radical scavenging abilities. In vitro studies revealed that PVP-CUR nanoparticles markedly mitigated oxidative stress and reduced apoptosis in a H2O2-induced human retinal pigment epithelial cell line (ARPE-19) and promoted phenotypic polarization from M1 to M2 in an LPS-induced human microglial cell line (HMC3). Further in vivo studies demonstrated the prominent therapeutic effects of PVP-CUR nanoparticles on experimental autoimmune uveitis (EAU), which relieved clinical and pathological progression, improved perfusion and tomographic manifestations of retinal vessels, and reduced blood-retinal barrier (BRB) leakage; these effects may be mediated by mitigating oxidative stress and attenuating macrophage/microglia-elicited inflammation. Notably, treatment with PVP-CUR nanoparticles was shown to regulate metabolite alterations in EAU rats, providing novel insights into the underlying mechanisms involved. Additionally, the PVP-CUR nanoparticles showed great biocompatibility in vivo. In summary, our study revealed that PVP-CUR nanoparticles may serve as effective and safe nanodrugs for treating uveitis and other oxidative stress- and inflammation-related diseases.

The relationship between dry eye disease and human microbiota: A review of the science

A complex relationship exists between human microbiota and the risk for ophthalmic disease. While the homeostatic composition of human microbiota is still being established, including what defines dysbiosis (i.e. changes in diversity and abundance), pilot research has begun to identify the potential influence of demographics, geography, and co-morbidities on the microbiota and describe their impact on ocular health. This review specifically focuses on the scientific relationships of the human oral and gut microbiota to dry eye disease (DED), a set of conditions impacting the tear film and ocular surface. Although data are sparse and often conflict across studies, the literature generally supports associations between microbial imbalance (dysbiosis) and DED and alterations in microbial diversity and abundance to specific aspects of DED. This review examines the relevant science and mechanistic relationships linking gut and oral dysbiosis and DED. Various physiochemical factors and therapeutic approaches that alter microbiota, including medications and fecal transplants are examined in relation to DED.

Multi-omic analysis reveals that Bacillus licheniformis enhances pekin ducks growth performance via lipid metabolism regulation

Introduction: Bacillus licheniformis (B.licheniformis) was widely used in poultry feeds. However, it is still unclear about how B.licheniformis regulates the growth and development of Pekin ducks. Methods: The experiment was designed to clarify the effect and molecular mechanism of B. licheniformis on the lipid metabolism and developmental growth of Pekin ducks through multiomics analysis, including transcriptomic and metabolomic analyses. Results: The results showed that compared with the control group, the addition of 400 mg/kg B. licheniformis could significantly increase the body weight of Pekin ducks and the content of triglyceride (p < 0.05), at the same time, the addition of B. licheniformis could affect the lipid metabolism of liver in Pekin ducks, and the addition of 400 mg/kg B. licheniformis could significantly increase the content of lipoprotein lipase in liver of Pekin ducks. Transcriptomic analysis revealed that the addition of B. licheniformis primarily impacted fatty acid and glutathione, amino acid metabolism, fatty acid degradation, as well as biosynthesis and elongation of unsaturated fatty acids. Metabolomic analysis indicated that B. licheniformis primarily affected the regulation of glycerol phospholipids, fatty acids, and glycerol metabolites. Multiomics analysis demonstrated that the addition of B. licheniformis to the diet of Pekin ducks enhanced the regulation of enzymes involved in fat synthesis via the PPAR signaling pathway, actively participating in fat synthesis and fatty acid transport. Discussion: We found that B. licheniformis effectively influences fat content and lipid metabolism by modulating lipid metabolism-associated enzymes in the liver. Ultimately, this study contributes to our understanding of how B. licheniformis can improve the growth performance of Pekin ducks, particularly in terms of fat deposition, thereby providing a theoretical foundation for its practical application. Conclusion: B. licheniformis can increase the regulation of enzymes related to fat synthesis through PPAR signal pathway, and actively participate in liver fat synthesis and fatty acid transport, thus changing the lipid metabolism of Pekin ducks, mainly in the regulation of glycerol phospholipids, fatty acids and glycerol lipid metabolites.

Metabolomics of Ramadan fasting and associated risk of chronic diseases

BACKGROUND

The dramatic change in lifestyle associated with Ramadan fasting raises questions about its effect on metabolism and health. Metabolites, as the end product of metabolism, are excellent candidates to be studied in this regard.

OBJECTIVE

This study aims to investigate the effect of Ramadan fasting on the metabolic profile and risk of chronic diseases.

METHODS

The London Ramadan study (LORANS) is an observational study in which 2 blood samples were collected from 72 participants a few days before and after the fasting month of Ramadan. We conducted metabolomic profiling using nuclear magnetic resonance spectroscopy to assess the change in individual metabolites from before to after Ramadan. Also, we generated metabolic scores (scaled from 0 to 100) for 7 chronic diseases in the UK Biobank and assessed the association of Ramadan fasting with these scores in LORANS.

RESULTS

Of the 72 participants, 35 were male (48.6%); the mean (± standard deviation) age was 45.7 (±16) y. Ramadan fasting was associated with changes in 14 metabolites (1 inflammation marker, 1 amino acid, 2 glycolysis-related metabolites, 2 ketone bodies, 2 triglyceride, and 6 lipoprotein subclasses), independent of changes in body composition. Using data from 117,981 participants in the UK Biobank, we generated metabolic scores for diabetes, hypertension, coronary artery disease, renal failure, colorectal cancer, breast cancer, and lung cancer. The metabolic scores for lung cancer, colorectal cancer, and breast cancer were lower after Ramadan in LORANS (-4.74, 9.6%, 95% confidence interval -6.56, -2.91, P < 0.001), (-1.09, -2.4%, -1.69, -0. 50, P < 0.001), and (-0.48, -1.1%, -0. 81, -0.15, P = 0.006), respectively.

CONCLUSIONS

Ramadan fasting is associated with short-term favorable changes in the metabolic profile concerning risk of some chronic diseases. These findings should be further investigated in future, larger studies of longer follow-up with clinical outcomes.

Endophytic fungi as a potential source of anti-cancer drug

Endophytes are considered one of the major sources of bioactive compounds used in different aspects of health care including cancer treatment. When colonized, they either synthesize these bioactive compounds as a part of their secondary metabolite production or augment the host plant machinery in synthesising such bioactive compounds. Hence, the study of endophytes has drawn the attention of the scientific community in the last few decades. Among the endophytes, endophytic fungi constitute a major portion of endophytic microbiota. This review deals with a plethora of anti-cancer compounds derived from endophytic fungi, highlighting alkaloids, lignans, terpenes, polyketides, polyphenols, quinones, xanthenes, tetralones, peptides, and spirobisnaphthalenes. Further, this review emphasizes modern methodologies, particularly omics-based techniques, asymmetric dihydroxylation, and biotic elicitors, showcasing the dynamic and evolving landscape of research in this field and describing the potential of endophytic fungi as a source of anticancer drugs in the future.

NMR analysis seeking for cognitive decline and dementia metabolic markers in plasma from aged individuals

BACKGROUND

Blood biomarkers can improve the ability to diagnose dementia, providing new information to better understand the pathophysiology and causes of the disease. Some studies with patients have already shown changes in metabolic profiles among patients with pathological cognitive decline or Alzheimer's disease, when compared to individuals with normal cognition.

METHODS

To search for new metabolic biomarkers of dementia, we analyzed serum levels of several metabolites, measured by nuclear magnetic resonance spectroscopy, in elderly individuals, a group with normal cognitive decline (control), and three other groups with cognitive decline. pathological (low, moderate, and severe).

RESULTS

Decreased plasma levels of tyrosine, glutamate, valine, leucine, and isoleucine are associated with worsening of pathological cognitive decline. However, the area under analysis of receptor operating characteristics suggests that tyrosine and glutamate have low specificity and sensitivity. Valine, leucine, and isoleucine are influenced by blood glucose or diabetes, but these conditions do not seem to be of great influence in the differences observed. Isobutyrate, histidine, acetone and unknown-1 metabolite also decrease their plasma levels with increasing CD. Isobutyrate ad histidine could have neuroprotective and antioxidant actions, respectively. To elucidate the role of decreased unknown metabolite-1 as a CD biomarker, it will be necessary to previously investigate its identity. To define and elucidate the role of acetone in pathological CD, additional laboratory and clinical studies must be performed. All these metabolites together may constitute a set of biomarkers with capability to identify pathological CD or dementia.

SIGNIFICANCE AND NOVELTY

Decrease of glutamate, tyrosine, valine, leucine, isoleucine, histidine, isobutyrate, acetone and unknown-1 metabolite together are a set of biomarkers able to identify pathological CD or dementia. Histidine, isobutyrate, acetone and unknown-1 metabolite are more specific biomarkers of CD.

Critical Factors in Sample Collection and Preparation for Clinical Metabolomics of Underexplored Biological Specimens

This review article compiles critical pre-analytical factors for sample collection and extraction of eight uncommon or underexplored biological specimens (human breast milk, ocular fluids, sebum, seminal plasma, sweat, hair, saliva, and cerebrospinal fluid) under the perspective of clinical metabolomics. These samples are interesting for metabolomics studies as they reflect the status of living organisms and can be applied for diagnostic purposes and biomarker discovery. Pre-collection and collection procedures are critical, requiring protocols to be standardized to avoid contamination and bias. Such procedures must consider cleaning the collection area, sample stimulation, diet, and food and drug intake, among other factors that impact the lack of homogeneity of the sample group. Precipitation of proteins and removal of salts and cell debris are the most used sample preparation procedures. This review intends to provide a global view of the practical aspects that most impact results, serving as a starting point for the designing of metabolomic experiments.

Indirect biomarkers of blood doping: A systematic review

The detection of blood doping represents a current major issue in sports and an ongoing challenge for antidoping research. Initially focusing on direct detection methods to identify a banned substance or its metabolites, the antidoping effort has been progressively complemented by indirect approaches. The longitudinal and individual monitoring of specific biomarkers aims to identify nonphysiological variations that may be related to doping practices. From this perspective, the identification of markers sensitive to erythropoiesis alteration is key in the screening of blood doping. The current Athlete Biological Passport implemented since 2009 is composed of 14 variables (including two primary markers, i.e., hemoglobin concentration and OFF score) for the hematological module to be used for indirect detection of blood doping. Nevertheless, research has continually proposed and investigated new markers sensitive to an alteration of the erythropoietic cascade and specific to blood doping. If multiple early markers have been identified (at the transcriptomic level) or developed directly in a diagnostics' kit (at a proteomic level), other target variables at the end of the erythropoietic process (linked with the red blood cell functions) may strengthen the hematological module in the future. Therefore, this review aims to provide a global systematic overview of the biomarkers considered to date in the indirect investigation of blood doping.

Gut Microbiota in Ischemic Stroke: Role of Gut Bacteria-Derived Metabolites

Ischemic stroke (IS) remains a leading cause of death and long-term disability globally. Several mechanisms including glutamate excitotoxicity, calcium overload, neuroinflammation, oxidative stress, mitochondrial damage, and apoptosis are known to be involved in the pathogenesis of IS, but the underlying pathophysiology mechanisms of IS are not fully clarified. During the past decade, gut microbiota were recognized as a key regulator to affect the health of the host either directly or via their metabolites. Recent studies indicate that gut bacterial dysbiosis is closely related to hypertension, diabetes, obesity, dyslipidemia, and metabolic syndrome, which are the main risk factors for cardiovascular diseases. Increasing evidence indicates that IS can lead to perturbation in gut microbiota and increased permeability of the gut mucosa, known as "leaky gut," resulting in endotoxemia and bacterial translocation. In turn, gut dysbiosis and impaired intestinal permeability can alter gut bacterial metabolite signaling profile from the gut to the brain. Microbiota-derived products and metabolites, such as short-chain fatty acids (SCFAs), bile acids (BAs), trimethylamine N-oxide (TMAO), lipopolysaccharides (LPS), and phenylacetylglutamine (PAGln) can exert beneficial or detrimental effects on various extraintestinal organs, including the brain, liver, and heart. These metabolites have been increasingly acknowledged as biomarkers and mediators of IS. However, the specific role of the gut bacterial metabolites in the context of stroke remains incompletely understood. In-depth studies on these products and metabolites may provide new insight for the development of novel therapeutics for IS.

Can plant hormonomics be built on simple analysis? A review

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

An overview of metabolomic and proteomic profiling in bipolar disorder and its clinical value

INTRODUCTION

Bipolar disorder (BD) is a complex psychiatric disease characterized by alternating mood episodes. As for any other psychiatric illness, currently there is no biochemical test that is able to support diagnosis or therapeutic decisions for BD. In this context, the discovery and validation of biomarkers are interesting strategies that can be achieved through proteomics and metabolomics.

AREAS COVERED

In this descriptive review, a literature search including original articles and systematic reviews published in the last decade was performed with the objective to discuss the results of BD proteomic and metabolomic profiling analyses and indicate proteins and metabolites (or metabolic pathways) with potential clinical value.

EXPERT OPINION

A large number of proteins and metabolites have been reported as potential BD biomarkers; however, most studies do not reach biomarker validation stages. An effort from the scientific community should be directed toward the validation of biomarkers and the development of simplified bioanalytical techniques or protocols to determine them in biological samples, in order to translate proteomic and metabolomic findings into clinical routine assays.

Identifying the intervention mechanisms of polydatin in hyperuricemia model rats by using UHPLC-Q-Exactive Orbitrap mass spectroscopy metabonomic approach

Introduction

Polydatin is a biologically active compound found in mulberries, grapes, and Polygonum cuspidatum, and it has uric acid-lowering effects. However, its urate-lowering effects and the molecular mechanisms underlying its function require further study.

Methods

In this study, a hyperuricemic rat model was established to assess the effects of polydatin on uric acid levels. The body weight, serum biochemical indicators, and histopathological parameters of the rats were evaluated. A UHPLC-Q-Exactive Orbitrap mass spectrometry-based metabolomics approach was applied to explore the potential mechanisms of action after polydatin treatment.

Results

The results showed a trend of recovery in biochemical indicators after polydatin administration. In addition, polydatin could alleviate damage to the liver and kidneys. Untargeted metabolomics analysis revealed clear differences between hyperuricemic rats and the control group. Fourteen potential biomarkers were identified in the model group using principal component analysis and orthogonal partial least squares discriminant analysis. These differential metabolites are involved in amino acid, lipid, and energy metabolism. Of all the metabolites, the levels of L-phenylalanine, L-leucine, O-butanoylcarnitine, and dihydroxyacetone phosphate decreased, and the levels of L-tyrosine, sphinganine, and phytosphingosine significantly increased in hyperuricemic rats. After the administration of polydatin, the 14 differential metabolites could be inverted to varying degrees by regulating the perturbed metabolic pathway.

Conclusion

This study has the potential to enhance our understanding of the mechanisms of hyperuricemia and demonstrate that polydatin is a promising potential adjuvant for lowering uric acid levels and alleviating hyperuricemia-related diseases.

Applications of multi-omics techniques to unravel the fermentation process and the flavor formation mechanism in fermented foods

Fermented foods are important components of the human diet. There is increasing awareness of abundant nutritional and functional properties present in fermented foods that arise from the transformation of substrates by microbial communities. Thus, it is significant to unravel the microbial communities and mechanisms of characteristic flavor formation occurring during fermentation. There has been rapid development of high-throughput and other omics technologies, such as metaproteomics and metabolomics, and as a result, there is growing recognition of the importance of integrating these approaches. The successful applications of multi-omics approaches and bioinformatics analyses have provided a solid foundation for exploring the fermentation process. Compared with single-omics, multi-omics analyses more accurately delineate microbial and molecular features, thus they are more apt to reveal the mechanisms of fermentation. This review introduces fermented foods and an overview of single-omics technologies - including metagenomics, metatranscriptomics, metaproteomics, and metabolomics. We also discuss integrated multi-omics and bioinformatic analyses and their role in recent research progress related to fermented foods, as well as summarize the main potential pathways involved in certain fermented foods. In the future, multilayered analyses of multi-omics data should be conducted to enable better understanding of flavor formation mechanisms in fermented foods.

How Central Carbon Metabolites of Mexican Mint (Plectranthus amboinicus) Plants Are Impacted under Different Watering Regimes

Plants are sessile, and their ability to reprogram their metabolism to adapt to fluctuations in soil water level is crucial but not clearly understood. A study was performed to determine alterations in intermediate metabolites involved in central carbon metabolism (CCM) following exposure of Mexican mint (Plectranthus amboinicus) to varying watering regimes. The water treatments were regular watering (RW), drought (DR), flooding (FL), and resumption of regular watering after flooding (DHFL) or after drought (RH). Leaf cluster formation and leaf greening were swift following the resumption of regular watering. A total of 68 key metabolites from the CCM routes were found to be significantly (p < 0.01) impacted by water stress. Calvin cycle metabolites in FL plants, glycolytic metabolites in DR plants, total tricarboxylic acid (TCA) cycle metabolites in DR and DHFL plants, and nucleotide biosynthetic molecules in FL and RH plants were significantly (p < 0.05) increased. Pentose phosphate pathway (PPP) metabolites were equally high in all the plants except DR plants. Total Calvin cycle metabolites had a significantly (p < 0.001) strong positive association with TCA cycle (r = 0.81) and PPP (r = 0.75) metabolites. Total PPP metabolites had a moderately positive association with total TCA cycle metabolites (r = 0.68; p < 0.01) and a negative correlation with total glycolytic metabolites (r = -0.70; p < 0.005). In conclusion, the metabolic alterations of Mexican mint plants under different watering regimes were revealed. Future studies will use transcriptomic and proteomic approaches to identify genes and proteins that regulate the CCM route.

Untargeted profiling of secondary metabolites and phytotoxins associated with stemphylium blight of lentil

Stemphylium botryosum alters lentil secondary metabolism and differentially affects resistant and susceptible genotypes. Untargeted metabolomics identifies metabolites and their potential biosynthetic pathways that play a crucial role in resistance to S. botryosum. The molecular and metabolic processes that mediate resistance to stemphylium blight caused by Stemphylium botryosum Wallr. in lentil are largely unknown. Identifying metabolites and pathways associated with Stemphylium infection may provide valuable insights and novel targets to breed for enhanced resistance. The metabolic changes following infection of four lentil genotypes by S. botryosum were investigated by comprehensive untargeted metabolic profiling employing reversed-phase or hydrophilic interaction liquid chromatography (HILIC) coupled to a Q-Exactive mass spectrometer. At the pre-flowering stage, plants were inoculated with S. botryosum isolate SB19 spore suspension and leaf samples were collected at 24, 96 and 144 h post-inoculation (hpi). Mock-inoculated plants were used as negative controls. After analyte separation, high-resolution mass spectrometry data was acquired in positive and negative ionization modes. Multivariate modeling revealed significant treatment, genotype and hpi effects on metabolic profile changes that reflect lentil response to Stemphylium infection. In addition, univariate analyses highlighted numerous differentially accumulated metabolites. By contrasting the metabolic profiles of SB19-inoculated and mock-inoculated plants and among lentil genotypes, 840 pathogenesis-related metabolites were detected including seven S. botryosum phytotoxins. These metabolites included amino acids, sugars, fatty acids and flavonoids in primary and secondary metabolism. Metabolic pathway analysis revealed 11 significant pathways including flavonoid and phenylpropanoid biosynthesis, which were affected upon S. botryosum infection. This research contributes to ongoing efforts toward a comprehensive understanding of the regulation and reprogramming of lentil metabolism under biotic stress, which will provide targets for potential applications in breeding for enhanced disease resistance.

Sample Preparation in Microbial Metabolomics: Advances and Challenges

Microbial metabolomics has gained significant interest as it reflects the physiological state of microorganisms. Due to the great variability of biological organisms, in terms of physicochemical characteristics and variable range of concentration of metabolites, the choice of sample preparation methods is a crucial step in the metabolomics workflow and will reflect on the quality and reliability of the results generated. The procedures applied to the preparation of microbial samples will vary according to the type of microorganism studied, the metabolomics approach (untargeted or targeted), and the analytical platform of choice. This chapter aims to provide an overview of the sample preparation workflow for microbial metabolomics, highlighting the pre-analytical factors associated with cultivation, harvesting, metabolic quenching, and extraction. Discussions focus on obtaining intracellular and extracellular metabolites. Finally, we introduced advanced sample preparation methods based on automated systems.

The community ecology perspective of omics data

The measurement of uncharacterized pools of biological molecules through techniques such as metabarcoding, metagenomics, metatranscriptomics, metabolomics, and metaproteomics produces large, multivariate datasets. Analyses of these datasets have successfully been borrowed from community ecology to characterize the molecular diversity of samples (ɑ-diversity) and to assess how these profiles change in response to experimental treatments or across gradients (β-diversity). However, sample preparation and data collection methods generate biases and noise which confound molecular diversity estimates and require special attention. Here, we examine how technical biases and noise that are introduced into multivariate molecular data affect the estimation of the components of diversity (i.e., total number of different molecular species, or entities; total number of molecules; and the abundance distribution of molecular entities). We then explore under which conditions these biases affect the measurement of ɑ- and β-diversity and highlight how novel methods commonly used in community ecology can be adopted to improve the interpretation and integration of multivariate molecular data. Video Abstract.

Urinary Metabolomics Study on the Protective Role of Cocoa in Zucker Diabetic Rats via 1H-NMR-Based Approach

Cocoa constitutes one of the richest sources of dietary flavonoids with demonstrated anti-diabetic potential. However, the metabolic impact of cocoa intake in a diabetic context remains unexplored. In this study, metabolomics tools have been used to investigate the potential metabolic changes induced by cocoa in type 2 diabetes (T2D). To this end, male Zucker diabetic fatty rats were fed on standard (ZDF) or 10% cocoa-rich diet (ZDF-C) from week 10 to 20 of life. Cocoa supplementation clearly decreased serum glucose levels, improved glucose metabolism and produced significant changes in the urine metabolome of ZDF animals. Fourteen differential urinary metabolites were identified, with eight of them significantly modified by cocoa. An analysis of pathways revealed that butanoate metabolism and the synthesis and degradation of branched-chain amino acids and ketone bodies are involved in the beneficial impact of cocoa on diabetes. Moreover, correlation analysis indicated major associations between some of these urine metabolites (mainly valine, leucine, and isoleucine) and body weight, glycemia, insulin sensitivity, and glycated hemoglobin levels. Overall, this untargeted metabolomics approach provides a clear metabolic fingerprint associated to chronic cocoa intake that can be used as a marker for the improvement of glucose homeostasis in a diabetic context.

A narrative review of metabolomics in the era of "-omics": integration into clinical practice for inborn errors of metabolism

BACKGROUND AND OBJECTIVE

Traditional targeted metabolomic investigations identify a pre-defined list of analytes in samples and have been widely used for decades in the diagnosis and monitoring of inborn errors of metabolism (IEMs). Recent technological advances have resulted in the development and maturation of untargeted metabolomics: a holistic, unbiased, analytical approach to detecting metabolic disturbances in human disease. We aim to provide a summary of untargeted metabolomics [focusing on tandem mass spectrometry (MS-MS)] and its application in the field of IEMs.

METHODS

Data for this review was identified through a literature search using PubMed, Google Scholar, and personal repositories of articles collected by the authors. Findings are presented within several sections describing the metabolome, the current use of targeted metabolomics in the diagnostic pathway of patients with IEMs, the more recent integration of untargeted metabolomics into clinical care, and the limitations of this newly employed analytical technique.

KEY CONTENT AND FINDINGS

Untargeted metabolomic investigations are increasingly utilized in screening for rare disorders, improving understanding of cellular and subcellular physiology, discovering novel biomarkers, monitoring therapy, and functionally validating genomic variants. Although the untargeted metabolomic approach has some limitations, this "next generation metabolic screening" platform is becoming increasingly affordable and accessible.

CONCLUSIONS

When used in conjunction with genomics and the other promising "-omic" technologies, untargeted metabolomics has the potential to revolutionize the diagnostics of IEMs (and other rare disorders), improving both clinical and health economic outcomes.

Not just a gut feeling: a deep exploration of functional bacterial metabolites that can modulate host health

Bacteria have been known to reside in the human gut for roughly two centuries, but their modulatory effects on host health status are still not fully characterized. The gut microbiota is known to interact with dietary components and nutrients, producing functional metabolites that may alter host metabolic processes. The majority of thoroughly researched and understood gut microbial metabolites fall into two categories: short-chain fatty acids (SCFAs) and bacterial derivatives of dietary tryptophan. Despite the heavy emphasis on these metabolites, other metabolites stemming from microbial origin have significant impacts on host health and disease states. In this narrative review, we summarize eight recent studies elucidating novel bacterial metabolites, detailing the process by which these metabolites are identified, their actions within specific categories of human health, and how diet may impact production of these metabolites. With similar future mechanistic research, a more complete picture of bacterial impact on host metabolism may be constructed.

COLMARq: A Web Server for 2D NMR Peak Picking and Quantitative Comparative Analysis of Cohorts of Metabolomics Samples

Highly quantitative metabolomics studies of complex biological mixtures are facilitated by the resolution enhancement afforded by 2D NMR spectra such as 2D ¹³C-¹H HSQC spectra. Here, we describe a new public web server, COLMARq, for the semi-automated analysis of sets of 2D HSQC spectra of cohorts of samples. The workflow of COLMARq includes automated peak picking using the deep neural network DEEP Picker, quantitative cross-peak volume extraction by numerical fitting using Voigt Fitter, the matching of corresponding cross-peaks across cohorts of spectra, peak volume normalization between different spectra, database query for metabolite identification, and basic univariate and multivariate statistical analyses of the results. COLMARq allows the analysis of cross-peaks that belong to both known and unknown metabolites. After a user has uploaded cohorts of 2D ¹³C-¹H HSQC and optionally 2D ¹H-¹H TOCSY spectra in their preferred format, all subsequent steps on the web server can be performed fully automatically, allowing manual editing if needed and the sessions can be saved for later use. The accuracy, versatility, and interactive nature of COLMARq enables quantitative metabolomics analysis, including biomarker identification, of a broad range of complex biological mixtures as is illustrated for cohorts of samples from bacterial cultures of Pseudomonas aeruginosa in both its biofilm and planktonic states.

Can polymer-degrading microorganisms solve the bottleneck of plastics' environmental challenges?

Increasing world population and industrial activities have enhanced anthropogenic pollution, plastic pollution being especially alarming. So, plastics should be recycled and/or make them biodegradable. Chemical and physical remediating methods are usually energy consuming and costly. In addition, they are not ecofriendly and usually produce toxic byproducts. Bioremediation is a proper option as it is cost-efficient and environmentally friendly. Plastic production and consumption are increasing daily, and, as a consequence, more microorganisms are exposed to these nonbiodegradable polymers. Therefore, investigating new efficient microorganisms and increasing the knowledge about their biology can pave the way for efficient and feasible plastic bioremediation processes. In this sense, omics, systems biology and bioinformatics are three important fields to analyze the biodegradation pathways in microorganisms. Based on the above-mentioned technologies, researchers can engineer microorganisms with specific desired properties to make bioremediation more efficient.

Metabolomics studies on cachexia in patients with cancer: a scoping review protocol

INTRODUCTION

Cancer seriously threatens human health worldwide. Cancer cachexia is one of the life-threatening consequences that occurs commonly in patients with cancer, and severely worsens patient survival, prognosis and quality of life. Previous studies have demonstrated that cancer cachexia is closely related to differential metabolites and metabolic pathways based on metabolomics analysis. This scoping review protocol, therefore, aims to provide the strategy for a formal scoping review that will summarise the differential metabolites and related metabolic pathways of cachexia in patients with cancer.

METHODS AND ANALYSIS

The proposed scoping review will follow the Arksey and O'Malley's methodological framework, Levac et al's recommendations for applying this framework, and Peters' enhancements of the framework. The key information from the selected studies will be extracted, including author, year of publication, cachexia definition, country/origin, study design, setting, population and sample size, biological specimens, independent variables, independent variables' measure and statistical analysis. A summary of metabolites will be divided into several sections depending on the biological specimen. Differential metabolites will be compared between paired groups, and the number and names of related metabolic pathways will be counted and described. The reporting of this scoping review will be in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews checklist. This is a scoping review protocol and describes the planned review process and provides data examples extracted from a pilot study to confirm the feasibility of further investigation of the subject.

ETHICS AND DISSEMINATION

An ethical approval is not required for this scoping review protocol, nor for the scoping review. The results of this scoping review will be disseminated through publication in a peer-reviewed journal, or presentation at a national or international conference.

Integrative metabolomic profiling reveals aberrations in myometrium associated with adenomyosis: a pilot study

BACKGROUND

Uterine adenomyosis is a common gynecologic disease in premenopausal women, the pathological mechanism of which remains largely unknown. The aim of this study was to identify metabolic biomarkers significantly altered in the myometrium of adenomyosis patients.

METHODS

The comprehensive metabolomic profiles of 17 myometrium specimens from adenomyosis patients and 25 control specimens were analyzed using untargeted approach by combination of gas chromatography-mass spectrometry and high performance liquid chromatography-mass spectrometry. Metabolic data were filtered using orthogonal partial least square-discriminant analysis and univariate statistics.

RESULTS

We firstly demonstrated that the myometrial metabolome of women with adenomyosis is distinct from that of women without adenomyosis. A total of 106 metabolites, mainly including nucleosides, lipids (including acylcarnitines), amino acids, organic acids and carbohydrates, were found to be differentially expressed in myometrium of uteri with adenomyosis compared to the control subjects. Functional inferences of these perturbed metabolites indicated that inflammation, oxidative stress, cell proliferation and apoptosis, and energy metabolism appeared to be involved in the progress of adenomyosis.

CONCLUSION

This study firstly described the integrated metabolic signatures of the adenomyosis uterus, which provided novel insights for the pathogenesis study of this disease.

Unraveling the Factors Determining Development of Type 2 Diabetes in Women With a History of Gestational Diabetes Mellitus Through Machine-Learning Techniques

Gestational diabetes mellitus (GDM) is a type of diabetes that usually resolves at the end of the pregnancy but exposes to a higher risk of developing type 2 diabetes mellitus (T2DM). This study aimed to unravel the factors, among those that quantify specific metabolic processes, which determine progression to T2DM by using machine-learning techniques. Classification of women who did progress to T2DM (labeled as PROG, n = 19) vs. those who did not (labeled as NON-PROG, n = 59) progress to T2DM has been performed by using Orange software through a data analysis procedure on a generated data set including anthropometric data and a total of 34 features, extracted through mathematical modeling/methods procedures. Feature selection has been performed through decision tree algorithm and then Naïve Bayes and penalized (L2) logistic regression were used to evaluate the ability of the selected features to solve the classification problem. Performance has been evaluated in terms of area under the operating receiver characteristics (AUC), classification accuracy (CA), precision, sensitivity, specificity, and F1. Feature selection provided six features, and based on them, classification was performed as follows: AUC of 0.795, 0.831, and 0.884; CA of 0.827, 0.813, and 0.840; precision of 0.830, 0.854, and 0.834; sensitivity of 0.827, 0.813, and 0.840; specificity of 0.700, 0.821, and 0.662; and F1 of 0.828, 0.824, and 0.836 for tree algorithm, Naïve Bayes, and penalized logistic regression, respectively. Fasting glucose, age, and body mass index together with features describing insulin action and secretion may predict the development of T2DM in women with a history of GDM.

The Application of Metabolomics in Recent Colorectal Cancer Studies: A State-of-the-Art Review

Simple Summary

Colorectal Cancer (CRC) is one of the leading causes of cancer-related death in the United States. Current diagnosis techniques are either highly invasive or lack sensitivity, suggesting the need for alternative techniques for biomarker detection. Metabolomics represents one such technique with great promise in identifying CRC biomarkers with high sensitivity and specificity, but thus far is rarely employed in a clinical setting. In order to provide a framework for future clinical usage, we characterized dysregulated metabolites across recent literature, identifying metabolites dysregulated across a variety of biospecimens. We additionally put special focus on the interplay of the gut microbiome and perturbed metabolites in CRC. We were able to identify many metabolites showing consistent dysregulation in CRC, demonstrating the value of metabolomics as a promising diagnostic technique.

Abstract

Colorectal cancer (CRC) is a highly prevalent disease with poor prognostic outcomes if not diagnosed in early stages. Current diagnosis techniques are either highly invasive or lack sufficient sensitivity. Thus, identifying diagnostic biomarkers of CRC with high sensitivity and specificity is desirable. Metabolomics represents an analytical profiling technique with great promise in identifying such biomarkers and typically represents a close tie with the phenotype of a specific disease. We thus conducted a systematic review of studies reported from January 2012 to July 2021 relating to the detection of CRC biomarkers through metabolomics to provide a collection of knowledge for future diagnostic development. We identified thirty-seven metabolomics studies characterizing CRC, many of which provided metabolites/metabolic profile-based diagnostic models with high sensitivity and specificity. These studies demonstrated that a great number of metabolites can be differentially regulated in CRC patients compared to healthy controls, adenomatous polyps, or across stages of CRC. Among these metabolite biomarkers, especially dysregulated were certain amino acids, fatty acids, and lysophosphatidylcholines. Additionally, we discussed the contribution of the gut bacterial population to pathogenesis of CRC through their modulation to fecal metabolite pools and summarized the established links in the literature between certain microbial genera and altered metabolite levels in CRC patients. Taken together, we conclude that metabolomics presents itself as a promising and effective method of CRC biomarker detection.

Innovative Approaches to Assess Intermediate Cardiovascular Risk Subjects: A Review From Clinical to Metabolomics Strategies

Current risk stratification strategies for coronary artery disease (CAD) have low predictive value in asymptomatic subjects classified as intermediate cardiovascular risk. This is relevant because not all coronary events occur in individuals with traditional multiple risk factors. Most importantly, the first manifestation of the disease may be either sudden cardiac death or acute coronary syndrome, after rupture and thrombosis of an unstable non-obstructive atherosclerotic plaque, which was previously silent. The inaccurate stratification using the current models may ultimately subject the individual to excessive or insufficient preventive therapies. A breakthrough in the comprehension of the molecular mechanisms governing the atherosclerosis pathology has driven many researches toward the necessity for a better risk stratification. In this Review, we discuss how metabolomics screening integrated with traditional risk assessments becomes a powerful approach to improve non-invasive CAD subclinical diagnostics. In addition, this Review highlights the findings of metabolomics studies performed by two relevant analytical platforms in current use-mass spectrometry (MS) hyphenated to separation techniques and nuclear magnetic resonance spectroscopy (NMR) -and evaluates critically the challenges for further clinical implementation of metabolomics data. We also discuss the modern understanding of the pathophysiology of atherosclerosis and the limitations of traditional analytical methods. Our aim is to show how discriminant metabolites originated from metabolomics approaches may become promising candidate molecules to aid intermediate risk patient stratification for cardiovascular events and how these tools could successfully meet the demands to translate cardiovascular metabolic biomarkers into clinical settings.

Liquid Chromatography-Mass Spectrometry for Clinical Metabolomics: An Overview

Metabolomics is a discipline that offers a comprehensive analysis of metabolites in biological samples. In the last decades, the notable evolution in liquid chromatography and mass spectrometry technologies has driven an exponential progress in LC-MS-based metabolomics. Targeted and untargeted metabolomics strategies are important tools in health and medical science, especially in the study of disease-related biomarkers, drug discovery and development, toxicology, diet, physical exercise, and precision medicine. Clinical and biological problems can now be understood in terms of metabolic phenotyping. This overview highlights the current approaches to LC-MS-based metabolomics analysis and its applications in the clinical research.

Challenges in LC-MS-based metabolomics for Alzheimer's disease early detection: targeted approaches versus untargeted approaches

BACKGROUND

Alzheimer's disease (AD) is one of the most common causes of dementia in old people. Neuronal deficits such as loss of memory, language and problem-solving are severely compromised in affected patients. The molecular features of AD are Aβ deposits in plaques or in oligomeric structures and neurofibrillary tau tangles in brain. However, the challenge is that Aβ is only one piece of the puzzle, and recent findings continue to support the hypothesis that their presence is not sufficient to predict decline along the AD outcome. In this regard, metabolomic-based techniques are acquiring a growing interest for either the early diagnosis of diseases or the therapy monitoring. Mass spectrometry is one the most common analytical platforms used for detection, quantification, and characterization of metabolic biomarkers. In the past years, both targeted and untargeted strategies have been applied to identify possible interesting compounds.

AIM OF REVIEW

The overall goal of this review is to guide the reader through the most recent studies in which LC-MS-based metabolomics has been proposed as a powerful tool for the identification of new diagnostic biomarkers in AD. To this aim, herein studies spanning the period 2009-2020 have been reported. Advantages and disadvantages of targeted vs untargeted metabolomic approaches have been outlined and critically discussed.

Metabolomics in the Diagnosis and Prognosis of COVID-19

Coronavirus disease 2019 (COVID-19) pandemic triggered an unprecedented global effort in developing rapid and inexpensive diagnostic and prognostic tools. Since the genome of SARS-CoV-2 was uncovered, detection of viral RNA by RT-qPCR has played the most significant role in preventing the spread of the virus through early detection and tracing of suspected COVID-19 cases and through screening of at-risk population. However, a large number of alternative test methods based on SARS-CoV-2 RNA or proteins or host factors associated with SARS-CoV-2 infection have been developed and evaluated. The application of metabolomics in infectious disease diagnostics is an evolving area of science that was boosted by the urgency of COVID-19 pandemic. Metabolomics approaches that rely on the analysis of volatile organic compounds exhaled by COVID-19 patients hold promise for applications in a large-scale screening of population in point-of-care (POC) setting. On the other hand, successful application of mass-spectrometry to detect specific spectral signatures associated with COVID-19 in nasopharyngeal swab specimens may significantly save the cost and turnaround time of COVID-19 testing in the diagnostic microbiology and virology laboratories. Active research is also ongoing on the discovery of potential metabolomics-based prognostic markers for the disease that can be applied to serum or plasma specimens. Several metabolic pathways related to amino acid, lipid and energy metabolism were found to be affected by severe disease with COVID-19. In particular, tryptophan metabolism via the kynurenine pathway were persistently dysregulated in several independent studies, suggesting the roles of several metabolites of this pathway such as tryptophan, kynurenine and 3-hydroxykynurenine as potential prognostic markers of the disease. However, standardization of the test methods and large-scale clinical validation are necessary before these tests can be applied in a clinical setting. With rapidly expanding data on the metabolic profiles of COVID-19 patients with varying degrees of severity, it is likely that metabolomics will play an important role in near future in predicting the outcome of the disease with a greater degree of certainty.

Exploiting targeted and untargeted approaches for the analysis of bacterial metabolites under altered growth conditions

In the host, pathogenic microorganisms have developed stress responses to cope with constantly changing environments. Stress responses are directly related to changes in several metabolomic pathways, which could hamper microorganisms' unequivocal identification. We evaluated the effect of various in vitro stress conditions (acidic, basic, oxidative, ethanolic, and saline conditions) on the metabolism of Staphylococcus aureus, Bacillus cereus, and Pseudomonas aeruginosa, which are common lung pathogens. The metabolite profiles of the bacteria were analyzed using liquid chromatography coupled to triple quadrupole and quadrupole time-of-flight mass spectrometry. The advantages of targeted and untargeted analysis combined with univariate and multivariate statistical analysis (principal component analysis, hierarchical cluster analysis, partial least square discriminant analysis, random forest) were combined to unequivocally identify bacterial species. In normal in vitro conditions, the targeted methodology, based on the analysis of primary metabolites, enabled the rapid and efficient discrimination of the three bacteria. In changing in vitro conditions and specifically in presence of the various stressors, the untargeted methodology proved to be more valuable for the global and accurate differentiation of the three bacteria, also considering the type of stress environment within each species. In addition, species-specific metabolites (i.e., fatty acids, polysaccharides, peptides, and nucleotide bases derivatives) were putatively identified. Good intra-day repeatability and inter-day repeatability (< 10% RSD and < 15% RSD, respectively) were obtained for the targeted and the untargeted methods. This untargeted approach highlights its importance in unusual (and less known) bacterial growth environments, being a powerful tool for infectious disease diagnosis, where the accurate classification of microorganisms is sought.

Unveiling metabolic changes in marsupialized odontogenic keratocyst: A pilot study

OBJECTIVE

We aimed to assess which metabolic pathways would be implicated in the phenotypic changes of the epithelial lining of odontogenic keratocyst after marsupialization, comparing pre- and post-marsupialized lesions with adjacent oral mucosa.

MATERIALS AND METHODS

Eighteen formalin-fixed and paraffin-embedded tissues from six subjects were divided into three paired groups: odontogenic keratocyst pre- (n = 6) and post-marsupialization (n = 6), and adjacent oral mucosa (n = 6). The metabolic pathways found in these groups were obtained by high-performance liquid chromatography-mass spectrometry-based untargeted metabolomics performed.

RESULTS

Through putative metabolite annotation followed by pathway enrichment and predictive analysis with automated algorithms (Mummichog and Gene Set Enrichment Analysis), we found differences in many cellular processes that may be involved in inflammation, oxidative stress response, keratinocyte-basal membrane attachment, differentiation, and proliferation functions, all relevant to odontogenic keratocyst pathobiology and the phenotype acquired after marsupialization.

CONCLUSION

Our study was able to identify several metabolic pathways potentially involved in the metaplastic changes induced by marsupialization of odontogenic keratocysts. An improved comprehension of this process could pave the way for the development of targeted therapies.

Pharmacometabonomics: data processing and statistical analysis

Individual variations in drug efficacy, side effects and adverse drug reactions are still challenging that cannot be ignored in drug research and development. The aim of pharmacometabonomics is to better understand the pharmacokinetic properties of drugs and monitor the drug effects on specific metabolic pathways. Here, we systematically reviewed the recent technological advances in pharmacometabonomics for better understanding the pathophysiological mechanisms of diseases as well as the metabolic effects of drugs on bodies. First, the advantages and disadvantages of all mainstream analytical techniques were compared. Second, many data processing strategies including filtering, missing value imputation, quality control-based correction, transformation, normalization together with the methods implemented in each step were discussed. Third, various feature selection and feature extraction algorithms commonly applied in pharmacometabonomics were described. Finally, the databases that facilitate current pharmacometabonomics were collected and discussed. All in all, this review provided guidance for researchers engaged in pharmacometabonomics and metabolomics, and it would promote the wide application of metabolomics in drug research and personalized medicine.

Dose-Related Urinary Metabolic Alterations of a Combination of Quercetin and Resveratrol-Treated High-Fat Diet Fed Rats

Most herbal polyphenols and flavonoids reveals multiple ameliorative benefits for obesity caused by chronic metabolic disorders. Accumulated studies have revealed that preferable therapeutic effects can be obtained through clinical combination of these two kinds of natural compounds for obesity improvement. The typical representative research was the combination of quercetin and resveratrol (CQR), in which the ratio of quercetin and resveratrol is 2:1, demonstrating a synergistic effect in anti-obesity process. Although there exists reports clarifying the mechanism of the combination of two to improve obesity from the perspective of improving adipose tissue inflammation or modulating the composition of intestinal flora, there are few further studies on the mechanism of drug action from the perspective of metabolites transformation. In this research, we mainly focused on the alterations of endogenous metabolites in rats, and analyzed the urine metabolites of obese and intervention model. Therefore, a gas chromatography-mass spectrometry (GC-MS) based metabolomics approach was applied to assess the potential effects and mechanisms of CQR at different dosages (45, 90, and 180 mg/kg) in high fat diet (HFD)-induced obesity rats. Body weight gain and visceral fat weight were reduced by CQR, as well as blood lipid and inflammatory factor levels were increased by CQR in a dose-related manner. Urinary metabolomics revealed 22 differential metabolites related to the HFD-induced obesity, which were reversed in a dose-dependent manner by CQR, of which 8 were reversed in the 45 mg/kg CQR group, 15 were reversed in the 90 mg/kg CQR group, and 18 were reversed in the 180 mg/kg CQR group. Combined with bioinformatics and pattern recognition, the results demonstrated that the key differential metabolites were basically involved in amino acid metabolism, galactose metabolism, pantothenate and CoA biosynthesis, pyruvate metabolism and lysine degradation. In summary, our results showed significant therapeutic action by CQR administration and remarkable metabolomic changes after HFD feeding and CQR intervention. Urinary metabolomic analysis was highlighted on account of providing holistic and comprehensive insights into the pathophysiological mechanisms of the HFD-induced obesity, which also supplied clues for the future mechanism studies of CQR's anti-obesity effects.

Adenomyosis is associated with specific proton nuclear magnetic resonance (1H-NMR) serum metabolic profiles

OBJECTIVE

To determine whether the adenomyosis phenotype affects the proton nuclear magnetic resonance (¹H-NMR)-based serum metabolic profile of patients.

DESIGN

Cohort study.

SETTING

University hospital-based research center.

PATIENTS

Seventy-seven patients who underwent laparoscopy for a benign gynecologic condition.

INTERVENTIONS

Pelvic magnetic resonance imaging and collection of a venous peripheral blood sample were performed during the preoperative workup. The women were allocated to the adenomyosis group (n = 32), or the control group (n = 45). The adenomyosis group was further subdivided into two groups: diffuse adenomyosis of the inner myometrium (n = 14) and focal adenomyosis of the outer myometrium (n = 18). Other adenomyosis phenotypes were excluded.

MAIN OUTCOME MEASURES

Metabolomic profiling based on ¹H-NMR spectroscopy in combination with statistical approaches.

RESULTS

The serum metabolic profiles of the patients with adenomyosis indicated lower concentrations of 3-hydroxybutyrate, glutamate, and serine compared with controls. Conversely, the concentrations of proline, choline, citrate, 2-hydroxybutyrate, and creatinine were higher in the adenomyosis group. The focal adenomyosis of the outer myometrium and the diffuse adenomyosis phenotypes also each exhibited a specific metabolic profile.

CONCLUSION

Serum metabolic changes were detected in women with features of adenomyosis compared with their disease-free counterparts, and a number of specific metabolic pathways appear to be engaged according to the adenomyosis phenotype. The metabolites with altered levels are particularly involved in immune activation as well as cell proliferation and cell migration. Nevertheless, this study did find evidence of a correlation between metabolite levels and symptoms thought to be related to adenomyosis. Further studies are required to determine the clinical significance of these differences in metabolic profiles.

Pro-biomics: Omics Technologies To Unravel the Role of Probiotics in Health and Disease

The comprehensive characterization of probiotic action has flourished during the past few decades, alongside the evolution of high-throughput, multiomics platforms. The integration of these platforms into probiotic animal and human studies has provided valuable insights into the holistic effects of probiotic supplementation on intestinal and extraintestinal diseases. Indeed, these methodologies have informed about global molecular changes induced in the host and residing commensals at multiple levels, providing a bulk of metagenomic, transcriptomic, proteomic, and metabolomic data. The meaningful interpretation of generated data remains a challenge; however, the maturation of the field of systems biology and artificial intelligence has supported analysis of results. In this review article, we present current literature on the use of multiomics approaches in probiotic studies, we discuss current trends in probiotic research, and examine the possibility of tailor-made probiotic supplementation. Lastly, we delve deeper into newer technologies that have been developed in the last few years, such as single-cell multiomics analyses, and provide future directions for the maximization of probiotic efficacy.

From Metabolite to Metabolome: Metabolomics Applications in Plasmodium Research

Advances in research over the past few decades have greatly improved metabolomics-based approaches in studying parasite biology and disease etiology. This improves the investigation of varied metabolic requirements during life stages or when following transmission to their hosts, and fulfills the demand for improved diagnostics and precise therapeutics. Therefore, this review highlights the progress of metabolomics in malaria research, including metabolic mapping of Plasmodium vertebrate life cycle stages to investigate antimalarials mode of actions and underlying complex host-parasite interactions. Also, we discuss current limitations as well as make several practical suggestions for methodological improvements which could drive metabolomics progress for malaria from a comprehensive perspective.

Future Directions in Sports-Related Concussion Management

This article focuses on 3 concepts that continue to be investigated in the search for the holy grail of concussion-a valid diagnostic test. Imaging advances are discussed with optimism that functional MRI and diffusion tensor imaging may be available clinically. Biomarkers and the use of genetic tests are covered. Sideline accelerometer use may help steer discussions of head trauma risk once technology exists to accurately estimate acceleration of the brain. In the meantime, strategies including allowing athletes to be substituted out of games for an evaluation and video review in elite sports can improve recognition of sports-related concussion.

Emerging Role of Metabolomics in Ovarian Cancer Diagnosis

Ovarian cancer is considered a silent killer due to the lack of clear symptoms and efficient diagnostic tools that often lead to late diagnoses. Over recent years, the impelling need for proficient biomarkers has led researchers to consider metabolomics, an emerging omics science that deals with analyses of the entire set of small-molecules (≤1.5 kDa) present in biological systems. Metabolomics profiles, as a mirror of tumor-host interactions, have been found to be useful for the analysis and identification of specific cancer phenotypes. Cancer may cause significant metabolic alterations to sustain its growth, and metabolomics may highlight this, making it possible to detect cancer in an early phase of development. In the last decade, metabolomics has been widely applied to identify different metabolic signatures to improve ovarian cancer diagnosis. The aim of this review is to update the current status of the metabolomics research for the discovery of new diagnostic metabolomic biomarkers for ovarian cancer. The most promising metabolic alterations are discussed in view of their potential biological implications, underlying the issues that limit their effective clinical translation into ovarian cancer diagnostic tools.

Circulating Metabolites as Potential Biomarkers for Neurological Disorders-Metabolites in Neurological Disorders

There are, still, limitations to predicting the occurrence and prognosis of neurological disorders. Biomarkers are molecules that can change in different conditions, a feature that makes them potential tools to improve the diagnosis of disease, establish a prognosis, and monitor treatments. Metabolites can be used as biomarkers, and are small molecules derived from the metabolic process found in different biological media, such as tissue samples, cells, or biofluids. They can be identified using various strategies, targeted or untargeted experiments, and by different techniques, such as high-performance liquid chromatography, mass spectrometry, or nuclear magnetic resonance. In this review, we aim to discuss the current knowledge about metabolites as biomarkers for neurological disorders. We will present recent developments that show the need and the feasibility of identifying such biomarkers in different neurological disorders, as well as discuss relevant research findings in the field of metabolomics that are helping to unravel the mechanisms underlying neurological disorders. Although several relevant results have been reported in metabolomic studies in patients with neurological diseases, there is still a long way to go for the clinical use of metabolites as potential biomarkers in these disorders, and more research in the field is needed.

Metabolomics applied in the study of emerging arboviruses caused by Aedes aegypti mosquitoes: A review

Arboviruses, such as chikungunya, dengue, yellow fever, and zika, caused by the bite of the Aedes aegypti mosquito, have been a frequent public health problem, with a high incidence of outbreaks in tropical and subtropical countries. These diseases are easily confused with a flu-like illness and present very similar symptoms, difficult to distinguish, and treat appropriately. The effects that these infections cause in the organism are fundamentally derived from complex metabolic processes. A prominent area of science that investigates the changes in the metabolism of complex organisms is the metabolomics. Metabolomics measures the metabolites produced or altered in biological organisms, through the use of robust analytical platforms, such as separation techniques hyphenated with mass spectrometry, combined with bioinformatics. This review article presents an overview of the basic concepts of metabolomics workflow and advances in this field, and compiles research articles that use this omic approach to study these arboviruses. In this context, the metabolomics is applied to search new therapies, understand the viral replication mechanisms, and access the host-virus interactions.

Endometriosis phenotypes are associated with specific serum metabolic profiles determined by proton-nuclear magnetic resonance

RESEARCH QUESTION

What is the correlation between serum metabolic profile and endometriosis phenotype?

DESIGN

A pilot study nestled in a prospective cohort study at a university hospital, including 46 patients with painful endometriosis who underwent surgery and 21 controls who did not have macroscopic endometriotic lesions. Endometriosis was strictly classified into two groups of 23 patients each: endometrioma (OMA) and deep infiltrating endometriosis (DIE). Serum samples were collected before surgery for metabolomic profiling based on proton-nuclear magnetic resonance spectroscopy in combination with statistical approaches. Comparative identification of the metabolites in the serum from endometriosis patients and from controls was carried out, including an analysis according to endometriosis phenotype.

RESULTS

The serum metabolic profiles of the endometriosis patients revealed significantly lower concentrations of several amino acids compared with the controls, whereas the concentrations of free fatty acids and ketone bodies were significantly higher. The OMA and the DIE phenotypes each had a specific metabolic profile, with higher concentrations of two ketone bodies in the OMA group, and higher concentrations of free fatty acids and lipids in the DIE group.

CONCLUSION

Proton-nuclear magnetic resonance-based metabolomics of serum samples were found to have ample potential for identifying metabolic changes associated with endometriosis phenotypes. This information may improve our understanding of the pathogenesis of endometriosis.

Resources and tools for the high-throughput, multi-omic study of intestinal microbiota

The human gut microbiome impacts several aspects of human health and disease, including digestion, drug metabolism and the propensity to develop various inflammatory, autoimmune and metabolic diseases. Many of the molecular processes that play a role in the activity and dynamics of the microbiota go beyond species and genic composition and thus, their understanding requires advanced bioinformatics support. This article aims to provide an up-to-date view of the resources and software tools that are being developed and used in human gut microbiome research, in particular data integration and systems-level analysis efforts. These efforts demonstrate the power of standardized and reproducible computational workflows for integrating and analysing varied omics data and gaining deeper insights into microbe community structure and function as well as host-microbe interactions.

Integrated LC-MS and GC-MS-based untargeted metabolomics studies of the effect of azadirachtin on Bactrocera dorsalis larvae

Azadirachtin exhibits excellent bioactivities against several hundred arthropods. However, current knowlege of its biochemical effect on B. dorsalis larvae is not deep enough. In this study, integrated LC-MS and GC-MS-based untargeted metabolomics were used to analyze the changes of endogenous metabolites and the biochemical effects of azadirachtin on B. dorsalis larvae. Azadirachtin has excellent bioactivities against B. dorsalis larvae in this study, leading to a longer developmental duration, lower survival rate, and low pupa weight. The effect of azadirachtin was investigated on a total of 22 and 13 differentially abundant metabolites in the LC-MS and GC-MS-based metabolomics results, are selected respectively. Pathway analysis indicated that 14 differentially enriched metabolic pathways, including seven influential pathways, are worthy of attention. Further integrated key metabolic pathway analysis showed that histidine metabolism, D-glutamine and D-glutamate metabolism, biotin metabolism, ascorbate and aldarate metabolism, pentose and glucuronate interconversions, and alanine, aspartate and glutamate metabolism in B. dorsalis larvae are significantly relevant pathways affected by azadirachtin. Although extrapolating the bioactivity results in this study to the practical project of B. dorsalis pest management in the field has limitations, it was found that azadirachtin has a significant effect on the primary metabolism of B. dorsalis larvae.

Metabolomic tools used in marine natural product drug discovery

Introduction: The marine environment is a very promising resource for natural product research, with many of these reaching the market as new drugs, especially in the field of cancer therapy as well as the drug discovery pipeline for new antimicrobials. Exploitation for bioactive marine compounds with unique structures and novel bioactivity such as the isoquinoline alkaloid; trabectedin, the polyether macrolide; halichondrin B, and the peptide; dolastatin 10, requires the use of analytical techniques, which can generate unbiased, quantitative, and qualitative data to benefit the biodiscovery process. Metabolomics has shown to bridge this understanding and facilitate the development of new potential drugs from marine sources and particularly their microbial symbionts.Areas covered: In this review, articles on applied secondary metabolomics ranging from 1990-2018 as well as to the last quarter of 2019 were probed to investigate the impact of metabolomics on drug discovery for new antibiotics and cancer treatment.Expert opinion: The current literature review highlighted the effectiveness of metabolomics in the study of targeting biologically active secondary metabolites from marine sources for optimized discovery of potential new natural products to be made accessible to a R&D pipeline.

Changes in the Serum Metabolome of Acute Myocardial Ischemia Rat Pretreatment with Electroacupuncture

Myocardial infarction (MI), the most common symptom is chest pain, occurs when blood flow decreases or stops to a part of the heart, causing damage to the heart muscle. Electroacupuncture pretreatment (EP) is a recent observation which has been shown to induce ischemic tolerance like the ischemia preconditioning, suggesting that EP may be a promising preventive strategy for individual susceptibility to MI. This study investigated mechanisms that underlie the effect of EP on MI through the use of gas chromatography-mass spectrometry (GC-MS)-based metabolic profiling. Male Sprague-Dawley rats were randomly divided to receive or not receive three days of EP at PC6 (Neiguan). Then on the fourth day, each group was further divided to undergo mock surgery or MI, induced by ligation of the left anterior descending coronary artery. After 24h, the blood samples and hearts were collected for the follow-up research. The results showed that treatment by EP significantly reduced the levels of CK-MB, cTnT, AST, and MDH in serum and decreased myocardial infarction area. According to GC-MS-based serum metabolic profiling and analysis, a total of 636 characteristic peaks were identified, including 158 known and 478 unknown metabolites. MI caused comprehensive metabolic changes in glycolysis-related metabolites, malate-aspartate shuttle (MAS) metabolites, and purine metabolites with anti-oxidant functions, while EP reversed more than half of the differential metabolic changes, mainly affecting amino acid and energy metabolism, especially the glutamate metabolism and MAS. In a word, our findings suggest that EP exerts its cardioprotective effect on MI by regulating amino acid and energy metabolisms. Meanwhile, GC-MS-based metabolomics provided a powerful way to characterize the metabolic features of MI, with and without EP, and thereby improved our understanding of the effect and mechanisms of EP.

A pilot metabolomics study of tuberculosis immune reconstitution inflammatory syndrome

BACKGROUND

Diagnosis of paradoxical tuberculosis-associated immune reconstitution inflammatory syndrome (TB-IRIS) is challenging and new tools are needed for early diagnosis as well as to understand the biochemical events that underlie the pathology in TB-IRIS.

METHODS

Plasma samples were obtained from participants from a randomized HIV/TB treatment strategy study (AIDS Clinical Trials Group [ACTG] A5221) with (n = 26) and without TB-IRIS (n = 22) for an untargeted metabolomics pilot study by liquid-chromatography mass spectrometry. The metabolic profile of these participants was compared at the study entry and as close to the diagnosis of TB-IRIS as possible (TB-IRIS window). Molecular features with p < 0.05 and log2 fold change ≥0.58 were submitted for pathway analysis through MetaboAnalyst. We also elucidated potential metabolic signatures for TB-IRIS using a LASSO regression model.

RESULTS

At the study entry, we showed that the arachidonic acid and glycerophospholipid metabolism were altered in the TB-IRIS group. Sphingolipid and linoleic acid metabolism were the most affected pathways during the TB-IRIS window. LASSO modeling selected a set of 8 and 7 molecular features with the potential to predict TB-IRIS at study entry and during the TB-IRIS window, respectively.

CONCLUSION

This study suggests that the use of plasma metabolites may distinguish HIV-TB patients with and without TB-IRIS.