Mass-spectrometry-based metabolomics: limitations and recommendations for future progress with particular focus on nutrition research

Implementation of multiomic mass spectrometry approaches for the evaluation of human health following environmental exposure

Omics analyses collectively refer to the possibility of profiling genetic variants, RNA, epigenetic markers, proteins, lipids, and metabolites. The most common analytical approaches used for detecting molecules present within biofluids related to metabolism are vibrational spectroscopy techniques, represented by infrared, Raman, and nuclear magnetic resonance (NMR) spectroscopies and mass spectrometry (MS). Omics-based assessments utilizing MS are rapidly expanding and being applied to various scientific disciplines and clinical settings. Most of the omics instruments are operated by specialists in dedicated laboratories; however, the development of miniature portable omics has made the technology more available to users for field applications. Variations in molecular information gained from omics approaches are useful for evaluating human health following environmental exposure and the development and progression of numerous diseases. As MS technology develops so do statistical and machine learning methods for the detection of molecular deviations from personalized metabolism, which are correlated to altered health conditions, and they are intended to provide a multi-disciplinary overview for researchers interested in adding multiomic analysis to their current efforts. This includes an introduction to mass spectrometry-based omics technologies, current state-of-the-art capabilities and their respective strengths and limitations for surveying molecular information. Furthermore, we describe how knowledge gained from these assessments can be applied to personalized medicine and diagnostic strategies.

Applications of chromatographic methods in metabolomics: A review

Chromatography is a robust and reliable separation method that can use various stationary phases to separate complex mixtures commonly seen in metabolomics. This review examines the types of chromatography and stationary phases that have been used in targeted or untargeted metabolomics with methods such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. General considerations for sample pretreatment and separations in metabolomics are considered, along with the various supports and separation formats for chromatography that have been used in such work. The types of liquid chromatography (LC) that have been most extensively used in metabolomics will be examined, such as reversed-phase liquid chromatography and hydrophilic liquid interaction chromatography. In addition, other forms of LC that have been used in more limited applications for metabolomics (e.g., ion-exchange, size-exclusion, and affinity methods) will be discussed to illustrate how these techniques may be utilized for new and future research in this field. Multidimensional LC methods are also discussed, as well as the use of gas chromatography and supercritical fluid chromatography in metabolomics. In addition, the roles of chromatography in NMR- vs. MS-based metabolomics are considered. Applications are given within the field of metabolomics for each type of chromatography, along with potential advantages or limitations of these separation methods.

Circulating metabolic markers after surgery identify patients at risk for severe postoperative complications: a prospective cohort study in colorectal cancer

BACKGROUND

Early detection of postoperative complications after colorectal cancer (CRC) surgery is associated with improved outcomes. The aim was to investigate early metabolomics signatures capable to detect patients at risk for severe postoperative complications after CRC surgery.

MATERIALS AND METHODS

Prospective cohort study of patients undergoing CRC surgery from 2015 to 2018. Plasma samples were collected before and after surgery, and analyzed by mass spectrometry obtaining 188 metabolites and 21 ratios. Postoperative complications were registered with Clavien-Dindo Classification and Comprehensive Complication Index.

RESULTS

One hundred forty-six patients were included. Surgery substantially modified metabolome and metabolic changes after surgery were quantitatively associated with the severity of postoperative complications. The strongest positive relationship with both Clavien-Dindo and Comprehensive Complication Index (β=4.09 and 63.05, P <0.001) corresponded to kynurenine/tryptophan, against an inverse relationship with lysophosphatidylcholines (LPCs) and phosphatidylcholines (PCs). Patients with LPC18:2/PCa36:2 below the cut-off 0.084 µM/µM resulted in a sevenfold higher risk of major complications (OR=7.38, 95% CI: 2.82-21.25, P <0.001), while kynurenine/tryptophan above 0.067 µM/µM a ninefold (OR=9.35, 95% CI: 3.03-32.66, P <0.001). Hexadecanoylcarnitine below 0.093 µM displayed a 12-fold higher risk of anastomotic leakage-related complications (OR=11.99, 95% CI: 2.62-80.79, P =0.004).

CONCLUSION

Surgery-induced phospholipids and amino acid dysregulation is associated with the severity of postoperative complications after CRC surgery, including anastomotic leakage-related outcomes. The authors provide quantitative insight on metabolic markers, measuring vulnerability to postoperative morbidity that might help guide early decision-making and improve surgical outcomes.

Life-history stage determines the diet of ectoparasitic mites on their honey bee hosts

Ectoparasitic mites of the genera Varroa and Tropilaelaps have evolved to exclusively exploit honey bees as food sources during alternating dispersal and reproductive life history stages. Here we show that the primary food source utilized by Varroa destructor depends on the host life history stage. While feeding on adult bees, dispersing V. destructor feed on the abdominal membranes to access to the fat body as reported previously. However, when V. destructor feed on honey bee pupae during their reproductive stage, they primarily consume hemolymph, indicated by wound analysis, preferential transfer of biostains, and a proteomic comparison between parasite and host tissues. Biostaining and proteomic results were paralleled by corresponding findings in Tropilaelaps mercedesae, a mite that only feeds on brood and has a strongly reduced dispersal stage. Metabolomic profiling of V. destructor corroborates differences between the diet of the dispersing adults and reproductive foundresses. The proteome and metabolome differences between reproductive and dispersing V. destructor suggest that the hemolymph diet coincides with amino acid metabolism and protein synthesis in the foundresses while the metabolism of non-reproductive adults is tuned to lipid metabolism. Thus, we demonstrate within-host dietary specialization of ectoparasitic mites that coincides with life history of hosts and parasites.

Recent Advances and Techniques for Identifying Novel Antibacterial Targets

BACKGROUND

With the emergence of drug-resistant bacteria, the development of new antibiotics is urgently required. Target-based drug discovery is the most frequently employed approach for the drug development process. However, traditional drug target identification techniques are costly and time-consuming. As research continues, innovative approaches for antibacterial target identification have been developed which enabled us to discover drug targets more easily and quickly.

METHODS

In this review, methods for finding drug targets from omics databases have been discussed in detail including principles, procedures, advantages, and potential limitations. The role of phage-driven and bacterial cytological profiling approaches is also discussed. Moreover, current article demonstrates the advancements being made in the establishment of computational tools, machine learning algorithms, and databases for antibacterial target identification.

RESULTS

Bacterial drug targets successfully identified by employing these aforementioned techniques are described as well.

CONCLUSION

The goal of this review is to attract the interest of synthetic chemists, biologists, and computational researchers to discuss and improve these methods for easier and quicker development of new drugs.

The Potential Effects of Isoleucine Restricted Diet on Cognitive Impairment in High-Fat-Induced Obese Mice via Gut Microbiota-Brain Axis

SCOPE

Obesity induced by high-fat diet (HFD) can cause lipid metabolism disorders and cognitive impairment. Isoleucine restriction can effectively alleviate lipid metabolism disorders caused by HFD but the underlying mechanisms on cognition are unknown.

METHODS AND RESULTS

Thirty 3-month-old C57BL/6J mice are divided equally into the following groups: the control group, HFD group, and HFD Low Ile group (67% reduction in isoleucine in high fat feeds). Feeding for 11 weeks with behavioral testing, which shows that isoleucine restriction attenuates HFD-induced cognitive dysfunction. As observed by staining, isoleucine restriction inhibits HFD-induced neuronal damage and microglia activation. Furthermore, isoleucine restriction significantly increases the relative abundance of gut microbiota, decreases the proportion of Proteobacteria, and reduces the levels of lipopolysaccharide (LPS) in serum and brain. Isoleucine restriction reduces protein expression of TLR4/MyD88/NF-κB signaling pathway and inhibits upregulation of proinflammatory cytokine genes and protein expression in mice brain. In addition, isoleucine restriction significantly improves insulin resistance in the brain as well as synaptic plasticity impairment.

CONCLUSION

Isoleucine restriction may be a potential intervention to reduce HFD-induced cognitive impairment by altering gut microbiota, reducing neuroinflammation, insulin resistance, and improving synaptic plasticity in mice brain.

Metabolomic Profiling of Recombinant Erythropoietin (rHuEpo) in Trained Caucasian Athletes

OBJECTIVE

Recombinant human erythropoietin (rHuEpo) is prohibited by the World Anti-Doping Agency but remains the drug of choice for many cheating athletes wishing to evade detection using current methods. The aim of this study was to identify a robust metabolomics signature of rHuEpo using an untargeted approach in blood (plasma and serum) and urine.

DESIGN

Longitudinal study.

SETTING

University of Glasgow.

PARTICIPANTS

Eighteen male participants regularly engaged in predominantly endurance-based activities, such as running, cycling, swimming, triathlon, and team sports, were recruited.

INTERVENTIONS

Each participant received 50 IU·kg -1 body mass of rHuEpo subcutaneously every 2 days for 4 weeks. Samples were collected at baseline, during rHuEpo administration (over 4 weeks) and after rHuEpo administration (week 7-10). The samples were analyzed using hydrophilic interaction liquid chromatography mass spectrometry.

MAIN OUTCOME MEASURES

Significant metabolic signatures of rHuEpo administration were identified in all biofluids tested in this study.

RESULTS

Regarding metabolomics data, 488 plasma metabolites, 694 serum metabolites, and 1628 urinary metabolites were identified. Reproducible signatures of rHuEpo administration across all biofluids included alterations of pyrimidine metabolism (orotate and dihydroorotate) and acyl-carnitines (palmitoyl-carnitine and elaidic carnitine), metabolic pathways that are associated with erythropoiesis or erythrocyte membrane function, respectively.

CONCLUSIONS

Preliminary metabolic signatures of rHuEpo administration were identified. Future studies will be required to validate these encouraging results in independent cohorts and with orthogonal techniques, such as integration of our data with signatures derived from other "omics" analyses of rHuEpo administration (eg, transcriptomics).

Rapid metabolic fingerprinting with the aid of chemometric models to identify authenticity of natural medicines: Turmeric, Ocimum, and Withania somnifera study

Herbal medicines are popular natural medicines that have been used for decades. The use of alternative medicines continues to expand rapidly across the world. The World Health Organization suggests that quality assessment of natural medicines is essential for any therapeutic or health care applications, as their therapeutic potential varies between different geographic origins, plant species, and varieties. Classification of herbal medicines based on a limited number of secondary metabolites is not an ideal approach. Their quality should be considered based on a complete metabolic profile, as their pharmacological activity is not due to a few specific secondary metabolites but rather a larger group of bioactive compounds. A holistic and integrative approach using rapid and nondestructive analytical strategies for the screening of herbal medicines is required for robust characterization. In this study, a rapid and effective quality assessment system for geographical traceability, species, and variety-specific authenticity of the widely used natural medicines turmeric, Ocimum, and Withania somnifera was investigated using Fourier transform near-infrared (FT-NIR) spectroscopy-based metabolic fingerprinting. Four different geographical origins of turmeric, five different Ocimum species, and three different varieties of roots and leaves of Withania somnifera were studied with the aid of machine learning approaches. Extremely good discrimination (R2 > 0.98, Q2 > 0.97, and accuracy = 1.0) with sensitivity and specificity of 100% was achieved using this metabolic fingerprinting strategy. Our study demonstrated that FT-NIR-based rapid metabolic fingerprinting can be used as a robust analytical method to authenticate several important medicinal herbs.

The Expectation and Reality of the HepG2 Core Metabolic Profile

To represent the composition of small molecules circulating in HepG2 cells and the formation of the "core" of characteristic metabolites that often attract researchers' attention, we conducted a meta-analysis of 56 datasets obtained through metabolomic profiling via mass spectrometry and NMR. We highlighted the 288 most commonly studied compounds of diverse chemical nature and analyzed metabolic processes involving these small molecules. Building a complete map of the metabolome of a cell, which encompasses the diversity of possible impacts on it, is a severe challenge for the scientific community, which is faced not only with natural limitations of experimental technologies, but also with the absence of transparent and widely accepted standards for processing and presenting the obtained metabolomic data. Formulating our research design, we aimed to reveal metabolites crucial to the Hepg2 cell line, regardless of all chemical and/or physical impact factors. Unfortunately, the existing paradigm of data policy leads to a streetlight effect. When analyzing and reporting only target metabolites of interest, the community ignores the changes in the metabolomic landscape that hide many molecular secrets.

Exhaled volatile organic compounds for diagnosis and monitoring of asthma

The asthmatic inflammatory process results in the generation of volatile organic compounds (VOCs), which are subsequently secreted by the airways. The study of these elements through gas chromatography-mass spectrometry (GC-MS), which can identify individual molecules with a discriminatory capacity of over 85%, and electronic-Nose (e-NOSE), which is able to perform a quick onboard pattern-recognition analysis of VOCs, has allowed new prospects for non-invasive analysis of the disease in an "omics" approach. In this review, we aim to collect and compare the progress made in VOCs analysis using the two methods and their instrumental characteristics. Studies have described the potential of GC-MS and e-NOSE in a multitude of relevant aspects of the disease in both children and adults, as well as differential diagnosis between asthma and other conditions such as wheezing, cystic fibrosis, COPD, allergic rhinitis and last but not least, the accuracy of these methods compared to other diagnostic tools such as lung function, FeNO and eosinophil count. Due to significant limitations of both methods, it is still necessary to improve and standardize techniques. Currently, e-NOSE appears to be the most promising aid in clinical practice, whereas GC-MS, as the gold standard for the structural analysis of molecules, remains an essential tool in terms of research for further studies on the pathophysiologic pathways of the asthmatic inflammatory process. In conclusion, the study of VOCs through GC-MS and e-NOSE appears to hold promise for the non-invasive diagnosis, assessment, and monitoring of asthma, as well as for further research studies on the disease.

Nutritional Metabolomics in Diet-Breast Cancer Relations: Current Research, Challenges, and Future Directions-A Review

Breast cancer is one of the most common types of cancer in women worldwide, and its incidence is increasing. Diet has been identified as a modifiable risk factor for breast cancer, but the complex interplay between diet, metabolism, and cancer development is not fully understood. Nutritional metabolomics is a rapidly evolving field that can provide insights into the metabolic changes associated with dietary factors and their impact on breast cancer risk. The review's objective is to provide a comprehensive overview of the current research on the application of nutritional metabolomics in understanding the relationship between diet and breast cancer. The search strategy involved querying several electronic databases, including PubMed, Scopus, Web of Science, and Google Scholar. The search terms included combinations of relevant keywords such as "nutritional metabolomics", "diet", "breast cancer", "metabolites", and "biomarkers". In this review, both in vivo and in vitro studies were included, and we summarize the current state of knowledge on the role of nutritional metabolomics in understanding the diet-breast cancer relationship, including identifying specific metabolites and metabolic pathways associated with breast cancer risk. We also discuss the challenges associated with nutritional metabolomics research, including standardization of analytical methods, interpretation of complex data, and integration of multiple-omics approaches. Finally, we highlight future directions for nutritional metabolomics research in studying diet-breast cancer relations, including investigating the role of gut microbiota and integrating multiple-omics approaches. The application of nutritional metabolomics in the study of diet-breast cancer relations, including 2-amino-4-cyano butanoic acid, piperine, caprate, rosten-3β,17β-diol-monosulfate, and γ-carboxyethyl hydrochroman, among others, holds great promise for advancing our understanding of the role of diet in breast cancer development and identifying personalized dietary recommendations for breast cancer prevention, control, and treatment.

Fingerprinting Chemical Markers in the Mediterranean Orange Blossom Honey: UHPLC-HRMS Metabolomics Study Integrating Melissopalynological Analysis, GC-MS and HPLC-PDA-ESI/MS

(1) Background: Citrus honey constitutes a unique monofloral honey characterized by a distinctive aroma and unique taste. The non-targeted chemical analysis can provide pivotal information on chemical markers that differentiate honey based on its geographical and botanical origin. (2) Methods: Within the PRIMA project "PLANT-B", a metabolomics workflow was established to unveil potential chemical markers of orange blossom honey produced in case study areas of Egypt, Italy, and Greece. In some of these areas, aromatic medicinal plants were cultivated to enhance biodiversity and attract pollinators. The non-targeted chemical analysis and metabolomics were conducted using ultra-high-performance liquid chromatography high-resolution mass spectrometry (UHPLC-HRMS). (3) Results: Forty compounds were disclosed as potential chemical markers, enabling the differentiation of the three orange blossom honeys according to geographical origin. Italian honey showed a preponderance of flavonoids, while in Greek honey, terpenoids and iridoids were more abundant than flavonoids, except for hesperidin. In Egyptian honey, suberic acid and a fatty acid ester derivative emerged as chemical markers. New, for honey, furan derivatives were identified using GC-MS in Greek samples. (4) Conclusions: The application of UHPLC-HRMS metabolomics combined with an elaborate melissopalynological analysis managed to unveil several potential markers of Mediterranean citrus honey potentially associated with citrus crop varieties and the local indigenous flora.

Detection and analysis of chiral molecules as disease biomarkers

The chirality of small metabolic molecules is important in controlling physiological processes and indicating the health status of humans. Abnormal enantiomeric ratios of chiral molecules in biofluids and tissues occur in many diseases, including cancers and kidney and brain diseases. Thus, chiral small molecules are promising biomarkers for disease diagnosis, prognosis, adverse drug-effect monitoring, pharmacodynamic studies and personalized medicine. However, it remains difficult to achieve cost-effective and reliable analysis of small chiral molecules in clinical procedures, in part owing to their large variety and low concentration. In this Review, we describe current and emerging techniques that detect and quantify small-molecule enantiomers and their biological importance.

Data Science and Plant Metabolomics

The study of plant metabolism is one of the most complex tasks, mainly due to the huge amount and structural diversity of metabolites, as well as the fact that they react to changes in the environment and ultimately influence each other. Metabolic profiling is most often carried out using tools that include mass spectrometry (MS), which is one of the most powerful analytical methods. All this means that even when analyzing a single sample, we can obtain thousands of data. Data science has the potential to revolutionize our understanding of plant metabolism. This review demonstrates that machine learning, network analysis, and statistical modeling are some techniques being used to analyze large quantities of complex data that provide insights into plant development, growth, and how they interact with their environment. These findings could be key to improving crop yields, developing new forms of plant biotechnology, and understanding the relationship between plants and microbes. It is also necessary to consider the constraints that come with data science such as quality and availability of data, model complexity, and the need for deep knowledge of the subject in order to achieve reliable outcomes.

Metabolomics facilitates the discovery of metabolic profiles and pathways for myopia: A systematic review

BACKGROUND

Myopia is one of the major eye disorders and the global burden is increasing rapidly. Our purpose is to systematically summarize potential metabolic biomarkers and pathways in myopia to facilitate the understanding of disease mechanisms as well as the discovery of novel therapeutic measures.

METHODS

Myopia-related metabolomics studies were searched in electronic databases of PubMed and Web of Science until June 2021. Information regarding clinical and demographic characteristics of included studies and metabolomics findings were extracted. Myopia-related metabolic pathways were analysed for differential metabolic profiles, and the quality of included studies was assessed based on the QUADOMICS tool. Pathway analyses of differential metabolites were performed using bioinformatics tools and online software such as the Metaboanalyst 5.0.

RESULTS

The myopia-related metabolomics studies included in this study consisted of seven human and two animal studies. The results of the study quality assessment showed that studies were all phase I studies and all met the evaluation criteria of 70% or more. The myopia-control serum study identified 23 differential metabolites with the Sphingolipid metabolism pathway beings enriched. The high myopia-cataract aqueous humour study identified 40 differential metabolites with the Arginine biosynthesis pathway being enriched. The high myopia-control serum study identified 43 differential metabolites and 4 pathways were significantly associated with metabolites including Citrate cycle; Alanine, aspartate and glutamate metabolism; Glyoxylate and dicarboxylate metabolism; Biosynthesis of unsaturated fatty acids (all P value < 0.05).

CONCLUSIONS

This study summarizes potential metabolic biomarkers and pathways in myopia, providing new clues to elucidate disease mechanisms.

Filling gaps in animal welfare assessment through metabolomics

Sustainability has become a central issue in Italian livestock systems driving food business operators to adopt high standards of production concerning animal husbandry conditions. Meat sector is largely involved in this ecological transition with the introduction of new label claims concerning the defense of animal welfare (AW). These new guarantees referred to AW provision require new tools for the purpose of authenticity and traceability to assure meat supply chain integrity. Over the years, European Union (EU) Regulations, national, and international initiatives proposed provisions and guidelines for assuring AW introducing requirements to be complied with and providing tools based on scoring systems for a proper animal status assessment. However, the comprehensive and objective assessment of the AW status remains challenging. In this regard, phenotypic insights at molecular level may be investigated by metabolomics, one of the most recent high-throughput omics techniques. Recent advances in analytical and bioinformatic technologies have led to the identification of relevant biomarkers involved in complex clinical phenotypes of diverse biological systems suggesting that metabolomics is a key tool for biomarker discovery. In the present review, the Five Domains model has been employed as a vademecum describing AW. Starting from the individual Domains-nutrition (I), environment (II), health (III), behavior (IV), and mental state (V)-applications and advances of metabolomics related to AW setting aimed at investigating phenotypic outcomes on molecular scale and elucidating the biological routes most perturbed from external solicitations, are reviewed. Strengths and weaknesses of the current state-of-art are highlighted, and new frontiers to be explored for AW assessment throughout the metabolomics approach are argued. Moreover, a detailed description of metabolomics workflow is provided to understand dos and don'ts at experimental level to pursue effective results. Combining the demand for new assessment tools and meat market trends, a new cross-strategy is proposed as the promising combo for the future of AW assessment.

Longitudinal associations of plasma metabolites with persistent fatigue among colorectal cancer survivors up to 2 years after treatment

The underlying biological mechanisms causing persistent fatigue complaints after colorectal cancer treatment need further investigation. We investigated longitudinal associations of circulating concentrations of 138 metabolites with total fatigue and subdomains of fatigue between 6 weeks and 2 years after colorectal cancer treatment. Among stage I-III colorectal cancer survivors (n = 252), blood samples were obtained at 6 weeks, and 6, 12 and 24 months posttreatment. Total fatigue and fatigue subdomains were measured using a validated questionnaire. Tandem mass spectrometry was applied to measure metabolite concentrations (BIOCRATES AbsoluteIDQp180 kit). Confounder-adjusted longitudinal associations were analyzed using linear mixed models, with false discovery rate (FDR) correction. We assessed interindividual (between-participant differences) and intraindividual longitudinal associations (within-participant changes over time). In the overall longitudinal analysis, statistically significant associations were observed for 12, 32, 17 and three metabolites with total fatigue and the subscales "fatigue severity," "reduced motivation" and "reduced activity," respectively. Specifically, higher concentrations of several amino acids, lysophosphatidylcholines, diacylphosphatidylcholines, acyl-alkylphosphatidylcholines and sphingomyelins were associated with less fatigue, while higher concentrations of acylcarnitines were associated with more fatigue. For "fatigue severity," associations appeared mainly driven by intraindividual associations, while for "reduced motivation" stronger interindividual associations were found. We observed longitudinal associations of several metabolites with total fatigue and fatigue subscales, and that intraindividual changes in metabolites over time were associated with fatigue severity. These findings point toward inflammation and an impaired energy metabolism due to mitochondrial dysfunction as underlying mechanisms. Mechanistic studies are necessary to determine whether these metabolites could be targets for intervention.

Tool and techniques study to plant microbiome current understanding and future needs: an overview

Microorganisms are present in the universe and they play role in beneficial and harmful to human life, society, and environments. Plant microbiome is a broad term in which microbes are present in the rhizo, phyllo, or endophytic region and play several beneficial and harmful roles with the plant. To know of these microorganisms, it is essential to be able to isolate purification and identify them quickly under laboratory conditions. So, to improve the microbial study, several tools and techniques such as microscopy, rRNA, or rDNA sequencing, fingerprinting, probing, clone libraries, chips, and metagenomics have been developed. The major benefits of these techniques are the identification of microbial community through direct analysis as well as it can apply in situ. Without tools and techniques, we cannot understand the roles of microbiomes. This review explains the tools and their roles in the understanding of microbiomes and their ecological diversity in environments.

Psychological distress and metabolomic markers: A systematic review of posttraumatic stress disorder, anxiety, and subclinical distress

Psychological distress can be conceptualized as an umbrella term encompassing symptoms of depression, anxiety, posttraumatic stress disorder (PTSD), or stress more generally. A systematic review of metabolomic markers associated with distress has the potential to reveal underlying molecular mechanisms linking distress to adverse health outcomes. The current systematic review extends prior reviews of clinical depressive disorders by synthesizing 39 existing studies that examined metabolomic markers for PTSD, anxiety disorders, and subclinical psychological distress in biological specimens. Most studies were based on small sets of pre-selected candidate metabolites, with few metabolites overlapping between studies. Vast heterogeneity was observed in study design and inconsistent patterns of association emerged between distress and metabolites. To gain a more robust understanding of distress and its metabolomic signatures, future research should include 1) large, population-based samples and longitudinal assessments, 2) replication and validation in diverse populations, 3) and agnostic metabolomic strategies profiling hundreds of targeted and nontargeted metabolites. Addressing these research priorities will improve the scope and reproducibility of future metabolomic studies of psychological distress.

1 H-NMR Metabolomics Profiling and Volatile Content of 'Hoja Santa' (Piper auritum Kunth): A Millenary Edible Plant Consumed in Mexico

The leaves of Piper auritum Kunth ('Hoja Santa') have been consumed for centuries by native people of central and southern Mexico as a fresh vegetable or condiment. Herein we present the result of the ¹ H-NMR metabolomics profiling of three accessions of P. auritum harvested in three different provinces of Mexico (Puebla, Tlaxcala, and Oaxaca). The volatile content associated with the flavoring properties of the plant was also determined by GC/MS. The non-targeted metabolome of these samples revealed that P. auritum is a source of free essential amino acids such as isoleucine, leucine, threonine, valine, histidine, phenylalanine, and tryptophan as well as organic acids, free monosaccharides, and valuable nutraceuticals such as trigonelline, Myo-inositol, betaine, and choline. Principal component analysis and orthogonal partial least squares discriminated analysis of the metabolites found in P. auritum revealed trigonelline as the main differential compound found in the three studied accessions, suggesting this metabolite as a possible chemical marker. According to these statistical approaches, 60 % of the differential metabolites were provided by Oaxaca samples, suggesting that leaves harvested in this province have better (p<0.05) nutritional properties than the other samples analyzed. Nevertheless, the high abundance of the anti-nutrient safrole (90 %) in the volatile fraction, advises the potential toxicity of P. auritum consumed in Oaxaca. On the other hand, samples harvested in the northern highlands of Puebla, contained the lowest levels of safrole (30 %) and acceptable levels of nutrients and nutraceuticals including choline. From the three groups of studied plants, those harvested in the northern highlands from Puebla, could be considered safer for human consumption than the other analyzed accessions.

Advances in experimental and computational methodologies for the study of microbial-surface interactions at different omics levels

The study of the biological response of microbial cells interacting with natural and synthetic interfaces has acquired a new dimension with the development and constant progress of advanced omics technologies. New methods allow the isolation and analysis of nucleic acids, proteins and metabolites from complex samples, of interest in diverse research areas, such as materials sciences, biomedical sciences, forensic sciences, biotechnology and archeology, among others. The study of the bacterial recognition and response to surface contact or the diagnosis and evolution of ancient pathogens contained in archeological tissues require, in many cases, the availability of specialized methods and tools. The current review describes advances in in vitro and in silico approaches to tackle existing challenges (e.g., low-quality sample, low amount, presence of inhibitors, chelators, etc.) in the isolation of high-quality samples and in the analysis of microbial cells at genomic, transcriptomic, proteomic and metabolomic levels, when present in complex interfaces. From the experimental point of view, tailored manual and automatized methodologies, commercial and in-house developed protocols, are described. The computational level focuses on the discussion of novel tools and approaches designed to solve associated issues, such as sample contamination, low quality reads, low coverage, etc. Finally, approaches to obtain a systems level understanding of these complex interactions by integrating multi omics datasets are presented.

Current and emerging target identification methods for novel antimalarials

New antimalarial compounds with novel mechanisms of action are urgently needed to combat the recent rise in antimalarial drug resistance. Phenotypic high-throughput screens have proven to be a successful method for identifying new compounds, however, do not provide mechanistic information about the molecular target(s) responsible for antimalarial action. Current and emerging target identification methods such as in vitro resistance generation, metabolomics screening, chemoproteomic approaches and biophysical assays measuring protein stability across the whole proteome have successfully identified novel drug targets. This review provides an overview of these techniques, comparing their strengths and weaknesses and how they can be utilised for antimalarial target identification.

Cell-Based Metabolomics Approach for Anticipating and Investigating Cytotoxicity of Gold Nanorods

Despite the increasing application of gold nanoparticles, there has been little assessment of biological system toxicity to evaluate their potential impact on human health. In this study, the human hepatoma cell line (Hep G2) was used in a metabolomics approach to study the effects of shape, time, and dose of gold nanorods (GNRs). Using optimized parameters for chromatography and mass spectrometry, the metabolites detected by GC-MS were processed with MS DIAL and identified with Fiehnlib. Key metabolic pathways affected by GNRs were identified by endo-metabolic profiling of cells mixed with GNRs of varying shape while varying the dose and time of exposure. The shape of GNRs affected cytotoxicity, and short GNR (GNR-S) triggered disorder of cell metabolism. High concentrations of GNRs caused more significant toxicity. The cytotoxicity and bioTEM results illustrated that the mitochondria toxicity, as the main cytotoxicity of GNRs, caused declining cytoprotective ability. The mitochondrial dysfunction disrupted alanine, aspartate, glutamate, arginine, and proline metabolism, with amino acid synthesis generally downregulated. However, the efflux function of cells can exclude GNRs extracellularly within 24 h, resulting in reduced cell mitochondrial metabolic toxicity and allowing metabolic disorders to recover to normal function.

The metabolic adaptation of the adult offspring after maternal high-dosed folic acid supplementation based on the proteomics and metabolomics in rats

BACKGROUND

The onset of complex diseases at a later stage of life has been evidently linked with maternal folic acid (FA) ingestion. However, little is known regarding the underlying molecule fingerprints of the offspring.

METHODS

We integrated proteomics-metabolomics profiles and analyzed the influence of maternal FA supplementation on the metabolism of the adult offspring rats. 20 pregnant female rats were randomly assigned to a FA supplementation (FolS group, 10 mg/kg FA) or control group (2 mg/kg FA respectively).

RESULTS

Such omics approach revealed that dopaminergic synapse pathway, tricarboxylic acid cycle and neural development related metabolites such as glutamic acid and γ-aminobutyric acid were significantly up-regulated in the FolS group, whereas pyruvic acid, oxalic acid and adipic acid was reduced.

CONCLUSIONS

Maternal FA supplementation can cause the alterations of metabolites and protein in the offspring rats.

Diet and BMI Correlate with Metabolite Patterns Associated with Aggressive Prostate Cancer

Three metabolite patterns have previously shown prospective inverse associations with the risk of aggressive prostate cancer within the European Prospective Investigation into Cancer and Nutrition (EPIC). Here, we investigated dietary and lifestyle correlates of these three prostate cancer-related metabolite patterns, which included: 64 phosphatidylcholines and three hydroxysphingomyelins (Pattern 1), acylcarnitines C18:1 and C18:2, glutamate, ornithine, and taurine (Pattern 2), and 8 lysophosphatidylcholines (Pattern 3). In a two-stage cross-sectional discovery (n = 2524) and validation (n = 518) design containing 3042 men free of cancer in EPIC, we estimated the associations of 24 dietary and lifestyle variables with each pattern and the contributing individual metabolites. Associations statistically significant after both correction for multiple testing (False Discovery Rate = 0.05) in the discovery set and at p < 0.05 in the validation set were considered robust. Intakes of alcohol, total fish products, and its subsets total fish and lean fish were positively associated with Pattern 1. Body mass index (BMI) was positively associated with Pattern 2, which appeared to be driven by a strong positive BMI-glutamate association. Finally, both BMI and fatty fish were inversely associated with Pattern 3. In conclusion, these results indicate associations of fish and its subtypes, alcohol, and BMI with metabolite patterns that are inversely associated with risk of aggressive prostate cancer.

Digital models in biotechnology: Towards multi-scale integration and implementation

Industrial biotechnology encompasses a large area of multi-scale and multi-disciplinary research activities. With the recent megatrend of digitalization sweeping across all industries, there is an increased focus in the biotechnology industry on developing, integrating and applying digital models to improve all aspects of industrial biotechnology. Given the rapid development of this field, we systematically classify the state-of-art modelling concepts applied at different scales in industrial biotechnology and critically discuss their current usage, advantages and limitations. Further, we critically analyzed current strategies to couple cell models with computational fluid dynamics to study the performance of industrial microorganisms in large-scale bioprocesses, which is of crucial importance for the bio-based production industries. One of the most challenging aspects in this context is gathering intracellular data under industrially relevant conditions. Towards comprehensive models, we discuss how different scale-down concepts combined with appropriate analytical tools can capture intracellular states of single cells. We finally illustrated how the efforts could be used to develop digitals models suitable for both cell factory design and process optimization at industrial scales in the future.

Metabolomics for Agricultural Waste Valorization: Shifting Toward a Sustainable Bioeconomy

Agriculture has been considered as a fundamental industry for human survival since ancient times. Local and traditional agriculture are based on circular sustainability models, which produce practically no waste. However, owing to population growth and current market demands, modern agriculture is based on linear and large-scale production systems, generating tons of organic agricultural waste (OAW), such as rejected or inedible plant tissues (shells, peels, stalks, etc.). Generally, this waste accumulates in landfills and creates negative environmental impacts. The plant kingdom is rich in metabolic diversity, harboring over 200,000 structurally distinct metabolites that are naturally present in plants. Hence, OAW is considered to be a rich source of bioactive compounds, including phenolic compounds and secondary metabolites that exert a wide range of health benefits. Accordingly, OAW can be used as extraction material for the discovery and recovery of novel functional compounds that can be reinserted into the production system. This approach would alleviate the undesired environmental impacts of OAW accumulation in landfills, while providing added value to food, pharmaceutical, cosmetic, and nutraceutical products and introducing a circular economic model in the modern agricultural industry. In this regard, metabolomics-based approaches have gained increasing interest in the agri-food sector for a variety of applications, including the rediscovery of bioactive compounds, owing to advances in analytical instrumentation and data analytics platforms. This mini review summarizes the major aspects regarding the identification of novel bioactive compounds from agricultural waste, focusing on metabolomics as the main tool.

A Metabolomic Analysis of Cirrhotic Ascites

Ascites is a common complication of decompensated liver cirrhosis, and yet relatively little is known about its biochemical composition. We conducted two metabolomic investigations, comparing the profile of ascites from 33 cirrhotic patients and postoperative peritoneal drainage fluid from 33 surgical patients (Experiment 1). The profile of paired ascites and plasma was also compared in 17 cirrhotic patients (Experiment 2). Gas chromatography−mass spectrometry-based metabolomics identified 29 metabolites that significantly characterized ascites fluid, whether postoperative drainage fluid or plasma were used as controls. Ten elevated amino acids (glutamine, proline, histidine, tyrosine, glycine, valine, threonine, methionine, lysine, phenylalanine) and seven diminished lipids (laurate, myristate, palmitate, oleate, vaccenate, stearate, cholesterol) largely comprised the cirrhotic ascites metabolomic phenotype that differed significantly (adjusted p < 0.002 to 0.03) from peritoneal drainage fluid or plasma. The pattern of upregulated amino acids in cirrhotic ascites did not indicate albumin proteolysis by peritoneal bacteria. Bidirectional clustering showed that the more severe the cirrhosis, the lower the lipid concentration in ascitic fluid. The metabolomic compartment of ascites in patients with decompensated cirrhosis is characterized by increased amino acids and decreased lipids. These novel findings have potential relevance for diagnostic purposes.

Molecular signatures of tumor progression in pancreatic adenocarcinoma identified by energy metabolism characteristics

BACKGROUND

In this study, we performed a molecular evaluation of primary pancreatic adenocarcinoma (PAAD) based on the comprehensive analysis of energy metabolism-related gene (EMRG) expression profiles.

METHODS

Molecular subtypes were identified by nonnegative matrix clustering of 565 EMRGs. An overall survival (OS) predictive gene signature was developed and internally and externally validated based on three online PAAD datasets. Hub genes were identified in molecular subtypes by weighted gene correlation network analysis (WGCNA) coexpression algorithm analysis and considered as prognostic genes. LASSO cox regression was conducted to establish a robust prognostic gene model, a four-gene signature, which performed better in survival prediction than four previously reported models. In addition, a novel nomogram constructed by combining clinical features and the 4-gene signature showed high-confidence clinical utility. According to gene set enrichment analysis (GSEA), gene sets related to the high-risk group participate in the neuroactive ligand receptor interaction pathway.

CONCLUSIONS

In summary, EMRG-based molecular subtypes and prognostic gene models may provide a novel research direction for patient stratification and trials of targeted therapies.

Omics-based ecosurveillance uncovers the influence of estuarine macrophytes on sediment microbial function and metabolic redundancy in a tropical ecosystem

Vertical zonation within estuarine ecosystems can strongly influence microbial diversity and function by regulating competition, predation, and environmental stability. The degree to which microbial communities exhibit horizontal patterns through an estuary has received comparatively less attention. Here, we take a multi-omics ecosurveillance approach to study environmental gradients created by the transition between dominant vegetation types along a near pristine tropical river system (Wenlock River, Far North Queensland, Australia). The study sites included intertidal mudflats fringed by saltmarsh, mangrove or mixed soft substrata habitats. Collected sediments were analyzed for eukaryotes and prokaryotes using small sub-unit (SSU) rRNA gene amplicons to profile the relative taxonomic composition. Central carbon metabolism metabolites and other associated organic polar metabolites were analyzed using established metabolomics-based approaches, coupled with total heavy metals analysis. Eukaryotic taxonomic information was found to be more informative of habitat type. Bacterial taxonomy and community composition also showed habitat-specificity, with phyla Proteobacteria and Cyanobacteria strongly linked to mangroves and saltmarshes, respectively. In contrast, metabolite profiling was critical for understanding the biochemical pathways and expressed functional outputs in these systems that were tied to predicted microbial gene function (16S rRNA). A high degree of metabolic redundancy was observed in the bacterial communities, with the metabolomics data suggesting varying degrees of metabolic criticality based on habitat type. The predicted functions of the bacterial taxa combined with annotated metabolites accounted for the conservative perspective of microbial community redundancy against the putative metabolic pathway impacts in the metabolomics data. Coupling these data demonstrates that habitat-mediated estuarine gradients drive patterns of community diversity and metabolic function and highlights the real redundancy potential of habitat microbiomes. This information is useful as a point of comparison for these sensitive ecosystems and provides a framework for identifying potentially vulnerable or at-risk systems before they are significantly degraded.

Comprehensive molecular characterization and identification of prognostic signature in stomach adenocarcinoma on the basis of energy-metabolism-related genes

BACKGROUND

Stomach adenocarcinoma (STAD) is a leading cause of cancer deaths, but its molecular and prognostic characteristics has never been fully illustrated.

AIM

To describe a molecular evaluation of primary STAD and develop new therapies and identify promising prognostic signatures.

METHODS

We describe a comprehensive molecular evaluation of primary STAD based on comprehensive analysis of energy-metabolism-related gene (EMRG) expression profiles.

RESULTS

On the basis of 86 EMRGs that were significantly associated to patients’ progression-free survival (PFS), we propose a molecular classification dividing gastric cancer into two subtypes: Cluster 1, most of which are young patients and display more immune and stromal cell components in tumor microenvironment and lower tumor priority; and Cluster 2, which show early stages and better PFS. Moreover, we construct a 6-gene signature that can classify the prognostic risk of patients after a three-phase training test and validation process. Compared with patients with low-risk score, patients with high-risk score had shorter overall survival. Furthermore, calibration and DCA analysis plots indicate the excellent predictive performance of the 6-gene signature, and which present higher robustness and clinical usability compared with three previous reported prognostic gene signatures. According to gene set enrichment analysis, gene sets related to the high-risk group were participated in the ECM receptor interaction and hedgehog signaling pathway.

CONCLUSION

Identification of the EMRG-based molecular subtypes and prognostic gene model provides a roadmap for patient stratification and trials of targeted therapies.

Developments in high-resolution mass spectrometric analyses of new psychoactive substances

The proliferation of new psychoactive substances (NPS) has necessitated the development and improvement of current practices for the detection and identification of known NPS and newly emerging derivatives. High-resolution mass spectrometry (HRMS) is quickly becoming the industry standard for these analyses due to its ability to be operated in data-independent acquisition (DIA) modes, allowing for the collection of large amounts of data and enabling retrospective data interrogation as new information becomes available. The increasing popularity of HRMS has also prompted the exploration of new ways to screen for NPS, including broad-spectrum wastewater analysis to identify usage trends in the community and metabolomic-based approaches to examine the effects of drugs of abuse on endogenous compounds. In this paper, the novel applications of HRMS techniques to the analysis of NPS is reviewed. In particular, the development of innovative data analysis and interpretation approaches is discussed, including the application of machine learning and molecular networking to toxicological analyses.

Identification of new glycosidic terpenols and norisoprenoids (aroma precursors) in C. arabica L. green coffee by using a high-resolution mass spectrometry database developed in grape metabolomics

Grape aroma precursors have been extensively studied and many glycosidically-bound terpenols and C₁₃-norisoprenoids were identified. Instead, these compounds were scarcely investigated in green Coffea arabica where just few glycosidic compounds were identified so far. By resorting to knowledge of glycoside aroma precursors in grape and the possibility to identify their structures using a high-resolution mass spectrometry database constructed for grape metabolomics, targeted investigation of glycoside precursors in green C. arabica from different geographical origins, was performed. High linalool hexose-pentose was found in all the investigated samples and hexosyl-pentoside derivatives of geraniol, linalooloxide and another linalool isomer, were identified. Moreover, two putative norisoprenoid glycosides were characterized. β-Damascenone was detected in the volatile fraction of the examined C. arabica coffees only after acid addition, however no signals of β-damascenone glycosides, were found. Findings suggests that this important aroma compound could form by hydrolysis and dehydration of a putative 3-hydroxy-β-damascone glycoside precursor identified for the first time in coffee. Aglycones released during the roasting process contribute to enrich the coffee aroma with their positive sensory notes and the identification of these glycosides can contribute to disclose the coffee biology including biochemical, physiological and genetic aspects.

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Glycoside aroma precursors in green C. arabica coffee are poorly known.

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A grape database was used to investigate aroma precursors in green C. arabica.

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Geraniol and linalooloxide glycosides were identified for first time in coffee.

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Linalool hexosyl-pentoside was particularly abundant in samples from Ethiopia.

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Putative 3-hydroxy-β-damascone and vomifoliol glycosides were characterized.

Flat Disc-Shaped Sampling Probe and Online Re-extraction Apparatus for Mass Spectrometric Analysis of Skin Metabolites: A Proof of Concept

Sweat analysis provides an alternative and noninvasive way of clinical diagnostics. However, sampling and transferring sweat-derived samples to analytical instruments is challenging. In this report, we demonstrate a method utilizing a flat disc-shaped sampling probe, and a compatible re-extraction apparatus coupled online with extractive electrospray ionization (EESI) mass spectrometry (MS). The probe enables sampling of metabolites from a skin area of ∼2.2 cm². The subsequent online re-extraction and analysis by EESI-MS further mitigates matrix effects caused by sweat components, thus eliminating sample preparation steps. The total analysis time is only 6 min. We have optimized the key parameters of the system, including flow rate of the nebulizing gas in ESI, pressure of the nebulizing gas in pneumatic sample nebulizer, flow rate of the solvent in ESI, and composition of extractant. The standard solutions (0.1 mL) were supplemented with 0.04 M sodium chloride to mimic the matrix effect normally observed in sweat samples. The method has been characterized with four chemical standards (positive-ion mode of histidine, leucine, urocanic acid; negative-ion mode of lactic acid). The limits of detection range from 1.09 to 95.9 nmol. We have further demonstrated the suitability of the method for analysis of sweat. An attempt was made to identify some of the recorded signals by product-ion scan and accurate/exact mass matching.

Plasma Metabolome Normalization in Rheumatoid Arthritis Following Initiation of Methotrexate and the Identification of Metabolic Biomarkers of Efficacy

Methotrexate (MTX) efficacy in the treatment of rheumatoid arthritis (RA) is variable and unpredictable, resulting in a need to identify biomarkers to guide drug therapy. This study evaluates changes in the plasma metabolome associated with response to MTX in RA with the goal of understanding the metabolic basis for MTX efficacy towards the identification of potential metabolic biomarkers of MTX response. Plasma samples were collected from healthy control subjects (n = 20), and RA patients initiating MTX therapy (n = 20, 15 mg/week) before and after 16 weeks of treatment. The samples were analyzed by a semi-targeted metabolomic analysis, and then analyzed by univariate and multivariate methods, as well as an enrichment analysis. An MTX response was defined as a clinically significant reduction in the disease activity score in 28 joints (DAS-28) of greater than 1.2; achievement of clinical remission, defined as a DAS-28 < 2.6, was also utilized as an additional measure of response. In this study, RA is associated with an altered plasma metabolome that is normalized following initiation of MTX therapy. Metabolite classes found to be altered in RA and corrected by MTX therapy were diverse and included triglycerides (p = 1.1 × 10⁻¹⁶), fatty acids (p = 8.0 × 10⁻¹²), and ceramides (p = 9.8 × 10⁻¹³). Stratification based on responses to MTX identified various metabolites differentially impacted in responders and non-responders including glucosylceramides (GlcCer), phosphatidylcholines (PC), sphingomyelins (SM), phosphatidylethanolamines (PE), choline, inosine, hypoxanthine, guanosine, nicotinamide, and itaconic acid (p < 0.05). In conclusion, RA is associated with significant alterations to the plasma metabolome displaying at least partial normalization following 16 weeks of MTX therapy. Changes in multiple metabolites were found to be associated with MTX efficacy, including metabolites involved in fatty acid/lipid, nucleotide, and energy metabolism.

Untargeted metabolomics analysis by gas chromatography/time-of-flight mass spectrometry of human serum from methamphetamine abusers

Methamphetamine (METH) abuse has become a global public health problem. However, the potential mechanisms involving METH-induced metabolic disorders have thus far remained poorly understood. Metabolomics can provide a clue for the cause of apparent changes and consequently be used to investigate the METH-induced dysregulation of metabolite expression and the mechanism of metabolic disorder mechanism. This laboratory investigation included 80 METH abusers and 80 healthy people. The serum metabolites were detected and analysed by gas chromatography/time-of-flight mass spectrometry. Raw data were processed with the software MS DIAL, which includes deconvolution, peak alignment and compound identification. The data matrix was processed by univariate and multivariate analyses for significant metabolite screening with the criteria of variable importance in projection values > 1, fold change > 1.5 and the t test (p value < 0.05). Significant differences in 16 metabolites (deoxycholic acid, cholic acid, hydroxylamine, etc.) in serum were found between the METH abuse group and the control group. Energy metabolic pathways and several amino acid metabolic pathways (alanine, aspartic acid and glutamate metabolism and tryptophan metabolism) were primarily involved. Further analysis indicated that the area under the receiver operating characteristic curve (AUC) was 0.998 for these 16 metabolites. Among the metabolites, three carbohydrates (d-ribose, cellobiose and maltotriose) had an AUC of 0.975, which were determined as potential markers of abuse. We observed metabolic disturbances in METH abusers, particularly perturbation in energy metabolism and amino acid metabolism, which can provide new insights into the search for biomarkers and the mechanisms underlying the adverse effects of METH on human health.

Retention time prediction in hydrophilic interaction liquid chromatography with graph neural network and transfer learning

The combination of retention time (RT), accurate mass and tandem mass spectra can improve the structural annotation in untargeted metabolomics. However, the incorporation of RT for metabolite identification has received less attention because of the limitation of available RT data, especially for hydrophilic interaction liquid chromatography (HILIC). Here, the Graph Neural Network-based Transfer Learning (GNN-TL) is proposed to train a model for HILIC RTs prediction. The graph neural network was pre-trained using an in silico HILIC RT dataset (pseudo-labeling dataset) with ∼306 K molecules. Then, the weights of dense layers in the pre-trained GNN (pre-GNN) model were fine-tuned by transfer learning using a small number of experimental HILIC RTs from the target chromatographic system. The GNN-TL outperformed the methods in Retip, including the Random Forest (RF), Bayesian-regularized neural network (BRNN), XGBoost, light gradient-boosting machine (LightGBM), and Keras. It achieved the lowest mean absolute error (MAE) of 38.6 s on the test set and 33.4 s on an additional test set. It has the best ability to generalize with a small performance difference between training, test, and additional test sets. Furthermore, the predicted RTs can filter out nearly 60% false positive candidates on average, which is valuable for the identification of compounds complementary to mass spectrometry.

A novel minimally invasive OFM technique with orthotopic transplantation of hUC-MSCs and in vivo monitoring of liver metabolic microenvironment in liver fibrosis treatment

Background

Mesenchymal stromal cells (MSCs) transplantation showed promising therapeutic results in liver fibrosis. However, efficient cell delivery method is urgently needed and the therapeutic mechanism remains unclear. This study focused on developing a minimally invasive open-flow microperfusion (OFM) technique, which combined orthotopic transplantation of human umbilical cord-derived (hUC)-MSCs to liver and in vivo monitoring of liver microenvironment in mice with CCl₄-induced liver fibrosis.

Methods

The therapeutic potential of OFM route was evaluated by comparing OFM with intravenous (IV) injection route in terms of hUC-MSCs engraftment at the fibrosis liver, liver histopathological features, liver function and fibrotic markers expression after hUC-MSCs administration. OFM was also applied to sample liver interstitial fluid in vivo, and subsequent metabolomic analysis was performed to investigate metabolic changes in liver microenvironment.

Results

Compared with IV route, OFM route caused more hUC-MSCs accumulation in the liver and was more effective in improving the remodeling of liver structure and reducing collagen deposition in fibrotic liver. OFM transplantation of hUC-MSCs reduced blood ALT, AST, ALP and TBIL levels and increased ALB levels, to a greater extent than IV route. And OFM route appeared to have a more pronounced effect on ameliorating the CCl₄-induced up-regulation of the fibrotic markers, such as α-SMA, collagen I and TGF-β. In vivo monitoring of liver microenvironment demonstrated the metabolic perturbations induced by pathological condition and treatment intervention. Two metabolites and eight metabolic pathways, which were most likely to be associated with the liver fibrosis progression, were regulated by hUC-MSCs administration.

Conclusion

The results demonstrated that the novel OFM technique would be useful for hUC-MSCs transplantation in liver fibrosis treatment and for monitoring of the liver metabolic microenvironment to explore the underlying therapeutic mechanisms.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13287-021-02599-w.

Non-Targeted LC-MS Metabolomics Approach towards an Authentication of the Geographical Origin of Grain Maize (Zea mays L.) Samples

Safety along the food and feed supply chain is an emerging topic and closely linked to the ability to analytical trace the geographical origin of food or feed. In this study, ultra-performance liquid chromatography coupled with electrospray ionization quadrupole-time-of-flight mass spectrometry was used to trace back the geographical origin of 151 grain maize (Zea mays L.) samples from seven countries using a high resolution non-targeted metabolomics approach. Multivariate data analysis and univariate statistics were used to identify promising marker features related to geographical origin. Classification using only 20 selected markers with the Random Forest algorithm led to 90.5% correctly classified samples with 100 times repeated 10-fold cross-validation. The selected markers were assigned to the class of triglycerides, diglycerides and phospholipids. The marker set was further evaluated for its ability to separate between one sample class and the rest of the dataset, yielding accuracies above 89%. This demonstrates the high potential of the non-polar metabolome to authenticate the geographic origin of grain maize samples. Furthermore, this suggests that focusing on only a few lipids with high potential for grain maize authentication could be a promising approach for later transfer of the method to routine analysis.

A Review on Chromatography-Mass Spectrometry Applications on Anthocyanin and Ellagitannin Metabolites of Blackberries and Raspberries

Berries have been widely assessed for their beneficial health effects, predominately due to their high (poly)phenol content of anthocyanins and ellagitannins. After ellagitannins and ellagic acid are metabolized by the gut microbiome, a class of compounds known as urolithins are produced, which exert potential advantageous health effects. Anthocyanins, on the other hand, undergo a complex metabolic pathway after their interaction with microbial and endogenous enzymes, forming a broad range of metabolites and catabolic products. In most cases, in vitro models and cell lines are used to generate metabolites, whereas their assessment in vivo is currently limited. Thus far, several analytical methods have been developed for the qualitative and quantitative analysis of phenolic metabolites in berries, including liquid chromatography, mass spectrometry, and other hyphenated techniques, and have been undoubtedly valuable tools for the detailed metabolite characterization and profiling. In this review, a compilation of studies providing information on the qualitative and quantitative analysis of (poly)phenol metabolites in blackberries and raspberries after the utilization of in vitro and in vivo methods is presented. The different analytical techniques employed are assessed, focusing on the fate of the produced metabolic compounds in order to provide evidence on their characteristics, formation, and beneficial effects.

Serum Amino Acids Patterns and 4-Year Sarcopenia Risk in Community-Dwelling Chinese Older Adults

INTRODUCTION

Dietary protein intake and serum amino acids (AAs) are factors controlling the rate of muscle protein synthesis and catabolism. This study examined the association between serum AAs patterns and incident sarcopenia in community-dwelling older adults.

METHODS

Chinese older adults in Hong Kong aged ≥65 years attended a health check at baseline and 4-year follow-up. At baseline, fasting blood was collected to measure 17 serum AAs. Serum AAs patterns were identified using principal component analysis. Dietary protein intake was assessed using a validated food frequency questionnaire. A composite score was computed by summing the principal component score and sex-standardized protein intake. Six composite scores representing each AAs pattern were available for each participant. Sarcopenia was defined using the updated version of the Asian Working Group for Sarcopenia. Crude and adjusted multiple logistic regressions were performed to examine the associations between each of the 6 composite scores and sarcopenia over 4 years. Results are presented as odds ratio (OR) and 95% confidence interval (CI). To address multiple testing, a Bonferroni correction was applied using a corrected significance level of p < 0.008 (α 0.05/6 patterns).

RESULTS

Data of 2,610 participants (mean age 71.6 years, 45.4% men) were available. In men, serum AAs patterns characterized by high branched-chain AAs (BCAAs) (OR 0.77, 95% CI 0.69-0.87, p < 0.001) and tyrosine, tryptophan, and phenylalanine (OR 0.79, 95% CI 0.71-0.89, p < 0.001) were significantly associated with a lower risk of sarcopenia over 4-year follow-up. After adjusting for confounders, the associations were no longer significant. In women, serum AAs patterns characterized by glutamine, glutamic acid, and methionine (OR 1.28, 95% CI 1.11-1.47, p = 0.001) and arginine, taurine, and serine (OR 1.20, 95% CI 1.06-1.35, p = 0.003) were associated with a higher risk of sarcopenia. After adjusting for confounders, serum AAs pattern characterized by high BCAAs (adjusted OR 1.52, 95% CI 1.25-1.86, p < 0.001) and arginine, taurine, and serine (adjusted OR 1.30, 95% CI 1.09-1.56, p = 0.004) were significantly associated with a higher risk of sarcopenia. No association between other AAs patterns with incident sarcopenia was found.

CONCLUSIONS

In community-dwelling Chinese older adults, serum AAs patterns characterized by high BCAAs and nonessential AAs (arginine, taurine, and serine) were associated with a higher risk of sarcopenia in women. Findings may allow identifying new targets for interventions.

Assessment of the amino acid profile in Thai patients with type 2 diabetes mellitus using liquid chromatography-mass spectrometry

Background

Type 2 diabetes mellitus (T2DM) is a global health problem. Early identification of those at risk is necessary to prevent its onset through lifestyle and pharmacologic interventions. T2DM is characterized by metabolic abnormalities, including protein metabolism. Evaluation of the amino acid profile might be beneficial for early assessment.

Methods

Liquid chromatography-mass spectrometry was performed to separate and quantify plasma amino acids from two groups of Thai individuals, patients with T2DM (n=103) and healthy individuals (n=104). Multivariate analysis was applied to compare free amino acid levels between groups. Subgroup analyses of patients with T2DM were performed to assess the association between amino acid profiles and important T2DM clinical characteristics.

Results

The multivariate analysis showed that glutamic acid was significantly associated with T2DM (OR 1.113, 95% CI 1.006 to 1.231) and results from the subgroup analyses showed that this correlation was significant in all subgroups of patients (p<0.05).

Conclusions

This finding needs to be confirmed in larger groups of patients with T2DM to explore glutamic acid as a biomarker for early prevention in particular at-risk groups. An in-depth understanding of the involvement of glutamic acid in T2DM could enhance our understanding of the disease and potentially provide novel interventions.

Longitudinal associations of physical activity with plasma metabolites among colorectal cancer survivors up to 2 years after treatment

We investigated longitudinal associations of moderate-to-vigorous physical activity (MVPA) and light-intensity physical activity (LPA) with plasma concentrations of 138 metabolites after colorectal cancer (CRC) treatment. Self-reported physical activity data and blood samples were obtained at 6 weeks, and 6, 12 and 24 months post-treatment in stage I-III CRC survivors (n = 252). Metabolite concentrations were measured by tandem mass spectrometry (BIOCRATES AbsoluteIDQp180 kit). Linear mixed models were used to evaluate confounder-adjusted longitudinal associations. Inter-individual (between-participant differences) and intra-individual associations (within-participant changes over time) were assessed as percentage difference in metabolite concentration per 5 h/week of MVPA or LPA. At 6 weeks post-treatment, participants reported a median of 6.5 h/week of MVPA (interquartile range:2.3,13.5) and 7.5 h/week of LPA (2.0,15.8). Inter-individual associations were observed with more MVPA being related (FDR-adjusted q-value < 0.05) to higher concentrations of arginine, citrulline and histidine, eight lysophosphatidylcholines, nine diacylphosphatidylcholines, 13 acyl-alkylphosphatidylcholines, two sphingomyelins, and acylcarnitine C10:1. No intra-individual associations were found. LPA was not associated with any metabolite. More MVPA was associated with higher concentrations of several lipids and three amino acids, which have been linked to anti-inflammatory processes and improved metabolic health. Mechanistic studies are needed to investigate whether these metabolites may affect prognosis.

Metabolomic Laboratory-Developed Tests: Current Status and Perspectives

Laboratory-developed tests (LDTs) are a subset of in vitro diagnostic devices, which the US Food and Drug Administration defines as "tests that are manufactured by and used within a single laboratory". The review describes the emergence and history of LDTs. The current state and development prospects of LDTs based on metabolomics are analyzed. By comparing LDTs with the scientific metabolomics study of human bio samples, the characteristic features of metabolomic LDT are shown, revealing its essence, strengths, and limitations. The possibilities for further developments and scaling of metabolomic LDTs and their potential significance for healthcare are discussed. The legal aspects of LDT regulation in the United States, European Union, and Singapore, demonstrating different approaches to this issue, are also provided. Based on the data presented in the review, recommendations were made on the feasibility and ways of further introducing metabolomic LDTs into practice.

Visceral Adiposity and Cancer: Role in Pathogenesis and Prognosis

The prevalence of being overweight and obese has been expanded dramatically in recent years worldwide. Obesity usually occurs when the energetic introit overtakes energy expenditure from metabolic and physical activity, leading to fat accumulation mainly in the visceral depots. Excessive fat accumulation represents a risk factor for many chronic diseases, including cancer. Adiposity, chronic low-grade inflammation, and hyperinsulinemia are essential factors of obesity that also play a crucial role in tumor onset. In recent years, several strategies have been pointed toward boundary fat accumulation, thus limiting the burden of cancer attributable to obesity. While remodeling fat via adipocytes browning seems a tempting prospect, lifestyle interventions still represent the main pathway to prevent cancer and enhance the efficacy of treatments. Specifically, the Mediterranean Diet stands out as one of the best dietary approaches to curtail visceral adiposity and, therefore, cancer risk. In this Review, the close relationship between obesity and cancer has been investigated, highlighting the biological mechanisms at the basis of this link. Finally, strategies to remodel fat, including browning and lifestyle interventions, have been taken into consideration as a major perspective to limit excess body weight and tumor onset.