Metabolomics technology and bioinformatics

Multiscale metabolomics techniques: Insights into neuroscience research

The field of metabolomics examines the overall composition and dynamic patterns of metabolites in living organisms. The primary methods used in metabolomics include liquid chromatography (LC), nuclear magnetic resonance (NMR), and mass spectrometry (MS) analysis. These methods enable the identification and examination of metabolite types and contents within organisms, as well as modifications to metabolic pathways and their connection to the emergence of diseases. Research in metabolomics has extensive value in basic and applied sciences. The field of metabolomics is growing quickly, with the majority of studies concentrating on biomedicine, particularly early disease diagnosis, therapeutic management of human diseases, and mechanistic knowledge of biochemical processes. Multiscale metabolomics is an approach that integrates metabolomics techniques at various scales, including the holistic, tissue, cellular, and organelle scales, to enable more thorough and in-depth studies of metabolic processes in organisms. Multiscale metabolomics can be combined with methods from systems biology and bioinformatics. In recent years, multiscale metabolomics approaches have become increasingly important in neuroscience research due to the nervous system's high metabolic demands. Multiscale metabolomics can offer novel concepts and approaches for the diagnosis, treatment, and development of medication for neurological illnesses in addition to a more thorough understanding of brain metabolism and nervous system function. In this review, we summarize the use of multiscale metabolomics techniques in neuroscience, address the promise and constraints of these techniques, and provide an overview of the metabolome and its applications in neuroscience.

Targeted analysis of organic acids with GC-MS/MS: challenges and prospects

GC-MS/MS combines the superior chromatographic resolution of GC with the specific and sensitive detection of tandem MS. On paper, it is an ideal system for the routine analyses of organic acids, yet very few studies have used and published such methods. This is likely due to several challenges highlighted in this communication. Briefly, the combination of EI ionization with MRM detection provides arguably insufficient specificity when targeting organic acids. Moreover, the narrow peaks generally produced by GC can lead to inaccurate quantification when the mass spectrometer's cycle time is too long. Potential solutions to these problems are discussed.

Application of molecular networking to improve the compound annotation in liquid chromatography-mass spectrometry-based metabolomics analysis: A case study of Bupleuri radix

INTRODUCTION

Compound annotation is always a challenging step in metabolomics studies. The molecular networking strategy has been developed recently to organize the relationship between compounds as a network based on their tandem mass (MS2) spectra similarity, which can be used to improve compound annotation in metabolomics analysis.

OBJECTIVE

This study used Bupleuri Radix from different geographic areas to evaluate the performance of molecular networking strategy for compound annotation in liquid chromatography-mass spectrometry (LC-MS)-based metabolomics.

METHODOLOGY

The Bupleuri Radix extract was analyzed by LC-quadrupole time-of-flight MS under MSe acquisition mode. After raw data preprocessing, the resulting dataset was used for statistical analysis, including principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). The chemical makers related to the sample growth place were selected using variable importance in projection (VIP) > 2, fold change (FC) > 2, and p < 0.05. The molecular networking analysis was applied to conduct the compound annotation.

RESULTS

The score plots of PCA showed that the samples were classified into two clusters depending on their growth place. Then, the PLS-DA model was constructed to explore the chemical changes of the samples further. Sixteen compounds were selected as chemical makers and tentatively annotated by the feature-based molecular networking (FBMN) analysis.

CONCLUSION

The results showed that the molecular networking method fully exploits the MS information and is a promising tool for facilitating compound annotation in metabolomics studies. However, the software used for feature extraction influenced the results of library searching and molecular network construction, which need to be taken into account in future studies.

Mechanistic insight into the impact of polystyrene microparticle on submerged plant during asexual propagules germination to seedling: Internalization in functional organs and alterations of physiological phenotypes

Asexual reproduction is one of the most important propagations in aquatic plants. However, there is a lack of information about the growth-limiting mechanisms induced by microplastics on the submerged plant during asexual propagule germination to seedling. Hence, we investigated the effects of two sizes (2 µm, 0.2 µm) and three concentrations (0.5 mg/L, 5 mg/L, and 50 mg/L) of polystyrene microplastics (PSMPs) on Potamogeton crispus turion germination and seedling growth. Both PSMPs sizes were found in P. crispus seedling tissues. Metabolic profile alterations were observed in leaves, particularly affecting secondary metabolic pathways and ATP-binding cassette transporters. Metal elements are indispensable cofactors for photosynthesis; however, alterations in the metabolic profile led to varying degrees of reduced concentrations in magnesium, iron, copper, and zinc within P. crispus. Therefore, the maximum quantum yield of photosystem II significantly decreased in all concentrations with 0.2 µm-PSMPs, and at 50 mg/L with 2 µm-PSMPs. These findings reveal that internalization of microplastics, nutrient absorption inhibition, and metabolic changes contribute to the negative impact on P. crispus seedlings.

Classifying breast cancer using multi-view graph neural network based on multi-omics data

Introduction: As the evaluation indices, cancer grading and subtyping have diverse clinical, pathological, and molecular characteristics with prognostic and therapeutic implications. Although researchers have begun to study cancer differentiation and subtype prediction, most of relevant methods are based on traditional machine learning and rely on single omics data. It is necessary to explore a deep learning algorithm that integrates multi-omics data to achieve classification prediction of cancer differentiation and subtypes. Methods: This paper proposes a multi-omics data fusion algorithm based on a multi-view graph neural network (MVGNN) for predicting cancer differentiation and subtype classification. The model framework consists of a graph convolutional network (GCN) module for learning features from different omics data and an attention module for integrating multi-omics data. Three different types of omics data are used. For each type of omics data, feature selection is performed using methods such as the chi-square test and minimum redundancy maximum relevance (mRMR). Weighted patient similarity networks are constructed based on the selected omics features, and GCN is trained using omics features and corresponding similarity networks. Finally, an attention module integrates different types of omics features and performs the final cancer classification prediction. Results: To validate the cancer classification predictive performance of the MVGNN model, we conducted experimental comparisons with traditional machine learning models and currently popular methods based on integrating multi-omics data using 5-fold cross-validation. Additionally, we performed comparative experiments on cancer differentiation and its subtypes based on single omics data, two omics data, and three omics data. Discussion: This paper proposed the MVGNN model and it performed well in cancer classification prediction based on multiple omics data.

Unraveling the potential of segment scan mass spectral acquisition for chemical isotope labeling LC-MS-based metabolome analysis: Performance assessment across different types of biological samples

BACKGROUND

Chemical isotope labeling (CIL) LC-MS is a powerful tool for metabolome analysis with high metabolomic coverage and quantification accuracy. In CIL LC-MS, the overall metabolite detection efficiency using Orbitrap MS can be further improved by employing a segment scan method where the full m/z range is divided into multiple segments for spectral acquisition with a significant increase in the in-spectrum dynamic range. Considering the metabolic complexity in different types of biological samples (e.g., feces, urine, serum/plasma, cell/tissue extracts, saliva, etc.), we report the development and evaluation of the segment scan method for metabolome analysis of different sample types.

RESULTS

It was found that sample complexity significantly influenced the performance of the segment scan method. In metabolically complex samples such as feces and urine, the method yielded a substantial increase (up to 94 %) in detected peak pairs or metabolites, compared to conventional full scan. Conversely, less complex samples like saliva exhibited more modest gains (approximately 25 %). Based on the observations, a 120-m/z segment scan method was determined as a routine approach for CIL LC-Orbitrap-MS-based metabolomics with good compatibility with different types of biological samples. For this method, a further investigation on relative quantification accuracy was done. The peak area ratios of 12C-/13-labeled metabolites were slightly reduced with 72%-84 % of peak pairs falling within the ±25 % range of the anticipated peak ratio of 1.0 among different samples, as opposed to 81%-90 % in the full scan, which was attributed to the inclusion of more low-abundance peak pairs within the narrow MS segments. However, the overall peak ratio measurement precision was not significantly affected by the segment scan.

SIGNIFICANCE AND NOVELTY

The segment scan method was found to be useful for CIL LC-Orbitrap-MS-based metabolome analysis of different types of samples with significant improvement in metabolite detectability (25-94 % increase), compared to the conventional full scan method.

Sample Preparation from Plant Tissue for Gas Chromatography-Mass Spectrometry (GC-MS)we

Metabolites are intermediate products formed during metabolism. Metabolites play different roles, including providing energy, supporting structure, transmitting signals, catalyzing reactions, enhancing defense, and interacting with other species. Plant metabolomics research aims to detect precisely all metabolites found within tissues of plants through GC-MS. This chapter primarily focuses on extracting metabolites using chemicals such as methanol, chloroform, ribitol, MSTFA, and TMCS. The metabolic analysis method is frequently used according to the specific kind of sample or matrix being investigated and the analysis objective. Chromatography (LC, GC, and CE) with mass spectrometry and NMR spectroscopy is used in modern metabolomics to analyze metabolites from plant samples. The most frequently used method for metabolites analysis is the GC-MS. It is a powerful technique that combines gas chromatography's separation capabilities with mass spectrometry, offering detailed information, including structural identification of each metabolite. This chapter contains an easy-to-follow guide to extract plant-based metabolites. The current protocol provides all the information needed for extracting metabolites from a plant, precautions, and troubleshooting.

Optimising a urinary extraction method for non-targeted GC-MS metabolomics

Urine is ideal for non-targeted metabolomics, providing valuable insights into normal and pathological cellular processes. Optimal extraction is critical since non-targeted metabolomics aims to analyse various compound classes. Here, we optimised a low-volume urine preparation procedure for non-targeted GC-MS. Five extraction methods (four organic acid [OA] extraction variations and a "direct analysis" [DA] approach) were assessed based on repeatability, metabolome coverage, and metabolite recovery. The DA method exhibited superior repeatability, and achieved the highest metabolome coverage, detecting 91 unique metabolites from multiple compound classes comparatively. Conversely, OA methods may not be suitable for all non-targeted metabolomics applications due to their bias toward a specific compound class. In accordance, the OA methods demonstrated limitations, with lower compound recovery and a higher percentage of undetected compounds. The DA method was further improved by incorporating an additional drying step between two-step derivatization but did not benefit from urease sample pre-treatment. Overall, this study establishes an improved low-volume urine preparation approach for future non-targeted urine metabolomics applications using GC-MS. Our findings contribute to advancing the field of metabolomics and enable efficient, comprehensive analysis of urinary metabolites, which could facilitate more accurate disease diagnosis or biomarker discovery.

Omics approaches for the assessment of biological responses to nanoparticles

Nanotechnology has enabled the development of innovative therapeutics, diagnostics, and drug delivery systems. Nanoparticles (NPs) can influence gene expression, protein synthesis, cell cycle, metabolism, and other subcellular processes. While conventional methods have limitations in characterizing responses to NPs, omics approaches can analyze complete sets of molecular entities that change upon exposure to NPs. This review discusses key omics approaches, namely transcriptomics, proteomics, metabolomics, lipidomics and multi-omics, applied to the assessment of biological responses to NPs. Fundamental concepts and analytical methods used for each approach are presented, as well as good practices for omics experiments. Bioinformatics tools are essential to analyze, interpret and visualize large omics data, and to correlate observations in different molecular layers. The authors envision that conducting interdisciplinary multi-omics analyses in future nanomedicine studies will reveal integrated cell responses to NPs at different omics levels, and the incorporation of omics into the evaluation of targeted delivery, efficacy, and safety will improve the development of nanomedicine therapies.

Interpretable machine learning-accelerated seed treatment using nanomaterials for environmental stress alleviation

Crops are constantly challenged by different environmental conditions. Seed treatment using nanomaterials is a cost-effective and environmentally friendly solution for environmental stress mitigation in crop plants. Here, 56 seed nanopriming treatments are used to alleviate environmental stresses in maize. Seven selected nanopriming treatments significantly increase the stress resistance index (SRI) by 13.9% and 12.6% under salinity stress and combined heat-drought stress, respectively. Metabolomics data reveal that ZnO nanopriming treatment, with the highest SRI value, mainly regulates the pathways of amino acid metabolism, secondary metabolite synthesis, carbohydrate metabolism, and translation. Understanding the mechanism of seed nanopriming is still difficult due to the variety of nanomaterials and the complexity of interactions between nanomaterials and plants. Using the nanopriming data, we present an interpretable structure-activity relationship (ISAR) approach based on interpretable machine learning for predicting and understanding its stress mitigation effects. The post hoc and model-based interpretation approaches of machine learning are integrated to provide complementary advantages and may yield more illuminating or trustworthy results for researchers or policymakers. The concentration, size, and zeta potential of nanoparticles are identified as dominant factors for correlating root dry weight under salinity stress, and their effects and interactions are explained. Additionally, a web-based interactive tool is developed for offering prediction-level interpretation and gathering more details about a specific nanopriming treatment. This work offers a promising framework for accelerating the agricultural applications of nanomaterials and may contribute to nanosafety assessment.

Metabolomics in archaeological science: A review of their advances and present requirements

Metabolomics, the study of metabolites (small molecules of <1500 daltons), has been posited as a potential tool to explore the past in a comparable manner to other omics, e.g., genomics or proteomics. Archaeologists have used metabolomic approaches for a decade or so, mainly applied to organic residues adhering to archaeological materials. Because of advances in sensitivity, resolution, and the increased availability of different analytical platforms, combined with the low mass/volume required for analysis, metabolomics is now becoming a more feasible choice in the archaeological sector. Additional approaches, as presented by our group, show the versatility of metabolomics as a source of knowledge about the human past when using human osteoarchaeological remains. There is tremendous potential for metabolomics within archaeology, but further efforts are required to position it as a routine technique.

Behavioral metabolomics: how behavioral data can guide metabolomics research on neuropsychiatric disorders

INTRODUCTION

Metabolomics produces vast quantities of data but determining which metabolites are the most relevant to the disease or disorder of interest can be challenging.

OBJECTIVES

This study sought to demonstrate how behavioral models of psychiatric disorders can be combined with metabolomics research to overcome this limitation.

METHODS

We designed a preclinical, untargeted metabolomics procedure, that focuses on the determination of central metabolites relevant to substance use disorders that are (a) associated with changes in behavior produced by acute drug exposure and (b) impacted by repeated drug exposure. Untargeted metabolomics analysis was carried out on liquid chromatography-mass spectrometry data obtained from 336 microdialysis samples. Samples were collected from the medial striatum of male Sprague-Dawley (N = 21) rats whilst behavioral data were simultaneously collected as part of a (±)-3,4-methylenedioxymethamphetamine (MDMA)-induced behavioral sensitization experiment. Analysis was conducted by orthogonal partial least squares, where the Y variable was the behavioral data, and the X variables were the relative concentrations of the 737 detected features.

RESULTS

MDMA and its derivatives, serotonin, and several dopamine/norepinephrine metabolites were the greatest predictors of acute MDMA-produced behavior. Subsequent univariate analyses showed that repeated MDMA exposure produced significant changes in MDMA metabolism, which may contribute to the increased abuse liability of the drug as a function of repeated exposure.

CONCLUSION

These findings highlight how the inclusion of behavioral data can guide metabolomics data analysis and increase the relevance of the results to the phenotype of interest.

Metabolome Mining of Curcuma longa L. Using HPLC-MS/MS and Molecular Networking

Turmeric, Curcuma longa L., is a type of medicinal plant characterized by its perennial nature and rhizomatous growth. It is a member of the Zingiberaceae family and is distributed across the world's tropical and subtropical climates, especially in South Asia. Its rhizomes have been highly valued for food supplements, spices, flavoring agents, and yellow dye in South Asia since ancient times. It exhibits a diverse array of therapeutic qualities that encompass its ability to combat diabetes, reduce inflammation, act as an antioxidant, exhibit anticancer properties, and promote anti-aging effects. In this study, organic extracts of C. longa rhizomes were subjected to HPLC separation followed by ESI-MS and low-energy tandem mass spectrometry analyses. The Global Natural Product Social Molecular Networking (GNPS) approach was utilized for the first time in this ethnobotanically important species to conduct an in-depth analysis of its metabolomes based on their fragments. To sum it up, a total of 30 metabolites including 16 diarylheptanoids, 1 diarylpentanoid, 3 bisabolocurcumin ethers, 4 sesquiterpenoids, 4 cinnamic acid derivatives, and 2 fatty acid derivatives were identified. Among the 16 diarylheptanoids identified in this study, 5 of them are reported for the first time in this species.

Study on taste quality formation and leaf conducting tissue changes in six types of tea during their manufacturing processes

This study fristly investigated the taste quality formation and leaf conducting tissue changes in six types of Chinese tea (green, black, oolong, yellow, white, and dark) made from Mingke No.1 variety. Non-targeted metabolomics showed the vital manufacturing processes (green tea-de-enzyming, black tea-fermenting, oolong tea-turning-over, yellow tea-yellowing, white tea-withering, and dark tea-pile-fermenting) were highly related to their unique taste formation, due to different fermentation degree in these processes. After drying, the retained phenolics, theanine, caffeine, and other substances significantly impacted each tea taste quality formation. Meanwhile, the tea leaf conducting tissue structure was significantly influenced by high processing temperature, and the change of its inner diameter was related to moisture loss during tea processing, as indicated by its significant different Raman characteristic peaks (mainly cellulose and lignin) in each key process. This study provides a reference for process optimization to improve tea quality.

Multi-omics assisted breeding for biotic stress resistance in soybean

Biotic stress is a critical factor limiting soybean growth and development. Soybean responses to biotic stresses such as insects, nematodes, fungal, bacterial, and viral pathogens are governed by complex regulatory and defense mechanisms. Next-generation sequencing has availed research techniques and strategies in genomics and post-genomics. This review summarizes the available information on marker resources, quantitative trait loci, and marker-trait associations involved in regulating biotic stress responses in soybean. We discuss the differential expression of related genes and proteins reported in different transcriptomics and proteomics studies and the role of signaling pathways and metabolites reported in metabolomic studies. Recent advances in omics technologies offer opportunities to reshape and improve biotic stress resistance in soybean by altering gene regulation and/or other regulatory networks. We suggest using 'integrated omics' to precisely understand how soybean responds to different biotic stresses. We also discuss the potential challenges of integrating multi-omics for the functional analysis of genes and their regulatory networks and the development of biotic stress-resistant cultivars. This review will help direct soybean breeding programs to develop resistance against different biotic stresses.

Metabolomics Applied to Cyanobacterial Toxins and Natural Products

The biological and chemical diversity of Cyanobacteria is remarkable. These ancient prokaryotes are widespread in nature and can be found in virtually every habitat on Earth where there is light and water. They are producers of an array of secondary metabolites with important ecological roles, toxic effects, and biotechnological applications. The investigation of cyanobacterial metabolites has benefited from advances in analytical tools and bioinformatics that are employed in metabolomic analyses. In this chapter, we review selected articles highlighting the use of targeted and untargeted metabolomics in the analyses of secondary metabolites produced by cyanobacteria. Here, cyanobacterial secondary metabolites have been didactically divided into toxins and natural products according to their relevance to toxicological studies and drug discovery, respectively. This review illustrates how metabolomics has improved the chemical analysis of cyanobacteria in terms of speed, sensitivity, selectivity, and/or coverage, allowing for broader and more complex scientific questions.

Applications of machine learning in metabolomics: Disease modeling and classification

Metabolomics research has recently gained popularity because it enables the study of biological traits at the biochemical level and, as a result, can directly reveal what occurs in a cell or a tissue based on health or disease status, complementing other omics such as genomics and transcriptomics. Like other high-throughput biological experiments, metabolomics produces vast volumes of complex data. The application of machine learning (ML) to analyze data, recognize patterns, and build models is expanding across multiple fields. In the same way, ML methods are utilized for the classification, regression, or clustering of highly complex metabolomic data. This review discusses how disease modeling and diagnosis can be enhanced via deep and comprehensive metabolomic profiling using ML. We discuss the general layout of a metabolic workflow and the fundamental ML techniques used to analyze metabolomic data, including support vector machines (SVM), decision trees, random forests (RF), neural networks (NN), and deep learning (DL). Finally, we present the advantages and disadvantages of various ML methods and provide suggestions for different metabolic data analysis scenarios.

An initial investigation of accuracy required for the identification of small molecules in complex samples using quantum chemical calculated NMR chemical shifts

The majority of primary and secondary metabolites in nature have yet to be identified, representing a major challenge for metabolomics studies that currently require reference libraries from analyses of authentic compounds. Using currently available analytical methods, complete chemical characterization of metabolomes is infeasible for both technical and economic reasons. For example, unambiguous identification of metabolites is limited by the availability of authentic chemical standards, which, for the majority of molecules, do not exist. Computationally predicted or calculated data are a viable solution to expand the currently limited metabolite reference libraries, if such methods are shown to be sufficiently accurate. For example, determining nuclear magnetic resonance (NMR) spectroscopy spectra in silico has shown promise in the identification and delineation of metabolite structures. Many researchers have been taking advantage of density functional theory (DFT), a computationally inexpensive yet reputable method for the prediction of carbon and proton NMR spectra of metabolites. However, such methods are expected to have some error in predicted ¹³C and ¹H NMR spectra with respect to experimentally measured values. This leads us to the question–what accuracy is required in predicted ¹³C and ¹H NMR chemical shifts for confident metabolite identification? Using the set of 11,716 small molecules found in the Human Metabolome Database (HMDB), we simulated both experimental and theoretical NMR chemical shift databases. We investigated the level of accuracy required for identification of metabolites in simulated pure and impure samples by matching predicted chemical shifts to experimental data. We found 90% or more of molecules in simulated pure samples can be successfully identified when errors of ¹H and ¹³C chemical shifts in water are below 0.6 and 7.1 ppm, respectively, and below 0.5 and 4.6 ppm in chloroform solvation, respectively. In simulated complex mixtures, as the complexity of the mixture increased, greater accuracy of the calculated chemical shifts was required, as expected. However, if the number of molecules in the mixture is known, e.g., when NMR is combined with MS and sample complexity is low, the likelihood of confident molecular identification increased by 90%.

Polyphasic Systematics of the Fungicolous Genus Cladobotryum Based on Morphological, Molecular and Metabolomics Data

(1) Background: Species of the anamorphic genus Cladobotryum, are known for their fungicolous lifestyle, making them important mycopathogens in fungiculture. Many morphological, ecological, and molecular phylogenetic studies of the genus have been done to date, but taxonomic uncertainties and challenges still remain. Fungal secondary metabolites, being vastly diverse, are utilised as an extra tool in fungal systematics. Despite being studied for their potentially bioactive compounds, Cladobotryum species are insufficiently investigated regarding metabolomics. (2) Methods: The aim of this study is the identification of Greek strains of Cladobotryum by integrating morphological data, ITS-based phylogeny, and ¹H NMR-based metabolomics into a polyphasic approach. (3) Results: Twenty-three strains, isolated from sporophores of macromycetes inhabiting diverse Greek ecosystems, were morphologically identified as Cladobotryum apiculatum, C. fungicola, C. mycophilum, C. varium, C. verticillatum, and Hypomyces rosellus (anamorph C. dendroides), whereas seven strains, which produced red-pigmented metabolites, presented an ambiguous taxonomic position at the species level. Molecular phylogenetics and metabolomics corroborated the morphological findings. (4) Conclusions: Thorough morphological study, ITS region-based phylogeny, and NMR-based metabolomics contribute complementarily to resolving the genus Cladobotryum systematics.

The metabolic analysis in human aortic tissues of aortic dissection

Background

The metabolic profile of human aortic tissues is of great importance. Among the analytical platforms utilized in metabolomics, LC‐MS provides broad metabolome coverage. The non‐targeted metabolomics can comprehensively detect the entire metabolome of an organism and find the metabolic characteristics that have significant changes in the experimental group and the control group and elucidate the metabolic pathway concerning the recognized metabolites. Employing non‐targeted metabolomics is helpful to develop biomarkers for disease diagnosis and disease pathology research; for instance, Aortic aneurysm (AA) and Aortic dissection (AD).

Aim

This study sought to describe the non‐targeted analysis of 18 aortic tissue samples, comparing between AA and AD.

Material & Methods

Our experimental flow included dividing the samples into (AA, nine samples) and (AD, nine samples), SCIEX quadrupole timeofflight tandem mass spectrometer (TripleTOF) 6600+ mass spectrometer data refinement, MetDNA database analysis, and pathway analysis. We performed an initial validation by setting quality control parameters to evaluate the stability of the analysis system during the computer operation. We then used the repeatability of the control samples to examine the stability of the instrument during the entire analysis process to ensure the reliability of the results.

Results

Our study found 138 novel metabolites involved in galactose metabolism.

Discussion

138 novel metabolites found in this study will be further studied in the future.

Conclusion

Our study found 138 novel metabolites between AA and AD, which will provide viable clinical data for future studies aimed to implement galactose markers in aortic tissue analysis.

Ethnic disparities attributed to the manifestation in and response to type 2 diabetes: insights from metabolomics

Type 2 diabetes (T2D) associated health disparities among different ethnicities have long been known. Ethnic variations also exist in T2D related comorbidities including insulin resistance, vascular complications and drug response. Genetic heterogeneity, dietary patterns, nutrient metabolism and gut microbiome composition attribute to ethnic disparities in both manifestation and progression of T2D. These factors differentially regulate the rate of metabolism and metabolic health. Metabolomics studies have indicated significant differences in carbohydrate, lipid and amino acid metabolism among ethnicities. Interestingly, genetic variations regulating lipid and amino acid metabolism might also contribute to inter-ethnic differences in T2D. Comprehensive and comparative metabolomics analysis between ethnicities might help to design personalized dietary regimen and newer therapeutic strategies. In the present review, we explore population based metabolomics data to identify inter-ethnic differences in metabolites and discuss how (a) genetic variations, (b) dietary patterns and (c) microbiome composition may attribute for such differences in T2D.

A Review of Integrative Omic Approaches for Understanding Rice Salt Response Mechanisms

Soil salinity is one of the most serious environmental challenges, posing a growing threat to agriculture across the world. Soil salinity has a significant impact on rice growth, development, and production. Hence, improving rice varieties’ resistance to salt stress is a viable solution for meeting global food demand. Adaptation to salt stress is a multifaceted process that involves interacting physiological traits, biochemical or metabolic pathways, and molecular mechanisms. The integration of multi-omics approaches contributes to a better understanding of molecular mechanisms as well as the improvement of salt-resistant and tolerant rice varieties. Firstly, we present a thorough review of current knowledge about salt stress effects on rice and mechanisms behind rice salt tolerance and salt stress signalling. This review focuses on the use of multi-omics approaches to improve next-generation rice breeding for salinity resistance and tolerance, including genomics, transcriptomics, proteomics, metabolomics and phenomics. Integrating multi-omics data effectively is critical to gaining a more comprehensive and in-depth understanding of the molecular pathways, enzyme activity and interacting networks of genes controlling salinity tolerance in rice. The key data mining strategies within the artificial intelligence to analyse big and complex data sets that will allow more accurate prediction of outcomes and modernise traditional breeding programmes and also expedite precision rice breeding such as genetic engineering and genome editing.

Metabolomic and inflammatory signatures of symptom dimensions in major depression

BACKGROUND

Major depressive disorder (MDD) is a highly heterogenous disease, both in terms of clinical profiles and pathobiological alterations. Recently, immunometabolic dysregulations were shown to be correlated with atypical, energy-related symptoms but less so with the Melancholic or Anxious distress symptom dimensions of depression in The Netherlands Study of Depression and Anxiety (NESDA) study. In this study, we aimed to replicate these immunometabolic associations and to characterize the metabolomic correlates of each of the three MDD dimensions.

METHODS

Using three clinical rating scales, Melancholic, and Anxious distress, and Immunometabolic (IMD) dimensions were characterized in 158 patients who participated in the Predictors of Remission to Individual and Combined Treatments (PReDICT) study and from whom plasma and serum samples were available. The NESDA-defined inflammatory index, a composite measure of interleukin-6 and C-reactive protein, was measured from pre-treatment plasma samples and a metabolomic profile was defined using serum samples analyzed on three metabolomics platforms targeting fatty acids and complex lipids, amino acids, acylcarnitines, and gut microbiome-derived metabolites among other metabolites of central metabolism.

RESULTS

The IMD clinical dimension and the inflammatory index were positively correlated (r = 0.19, p = 0.019) after controlling for age, sex, and body mass index, whereas the Melancholic and Anxious distress dimensions were not, replicating the previous NESDA findings. The three symptom dimensions had distinct metabolomic signatures using both univariate and set enrichment statistics. IMD severity correlated mainly with gut-derived metabolites and a few acylcarnitines and long chain saturated free fatty acids. Melancholia severity was significantly correlated with several phosphatidylcholines, primarily the ether-linked variety, lysophosphatidylcholines, as well as several amino acids. Anxious distress severity correlated with several medium and long chain free fatty acids, both saturated and polyunsaturated ones, sphingomyelins, as well as several amino acids and bile acids.

CONCLUSION

The IMD dimension of depression appears reliably associated with markers of inflammation. Metabolomics provides powerful tools to inform about depression heterogeneity and molecular mechanisms related to clinical dimensions in MDD, which include a link to gut microbiome and lipids implicated in membrane structure and function.

Anti-cancer and antimicrobial potential of five soil Streptomycetes: a metabolomics-based study

Lack of new anti-cancer and anti-infective agents directed the pharmaceutical research to natural products' discovery especially from actinomycetes as one of the major sources of bioactive compounds. Metabolomics- and dereplication-guided approach has been used successfully in chemical profiling of bioactive actinomycetes. We aimed to study the metabolomic profile of five bioactive actinomycetes to investigate the interesting metabolites responsible for their antimicrobial and anti-cancer activities. Three actinomycetes, namely, Streptomyces sp. SH8, SH10 and SH13, were found to exhibit broad spectrum of antimicrobial activities, whereas isolate SH4 showed the broadest antimicrobial activity against all tested strains. In addition, isolates SH8, SH10 and SH12 displayed potent cytotoxicity against the breast cancer cell line Michigan Cancer Foundation-7 (MCF-7), whereas isolates SH4 and SH12 exhibited potent anti-cancer activity against the hepatoma cell line hepatoma G2 (HepG2) compared with their weak inhibitory properties on the normal breast cells MCF-10A and normal liver cells transformed human liver epithelial-2 (THLE2), respectively. All bioactive isolates were molecularly identified as Streptomyces sp. via 16S rRNA gene sequencing. Our actinobacterial dereplication analysis revealed putative identification of several bioactive metabolites including tetracycline, oxytetracycline and a macrolide antibiotic, novamethymycin. Together, chemical profiling of bioactive Streptomycetes via dereplication and metabolomics helped in assigning their unique metabolites and predicting the bioactive compounds instigating their diverse bioactivities.

Haemolymph metabolomic differences in silkworms (Bombyx mori L.) under mulberry leaf and two artificial diet rearing methods

The new technology of silkworm (Bombyx mori L.) artificial feed breeding has many characteristics and advantages. This study assessed silkworm rearing with mulberry leaf at all instars (MF) as the control, and used metabolomics to explore the differences in haemolymph metabolism of fifth instar silkworms under two modes of rearing with an artificial diet at all instars (AF) and rearing with an artificial diet during first to third instars and mulberry leaf during the fourth and fifth instars (AMF). The results show that, compared with silkworms of the MF group, the amount and fold change of various metabolites were higher in the haemolymph of AF group silkworms, and the metabolism of amino acids and uric acid, carbohydrates, lipids, and vitamins were changed. These changes may be the reasons for the poor performance of the AF silkworms. However, the amount and fold change of the various metabolites of silkworms in the AMF group were lower, and some metabolic pathways were more active. The amount of material and energy supply were greater. These changes could explain the high efficiency growth of body weight of silkworms after the conversion from artificial diet rearing to mulberry leaf rearing. These findings provide an important theoretical basis for the optimisation of artificial diet rearing technology for silkworms.

Serum metabolomic analysis in patients with Hashimoto's thyroiditis

BACKGROUND

Hashimoto's thyroiditis, an autoimmune thyroid disease, shows high morbidity worldwide, particularly in female. Patients with Hashimoto's thyroiditis have an increasing risk of hypothyroidism during the occurrence and progression of Hashimoto's thyroiditis. In recent years, metabolomics has been widely applied in autoimmune diseases, especially thyroid disorders. However, metabolites analysis in Hashimoto's thyroiditis is still absent.

METHODS

A total of 92 samples were collected, including 35 cases in the control group, 30 cases in the Hashimoto's thyroiditis with euthyroidism group, and 27 cases in the Hashimoto's thyroiditis with subclinical hypothyroidism group. SPSS 25.0 for statistical analysis and ROC curve, SIMCA 14.0, Metaboanalysis for multifactor analysis, and Origin 2021 for correlation analysis.

RESULTS

21 metabolites were identified. 10 metabolites were obtained from control group versus HTE group, 8 serum metabolites were abnormal between control group and HTS group, 3 metabolites were derived from HTE group versus HTS. Kyoto Encyclopedia of Genes and Genomes Enrichment analysis showed that fatty acid degradation, Arginine, and proline metabolism have a significant impact on HTE, while lysine degradation, tyrosine metabolism play an important role HTS group, compared to control group. In the comparison between the HTE and HTS group, Valine, leucine, and isoleucine degradation and Valine, leucine, and isoleucine biosynthesis exists a key role. Correlation analysis shows clinical are not related to metabolites. ROC curve indicates SM, LPC, PC can efficiency in identification patients with HT in different clinical stage from healthy individuals.

CONCLUSION

Serum metabolites were changed in HT. Phospholipids such as SM, LPC, PC influence the pathogenesis of Hashimoto's thyroiditis. Fatty acid degradation and lysine degradation pathways have an impact on different clinical stage of HT.

The Impact of the hAPP695SW Transgene and Associated Amyloid-β Accumulation on Murine Hippocampal Biochemical Pathways

Background: Alzheimer’s disease (AD) is a neurodegenerative disease characterized by the accumulation of amyloid-β (Aβ) peptide in the brain. Objective: Gain a better insight into alterations in major biochemical pathways underlying AD. Methods: We compared metabolomic profiles of hippocampal tissue of 20-month-old female Tg2576 mice expressing the familial AD-associated hAPP695SW transgene with their 20-month-old wild type female littermates. Results: The hAPP695SW transgene causes overproduction and accumulation of Aβ in the brain. Out of 180 annotated metabolites, 54 metabolites differed (30 higher and 24 lower in Tg2576 versus wild-type hippocampal tissue) and were linked to the amino acid, nucleic acid, glycerophospholipid, ceramide, and fatty acid metabolism. Our results point to 1) heightened metabolic activity as indicated by higher levels of urea, enhanced fatty acid β-oxidation, and lower fatty acid levels; 2) enhanced redox regulation; and 3) an imbalance of neuro-excitatory and neuro-inhibitory metabolites in hippocampal tissue of aged hAPP695SW transgenic mice. Conclusion: Taken together, our results suggest that dysregulation of multiple metabolic pathways associated with a concomitant shift to an excitatory-inhibitory imbalance are contributing mechanisms of AD-related pathology in the Tg2576 mouse.

NMR Hydrophilic Metabolomic Analysis of Bacterial Resistance Pathways Using Multivalent Antimicrobials with Challenged and Unchallenged Wild Type and Mutated Gram-Positive Bacteria

Multivalent membrane disruptors are a relatively new antimicrobial scaffold that are difficult for bacteria to develop resistance to and can act on both Gram-positive and Gram-negative bacteria. Proton Nuclear Magnetic Resonance (¹H NMR) metabolomics is an important method for studying resistance development in bacteria, since this is both a quantitative and qualitative method to study and identify phenotypes by changes in metabolic pathways. In this project, the metabolic differences between wild type Bacillus cereus (B. cereus) samples and B. cereus that was mutated through 33 growth cycles in a nonlethal dose of a multivalent antimicrobial agent were identified. For additional comparison, samples for analysis of the wild type and mutated strains of B. cereus were prepared in both challenged and unchallenged conditions. A C₁₆-DABCO (1,4-diazabicyclo-2,2,2-octane) and mannose functionalized poly(amidoamine) dendrimer (DABCOMD) were used as the multivalent quaternary ammonium antimicrobial for this hydrophilic metabolic analysis. Overall, the study reported here indicates that B. cereus likely change their peptidoglycan layer to protect themselves from the highly positively charged DABCOMD. This membrane fortification most likely leads to the slow growth curve of the mutated, and especially the challenged mutant samples. The association of these sample types with metabolites associated with energy expenditure is attributed to the increased energy required for the membrane fortifications to occur as well as to the decreased diffusion of nutrients across the mutated membrane.

GC-MS/MS Profiling of Plant Metabolites

Gas chromatography coupled to electron ionization (EI) quadrupole mass spectrometry (GC-MS) is currently one of the most developed and robust metabolomics technologies. This approach allows for simultaneous measurements of large number of chemically diverse compounds including organic acids, amino acids, sugars, sugar alcohols, aromatic amines, and fatty acids. Untargeted GC-MS profiling based on full scan data acquisition requires complicated raw data processing and sometime provides ambiguous metabolite identifications. Targeted analysis using GC-MS/MS can provide better specificity, increase sensitivity, and simplify data processing and compound identification but wider application of targeted GC-MS/MS approach in metabolomics is hampered by the lack of extensive databases of MRM transitions for non-derivatized and derivatized endogenous metabolites. The focus of this chapter is the automation of GC-MS/MS method development which makes it feasible to develop quantitative methods for several hundred metabolites and use this strategy for plant metabolomics applications.

The Investigation of Metabonomic Pathways of Serum of Iranian Women with Recurrent Miscarriage Using 1H NMR

Purpose

Recurrent miscarriage applies to pregnancy loss expulsion of the fetus within the first 24 weeks of pregnancy. This study is aimed at comparatively investigating the sera of women with RM with those who have no record of miscarriages to identify if there were any metabolite and metabolic pathway differences using ¹H NMR spectroscopy.

Methods

Serum samples were collected from women with RM (n = 30) and those who had no records of RM (n = 30) to obtain metabolomics information. ¹H NMR spectroscopy was carried out on the samples using Carr Purcell Meiboom Gill spin echo; also, Partial Least Squares Discriminant Analysis was performed in MATLAB software using the ProMetab program to obtain the classifying chemical shifts; the metabolites were identified by using the Human Metabolome Database (HMDB) in both the experimental and control groups. The pathway analysis option of the Metaboanalyst.ca website was used to identify the changed metabolic pathways.

Results

The results of the study revealed that 14 metabolites were different in the patients with RM. Moreover, the pathway analysis showed that taurine and hypotaurine metabolism along with phenylalanine, tyrosine, and tryptophan biosynthesis was significantly different in patients with RM.

Conclusion

The present study proposes that any alteration in the above metabolic pathways might lead to metabolic dysfunctions which may result in a higher probability of RM.

Metabolomic fingerprinting of pig seminal plasma identifies in vivo fertility biomarkers

Background

Metabolomic approaches, which include the study of low molecular weight molecules, are an emerging -omics technology useful for identification of biomarkers. In this field, nuclear magnetic resonance (NMR) spectroscopy has already been used to uncover (in) fertility biomarkers in the seminal plasma (SP) of several mammalian species. However, NMR studies profiling the porcine SP metabolome to uncover in vivo fertility biomarkers are yet to be carried out. Thus, this study aimed to evaluate the putative relationship between SP-metabolites and in vivo fertility outcomes. To this end, 24 entire ejaculates (three ejaculates per boar) were collected from artificial insemination (AI)-boars throughout a year (one ejaculate every 4 months). Immediately after collection, ejaculates were centrifuged to obtain SP-samples, which were stored for subsequent metabolomic analysis by NMR spectroscopy. Fertility outcomes from 1525 inseminations were recorded over a year, including farrowing rate, litter size, stillbirths per litter and the duration of pregnancy.

Results

A total of 24 metabolites were identified and quantified in all SP-samples. Receiver operating characteristic (ROC) curve analysis showed that lactate levels in SP had discriminative capacity for farrowing rate (area under the curve [AUC] = 0.764) while carnitine (AUC = 0.847), hypotaurine (AUC = 0.819), sn-glycero-3-phosphocholine (AUC = 0.833), glutamate (AUC = 0.799) and glucose (AUC = 0.750) showed it for litter size. Similarly, citrate (AUC = 0.743), creatine (AUC = 0.812), phenylalanine (AUC = 0.750), tyrosine (AUC = 0.753) and malonate (AUC = 0.868) levels had discriminative capacity for stillbirths per litter; and malonate (AUC = 0.767) and fumarate (AUC = 0.868) levels for gestation length.

Conclusions

The assessment of selected SP-metabolites in ejaculates through NMR spectroscopy could be considered as a promising non-invasive tool to predict in vivo fertility outcomes in pigs. Moreover, supplementing AI-doses with specific metabolites should also be envisaged as a way to improve their fertility potential.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40104-021-00636-5.

Metabolomics in Diabetes and Diabetic Complications: Insights from Epidemiological Studies

The increasing prevalence of diabetes and its complications, such as cardiovascular and kidney disease, remains a huge burden globally. Identification of biomarkers for the screening, diagnosis, and prognosis of diabetes and its complications and better understanding of the molecular pathways involved in the development and progression of diabetes can facilitate individualized prevention and treatment. With the advancement of analytical techniques, metabolomics can identify and quantify multiple biomarkers simultaneously in a high-throughput manner. Providing information on underlying metabolic pathways, metabolomics can further identify mechanisms of diabetes and its progression. The application of metabolomics in epidemiological studies have identified novel biomarkers for type 2 diabetes (T2D) and its complications, such as branched-chain amino acids, metabolites of phenylalanine, metabolites involved in energy metabolism, and lipid metabolism. Metabolomics have also been applied to explore the potential pathways modulated by medications. Investigating diabetes using a systems biology approach by integrating metabolomics with other omics data, such as genetics, transcriptomics, proteomics, and clinical data can present a comprehensive metabolic network and facilitate causal inference. In this regard, metabolomics can deepen the molecular understanding, help identify potential therapeutic targets, and improve the prevention and management of T2D and its complications. The current review focused on metabolomic biomarkers for kidney and cardiovascular disease in T2D identified from epidemiological studies, and will also provide a brief overview on metabolomic investigations for T2D.

A Systematic Review of Metabolic Alterations Underlying IgE-Mediated Food Allergy in Children

SCOPE

Immunoglobulin E-mediated food allergies (IgE-FA) are characterized by an ever-increasing prevalence, currently reaching up to 10.4% of children in the European Union. Metabolomics has the potential to provide a deeper understanding of the pathogenic mechanisms behind IgE-FA.

METHODS AND RESULTS

In this work, literature is systematically searched using Web of Science, PubMed, Scopus, and Embase, from January 2010 until May 2021, including human and animal metabolomic studies on multiple biofluids (urine, blood, feces). In total, 15 studies on IgE-FA are retained and a dataset of 277 potential biomarkers is compiled for in-depth pathway mapping. Decreased indoleamine 2,3-dioxygenase-1 (IDO- 1) activity is hypothesized due to altered plasma levels of tryptophan and its metabolites in IgE-FA children. In feces of children prior to IgE-FA, aberrant metabolization of sphingolipids and histidine is noted. Decreased fecal levels of (branched) short chain fatty acids ((B)SCFAs) compel a shift towards aerobic glycolysis and suggest dysbiosis, associated with an immune system shift towards T-helper 2 (Th2) responses. During animal anaphylaxis, a similar switch towards glycolysis is observed, combined with increased ketogenic pathways. Additionally, altered histidine, purine, pyrimidine, and lipid pathways are observed.

CONCLUSION

To conclude, this work confirms the unprecedented opportunities of metabolomics and supports the in-depth pathophysiological qualification in the quest towards improved diagnostic and prognostic biomarkers for IgE-FA.

How Perturbated Metabolites in Diabetes Mellitus Affect the Pathogenesis of Hypertension?

The presence of hypertension (HTN) in type 2 diabetes mellitus (DM) is a common phenomenon in more than half of the diabetic patients. Since HTN constitutes a predictor of vascular complications and cardiovascular disease in type 2 DM patients, it is of significance to understand the molecular and cellular mechanisms of type 2 DM binding to HTN. This review attempts to understand the mechanism via the perspective of the metabolites. It reviewed the metabolic perturbations, the biological function of perturbated metabolites in two diseases, and the mechanism underlying metabolic perturbation that contributed to the connection of type 2 DM and HTN. DM-associated metabolic perturbations may be involved in the pathogenesis of HTN potentially in insulin, angiotensin II, sympathetic nervous system, and the energy reprogramming to address how perturbated metabolites in type 2 DM affect the pathogenesis of HTN. The recent integration of the metabolism field with microbiology and immunology may provide a wider perspective. Metabolism affects immune function and supports immune cell differentiation by the switch of energy. The diverse metabolites produced by bacteria modified the biological process in the inflammatory response of chronic metabolic diseases either. The rapidly evolving metabolomics has enabled to have a better understanding of the process of diseases, which is an important tool for providing some insight into the investigation of diseases mechanism. Metabolites served as direct modulators of biological processes were believed to assess the pathological mechanisms involved in diseases.

Prospects and challenges of cancer systems medicine: from genes to disease networks

It is becoming evident that holistic perspectives toward cancer are crucial in deciphering the overwhelming complexity of tumors. Single-layer analysis of genome-wide data has greatly contributed to our understanding of cellular systems and their perturbations. However, fundamental gaps in our knowledge persist and hamper the design of effective interventions. It is becoming more apparent than ever, that cancer should not only be viewed as a disease of the genome but as a disease of the cellular system. Integrative multilayer approaches are emerging as vigorous assets in our endeavors to achieve systemic views on cancer biology. Herein, we provide a comprehensive review of the approaches, methods and technologies that can serve to achieve systemic perspectives of cancer. We start with genome-wide single-layer approaches of omics analyses of cellular systems and move on to multilayer integrative approaches in which in-depth descriptions of proteogenomics and network-based data analysis are provided. Proteogenomics is a remarkable example of how the integration of multiple levels of information can reduce our blind spots and increase the accuracy and reliability of our interpretations and network-based data analysis is a major approach for data interpretation and a robust scaffold for data integration and modeling. Overall, this review aims to increase cross-field awareness of the approaches and challenges regarding the omics-based study of cancer and to facilitate the necessary shift toward holistic approaches.

The role of metabolomics in personalized medicine for diabetes

Metabolomics is rapidly evolving omics technology in personalized medicine, it offers a new avenue for identification of multiple novel metabolic mediators of impaired glucose tolerance and dysglycemia. Liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry and nuclear magnetic resonance spectroscopy are most commonly used analytical methods in the field of metabolomics. Recent evidences showed that metabolomic profiles are link to the incidence of diabetes. In this review, an overview of metabolomics studies in diabetes revealed several diabetes-associated metabolites including 1,5-anhydroglycitol, branch chain amino acids, glucose, α-hydroxybutyric acid, 3-hydroundecanoyl-carnitine and phosphatidylcholine that could be potential biomarkers associated with diabetes. These identified metabolites can be used to develop personalized prognostics and diagnostic, and help in diabetes management.

The Fecal and Serum Metabolomics of Giant Pandas Based on Untargeted Metabolomics

Little is comprehensively known or understood about giant panda fecal and serum metabolites, which could serve as important indicators of the physiological metabolism of giant pandas. Therefore, we determined the contents of fecal and serum metabolites of giant pandas based on an untargeted metabolome. Four hundred and 955 metabolites were detected in the feces and serum of giant panda, respectively. Glycerophospholipid and choline metabolism were the main metabolic pathways in feces and serum. A significant correlation between the gut microbiota and fecal metabolites was found (P < 0.01). Fecal metabolites were not greatly affected by the age or gender of giant pandas, but serum metabolites were significantly affected by age and gender. The majority of different metabolites caused by age were higher in serum of younger giant pandas, including fatty acids, lipids, metabolites of bile acids, and intermediate products of vitamin D3. The majority of different metabolites caused by gender included fatty acids, phosphatidylcholine (PC), phosphatidylserine (PS), and phosphatidylethanolamine (PE). A separate feeding diet should be considered according to different ages and genders of giant panda. Therefore, our results could provide helpful suggestions to further protect captive giant pandas.

Combined analysis of microbial community and microbial metabolites based on untargeted metabolomics during pig manure composting

Compost has been widely used in agriculture in recent years, but the nutrients it provides are far from enough for plant growth. Therefore, it is necessary to systematically explore the fermentation process of composting. In this study, the succession of microbial community and metabolite characteristics in compost were analyzed by using microbial sequencing and metabolomics techniques. The results showed that compared with mesophilic phase and cooling phase, the richness and diversity of bacterial community decreased in thermophilic phase. At the genus level, Pseudomonas (8.90%), Lactobacillus (3.99%), Bacteroidetes (3.39%), Flavobacterium (3.25%) and Prevotella (Prevotella_9, 2.33%, Prevotellaceae_NK3B31_group, 2.44%) were the dominant genera in the pig manure composting. The abundance of Pseudomonas and Flavobacterium increased significantly while Lactobacillus and Prevotella were significantly decreased after composting, and the abundance of Bacteroidetes increased first and then decreased. Fatty acyls, sterol lipids, glycerophospholipids, polyketides and prenol lipids were common microbial metabolites in compost. Moreover, the linoleic acid metabolic pathway was significantly enriched in the three stages of composting, and linoleic acid metabolism might be the primary function of the microbial community in composting. The network analysis showed that between the microbial communities or between the microbial community and metabolites were closely related to each other.

A comprehensive review on carotenoids in foods and feeds: status quo, applications, patents, and research needs

Carotenoids are isoprenoids widely distributed in foods that have been always part of the diet of humans. Unlike the other so-called food bioactives, some carotenoids can be converted into retinoids exhibiting vitamin A activity, which is essential for humans. Furthermore, they are much more versatile as they are relevant in foods not only as sources of vitamin A, but also as natural pigments, antioxidants, and health-promoting compounds. Lately, they are also attracting interest in the context of nutricosmetics, as they have been shown to provide cosmetic benefits when ingested in appropriate amounts. In this work, resulting from the collaborative work of participants of the COST Action European network to advance carotenoid research and applications in agro-food and health (EUROCAROTEN, www.eurocaroten.eu, https://www.cost.eu/actions/CA15136/#tabs|Name:overview) research on carotenoids in foods and feeds is thoroughly reviewed covering aspects such as analysis, carotenoid food sources, carotenoid databases, effect of processing and storage conditions, new trends in carotenoid extraction, daily intakes, use as human, and feed additives are addressed. Furthermore, classical and recent patents regarding the obtaining and formulation of carotenoids for several purposes are pinpointed and briefly discussed. Lastly, emerging research lines as well as research needs are highlighted.

The complex microbiome in aquaponics: significance of the bacterial ecosystem

Purpose

Aquaponics is a technology that has minimal impact on the environment and which is often promoted as a solution for sustainable food production. Developing aquaponics sustainably requires a thorough understanding of the biological components of the system. Recent reports on the bacterial populations of aquaponics systems using new DNA sequencing technologies are revealing a complex and diverse microbial ecosystem. The purpose of this review is to present information on microbial composition and various factors affecting bacterial activity in aquaponics systems. Approaches for establishing a bacterial ecosystem during the setup of an aquaponics system, and microbiological safety of aquaponics products are also highlighted.

Methods

This review was developed by evaluating and synthesising current literature of peer-reviewed publications related to aquaponics and microbial communities. Based on the results from credible academic journals, publications were categorised into five groups: methods used to characterise microbiomes, biofiltration microorganisms, bacterial diversity, biofilter establishment, and safety of aquaponics products.

Results

The microbial ecosystem is essential for biological filtration of water through the mineralisation of nutrients required for plant growth in an integrated system. The aquaponics microbiome is complex, and bacterial composition varies between the different compartments of these systems. Establishing these bacterial ecosystems is essential for optimal functioning of aquaponics. At the phylum level, Proteobacteria and Bacteroidetes are dominant in aquaponics systems. Despite bacteria being fundamental to aquaponics, there are currently no reports of human pathogens in aquaponics products.

Conclusion

Knowledge of the composition of bacterial populations in aquaponics systems will enhance understanding of relationships and functions within the microbiome. This in turn will allow for the establishment of sustainable and healthy aquaponics systems for food production.

Identification of biomarkers to diagnose diseases and find adverse drug reactions by metabolomics

Metabolomics has been widely used for investigating the biological functions of disease expression and has the potential to discover biomarkers in circulating biofluids or tissue extracts that reflect in phenotypic changes. Metabolic profiling has advantages because of the use of unbiased techniques, including multivariate analysis, and has been applied in pharmacological studies to predict therapeutic and adverse reactions of drugs, which is called pharmacometabolomics (PMx). Nuclear magnetic resonance (NMR)- and mass spectrometry (MS)-based metabolomics has contributed to the discovery of recent disease biomarkers; however, the optimal strategy for the study purpose must be selected from many established protocols, methodologies and analytical platforms. Additionally, information on molecular localization in tissue is essential for further functional analyses related to therapeutic and adverse effects of drugs in the process of drug development. MS imaging (MSI) is a promising technology that can visualize molecules on tissue surfaces without labeling and thus provide localized information. This review summarizes recent uses of MS-based global and wide-targeted metabolomics technologies and the advantages of the MSI approach for PMx and highlights the PMx technique for the biomarker discovery of adverse drug effects.

Bioinformatics as a Tool for the Structural and Evolutionary Analysis of Proteins

This chapter deals with the topic of bioinformatics, computational, mathematics, and statistics tools applied to biology, essential for the analysis and characterization of biological molecules, in particular proteins, which play an important role in all cellular and evolutionary processes of the organisms. In recent decades, with the next generation sequencing technologies and bioinformatics, it has facilitated the collection and analysis of a large amount of genomic, transcriptomic, proteomic, and metabolomic data from different organisms that have allowed predictions on the regulation of expression, transcription, translation, structure, and mechanisms of action of proteins as well as homology, mutations, and evolutionary processes that generate structural and functional changes over time. Although the information in the databases is greater every day, all bioinformatics tools continue to be constantly modified to improve performance that leads to more accurate predictions regarding protein functionality, which is why bioinformatics research remains a great challenge.

Water Conservation and Plant Survival Strategies of Rhizobacteria under Drought Stress

Drylands are stressful environment for plants growth and production. Plant growth-promoting rhizobacteria (PGPR) acts as a rampart against the adverse impacts of drought stress in drylands and enhances plant growth and is helpful in agricultural sustainability. PGPR improves drought tolerance by implicating physio-chemical modifications called rhizobacterial-induced drought endurance and resilience (RIDER). The RIDER response includes; alterations of phytohormonal levels, metabolic adjustments, production of bacterial exopolysaccharides (EPS), biofilm formation, and antioxidant resistance, including the accumulation of many suitable organic solutes such as carbohydrates, amino acids, and polyamines. Modulation of moisture status by these PGPRs is one of the primary mechanisms regulating plant growth, but studies on their effect on plant survival are scarce in sandy/desert soil. It was found that inoculated plants showed high tolerance to water-deficient conditions by delaying dehydration and maintaining the plant’s water status at an optimal level. PGPR inoculated plants had a high recovery rate after rewatering interms of similar biomass at flowering compared to non-stressed plants. These rhizobacteria enhance plant tolerance and also elicit induced systemic resistance of plants to water scarcity. PGPR also improves the root growth and root architecture, thereby improving nutrient and water uptake. PGPR promoted accumulation of stress-responsive plant metabolites such as amino acids, sugars, and sugar alcohols. These metabolites play a substantial role in regulating plant growth and development and strengthen the plant’s defensive system against various biotic and abiotic stresses, in particular drought stress.

Metabolomic analysis of organic acids, amino acids, and fatty acids in plasma of Hanwoo beef on a high-protein diet

INTRODUCTION

Ketoacidosis of metabolic disease showed in beef cattle although body weight was increased by high-grain diets (HGDs). However, few studies have examined for health status related with metabolic disease of ketoacidosis following high-protein diet (HPD).

OBJECTIVES

Metabolomic analysis was performed for the monitoring of health status associated with metabolic disease of ketoacidosis in the plasma of Hanwoo heifers following a HPD.

METHODS

Hanwoo heifers of 24 months with 459 ± 42 kg weight were used under a 2 × 2 crossover design. The plasma was collected from control (n = 5) and HPD group (n = 5) on day 21 following diet adaptation for 20 days. Metabolic profiling analysis of organic acids (OAs), amino acids (AAs) and fatty acids (FAs) by gas chromatography-tandem mass spectrometry combined with star pattern analysis was performed in plasma. Levels of OAs, AAs and FAs were evaluated by Mann-Whitney test, PCA and PLS-DA.

RESULTS

In HPD group, ketoacidosis as metabolic disease was monitored by elevated acetoacetic acid and 3-hydroxybutyric acid. In addition, the elevation of ketogenic AAs, reduction of medium chain FAs and OAs with energy metabolism in TCA cycle were monitored in HPD group. Star graphic pattern was characteristic and readily distinguished between control and HPD groups. In PLS-DA, two groups were separated with VIP score of top-ranked 10 FAs as important metabolites for discrimination.

CONCLUSION

Elevation of ketone body including ketogenic AAs and reduced energy metabolism of FAs and OAs may useful for evaluation of health states associated with ketoacidosis from metabolic event by HPD in beef cattle.

Experimental races of Capsicum annuum cv. jalapeño: Chemical characterization and classification by 1H NMR/machine learning

This work reports on the metabolic fingerprinting of ten new races of Capsicum annuum cv. jalapeño using ¹H NMR based metabolomics coupled to machine learning projections. Ten races were classified and evaluated according to their differential metabolites, variables of commercial interest and by multivariate data analysis/machine learning algorithm. According to our results, experimental races of jalapeño peppers exhibited differences in carbohydrate, amino acid, nucleotide and organic acid contents. Forty-eight metabolites were identified by 1D and 2D NMR and the differential metabolites were quantified by qNMR. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) separated the studied races into two groups. The group A included the races Colosus, Emperador, Fundador and Rayo whereas the group B included the races Don Benito, SMJ 1416, SMJ 1417, SMJ 1423, SMJ 145 and STAM J0904. OPLS-DA revealed that levels of citric acid in group A were higher than in group B, while the levels of asparagine, fumaric acid, GABA, glucose, malic acid, pyruvic, quinic acid, sucrose and tryptophan were higher in the group B. Remarkably, ascorbic acid was exclusively found in the race Colosus. Random forest model revealed the diversity of the experimental races and the similarity rate with the well-established races. The most relevant variables used to generate a model were length, weight, yield, width, xylose content and organic acids content.

Do polystyrene nanoplastics affect the toxicity of cadmium to wheat (Triticum aestivum L.)?

There has been an increase on the research of microplastics (<5 mm in diameter) as carriers for toxic chemicals to evaluate their risks for human health and environment, but only few works focused on nanoplastics (1 nm-1000 nm in diameter) interacting with pre-existing contaminants such as heavy metals. It is still unclear whether polystyrene nanoplastics (PSNPs) could affect the toxicity of cadmium to wheat (Triticum aestivum L.). Here, we assessed the impact of polystyrene nanoplastics (0, 10 mg/L) on the Cd (0, 20 μM) toxicity to wheat grown in 25% Hoagland solution for three weeks. We found that the presence of PSNPs could partially reduce Cd contents in leaves and alleviate Cd toxicity to wheat, which might be due to weakened adsorption capacity of PSNPs affected by ionic strength. In addition, PSNPs have little effect on catalase (CAT), peroxidase (POD) activities, except for decreasing superoxide dismutase (SOD) activity, which suggested that antioxidant defense systems might not be the main mechanism to reduce the oxidative damage induced by Cd in wheat. Electron paramagnetic resonance (EPR) analysis showed that PSNPs could accelerate the formation of long-lived radicals in leaves after exposure to Cd. Notably, our metabolomics profiling further indicated that the simultaneously elevated carbohydrate and amino acid metabolisms induced by PSNPs could partly alleviate Cd toxicity to wheat. Nevertheless, the present study provides important implications for the toxicological interaction and future risk assessment of co-contamination of nanoplastics and heavy metals in the environment.

NMR metabolomic analysis of bacterial resistance pathways using multivalent quaternary ammonium functionalized macromolecules

INTRODUCTION

Multivalent antimicrobial dendrimers are an exciting new system that is being developed to address the growing problem of drug resistant bacteria. Nuclear Magnetic Resonance (NMR) metabolomics is a quantitative and reproducible method for the determination of bacterial response to environmental stressors and for visualization of perturbations to biochemical pathways.

OBJECTIVES

NMR metabolomics is used to elucidate metabolite differences between wild type and antimicrobially mutated Escherichia coli (E. coli) samples.

METHODS

Proton (¹H) NMR hydrophilic metabolite analysis was conducted on samples of E. coli after 33 growth cycles of a minimum inhibitory challenge to E. coli by poly(amidoamine) dendrimers functionalized with mannose and with C₁₆-DABCO quaternary ammonium endgroups and compared to the metabolic profile of wild type E. coli.

RESULTS

The wild type and mutated E. coli samples were separated into distinct sample sets by hierarchical clustering, principal component analysis (PCA) and sparse partial least squares discriminate analysis (sPLS-DA). Metabolite components of membrane fortification and energy related pathways had a significant p value and fold change between the wild type and mutated E. coli. Amino acids commonly associated with membrane fortification from cationic antimicrobials, such as lysine, were found to have a higher concentration in the mutated E. coli than in the wild type E. coli. N-acetylglucosamine, a major component of peptidoglycan synthesis, was found to have a 25-fold higher concentration in the mid log phase of the mutated E. coli than in the mid log phase of the wild type.

CONCLUSION

The metabolic profile suggests that E. coli change their peptidoglycan composition in order to garner protection from the highly positively charged and multivalent C₁₆-DABCO and mannose functionalized dendrimer.