Mass spectrometry-based metabolomics

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

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

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

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

The Potential of Metabolomics in Biomedical Applications

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

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

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

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

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

Surfing the Big Data Wave: Omics Data Challenges in Transplantation

In both research and care, patients, caregivers, and researchers are facing a leap forward in the quantity of data that are available for analysis and interpretation, marking the daunting "big data era." In the biomedical field, this quantitative shift refers mostly to the -omics that permit measuring and analyzing biological features of the same type as a whole. Omics studies have greatly impacted transplantation research and highlighted their potential to better understand transplant outcomes. Some studies have emphasized the contribution of omics in developing personalized therapies to avoid graft loss. However, integrating omics data remains challenging in terms of analytical processes. These data come from multiple sources. Consequently, they may contain biases and systematic errors that can be mistaken for relevant biological information. Normalization methods and batch effects have been developed to tackle issues related to data quality and homogeneity. In addition, imputation methods handle data missingness. Importantly, the transplantation field represents a unique analytical context as the biological statistical unit is the donor-recipient pair, which brings additional complexity to the omics analyses. Strategies such as combined risk scores between 2 genomes taking into account genetic ancestry are emerging to better understand graft mechanisms and refine biological interpretations. The future omics will be based on integrative biology, considering the analysis of the system as a whole and no longer the study of a single characteristic. In this review, we summarize omics studies advances in transplantation and address the most challenging analytical issues regarding these approaches.

LC-HRMS based Targeted Metabolomics for High-throughput and Quantitative Analysis of 21 Growth Inhibition Related Metabolites in CHO Cell Fed-Batch Cultures

Chinese hamster ovary (CHO) cells have been widely used in the biopharmaceutical industry for production of therapeutic proteins. CHO cells in fed-batch cultures produce various amino acid-derived intermediate metabolites. These small molecule metabolic byproducts have proven to be critical to cell growth, culture performance, and more interestingly antibody drug productivity. Herein, we developed a liquid chromatography - high resolution mass spectrometry (LC-HRMS) based targeted metabolomics approach for comprehensive quantification of total 21 growth inhibition related metabolites generated from 14 different amino acids in CHO cell fed-batch cultures. High throughput derivatization procedures, matrix-matched calibration curves, stable isotope-labeled internal standards, and accurate mass Full MS scan were utilized in order to achieve our goal for wide scope of metabolite screening as well as validity and reliability of metabolite quantification. We further present a novel analytical strategy for extending the assay's dynamic range by utilizing naturally occurring isotope M+1 ion as a quantification analogue in the circumstances where the principal M ion is beyond its calibration range. The integrated method was qualified for selectivity, sensitivity, linearity, accuracy, precision, isotope analysis and other analytical aspects to demonstrate assay robustness. We then applied this metabolomics approach to characterize metabolites of interest in a CHO cell based monoclonal antibody (mAb) production process with fed-batch bioreactor culture mode. Absolute quantification combined with multivariate statistical analysis illustrated that our target analytes derived from amino acids, especially from branched-chain amino acids, closely correlated with cell viability and significantly differentiated cellular stages in production process.

Impact of the pre-examination phase on multicenter metabolomic studies

The development of metabolomics in clinical applications has been limited by the lack of validation in large multicenter studies. Large population cohorts and their biobanks are a valuable resource for acquiring insights into molecular disease mechanisms. Nevertheless, most of their collections are not tailored for metabolomics and have been created without specific attention to the pre-analytical requirements for high-quality metabolome assessment. Thus, comparing samples obtained by different pre-analytical procedures remains a major challenge. Here, ¹H NMR-based analyses are used to demonstrate how human serum and plasma samples collected with different operating procedures within several large European cohort studies from the Biobanking and Biomolecular Resources Infrastructure - Large Prospective Cohorts (BBMRI-LPC) consortium can be easily revealed by supervised multivariate statistical analyses at the initial stages of the process, to avoid biases in the downstream analysis. The inter-biobank differences are discussed in terms of deviations from the validated CEN/TS 16945:2016 / ISO 23118:2021 norms. It clearly emerges that biobanks must adhere to the evidence-based guidelines in order to support wider-scale application of metabolomics in biomedicine, and that NMR spectroscopy is informative in comparing the quality of different sample sources in multi cohort/center studies.

A systematic review: metabolomics-based identification of altered metabolites and pathways in the skin caused by internal and external factors

The skin's ability to function optimally is affected by many diverse factors. Metabolomics has a great potential to improve our understanding of the underlying metabolic changes and the affected pathways. Therefore, the objective of this study was to review the current state of the literature and to perform further metabolic pathway analysis on the obtained data. The aim was to gain an overview of the metabolic changes under altered conditions and to identify common and different patterns as a function of the investigated factors. A cross-study comparison of the extracted studies from different databases identified 364 metabolites, whose concentrations were considerably altered by the following factor groups: irradiation, xenobiotics, aging, and skin diseases (mainly psoriasis). Using metabolic databases and pathway analysis tools the individual metabolites were assigned to the corresponding metabolic pathways and the most strongly affected signaling pathways were identified. All factors induced oxidative stress. Thus, antioxidant defense systems, especially coenzyme Q₁₀  (aging) and the glutathione system (irradiation, aging, xenobiotics) were impacted. Lipid metabolism was also impacted by all factors studied. The carnitine shuttle as part of β-oxidation was activated by all factor groups except aging. Glycolysis, Krebs (TCA) cycle and purine metabolism were mainly affected by irradiation and xenobiotics. The pentose phosphate pathway was activated and Krebs cycle was downregulated in response to oxidative stress. In summary, it can be ascertained that mainly energy metabolism, lipid metabolism, antioxidative defense and DNA repair systems were impacted by the factors studied.

Role of TlyA in the Biology of Uncultivable Mycobacteria

TlyA proteins are related to distinct functions in a diverse spectrum of bacterial pathogens including mycobacterial spp. There are several annotated proteins function as hemolysin or pore forming molecules that play an important role in the virulence of pathogenic organisms. Many studies reported the dual activity of mycobacterial TlyA as 'hemolysin' and 'S-adenosylmethionine dependent rRNA methylase'. To act as a hemolysin, a sequence must have a signal sequence and transmembrane segment which helps the protein to enter the extracellular environment. Interestingly, the mycobacterial tlyA has neither a traditional signal sequences of general/sec/tat pathways nor any transmembrane segments are present. Still it can reach the extracellular milieu with the help of non-classical signal mechanisms. Also, retention of tlyA in cultivable mycobacterial pathogens (such as Mycobacterium tuberculosis and M. marinum) as well as uncultivated mycobacterial pathogens despite their extreme reductive evolution (such as M. leprae, M. lepromatosis and M. uberis) suggests its crucial role in evolutionary biology of pathogenic mycobacteria. Numerous virulence factors have been characterised from the uncultivable mycobacteria but the information of TlyA protein is still limited in terms of molecular and structural characterisation. The genomic insights offered by comparative analysis of TlyA sequences and its conserved domains reveal its pore forming activity which further confirms its role as a virulence protein, particularly in uncultivable mycobacteria. Therefore, this review presents a comparative analysis of mycobacterial TlyA family by sequence homology and alignment to improve our understanding of this unconventional hemolysin and RNA methyltransferase TlyA of uncultivable mycobacteria.

Polyploidization Increases the Lipid Content and Improves the Nutritional Quality of Rice

Plant polyploidization is frequently associated with changes in nutrient contents. However, the possible contribution of metabolites to this change has not been investigated by characterizing the metabolite contents of diploid and tetraploid forms of rice (Oryza sativa L.). We compared the metabolites of a group of diploid-tetraploid japonica brown rice and a group of diploid-tetraploid indica brown rice based on liquid chromatography-tandem mass spectrometry. In total, 401 metabolites were identified; of these, between the two diploid-tetraploid groups, 180 showed opposite expression trends, but 221 showed the same trends (147 higher abundance vs. 74 lower abundance). Hierarchical cluster analysis of differential metabolites between diploid and tetraploid species showed a clear grouping pattern, in which the expression abundance of lipids, amino acids and derivatives, and phenolic acids increased in tetraploids. Further analysis revealed that the lipids in tetraploid rice increased significantly, especially unsaturated fatty acids and phospholipids. This study provides further basis for understanding the changes in rice nutritional quality following polyploidization and may serve as a new theoretical reference for breeding eutrophic or functional rice varieties via polyploidization.

Application of Intraoperative Mass Spectrometry and Data Analytics for Oncological Margin Detection, A Review

OBJECTIVE

A common phase of early-stage oncological treatment is the surgical resection of cancerous tissue. The presence of cancer cells on the resection margin, referred to as positive margin, is correlated with the recurrence of cancer and may require re-operation, negatively impacting many facets of patient outcomes. There exists a significant gap in the surgeons ability to intraoperatively delineate between tissues. Mass spectrometry methods have shown considerable promise as intraoperative tissue profiling tools that can assist with the complete resection of cancer. To do so, the vastness of the information collected through these modalities must be digested, relying on robust and efficient extraction of insights through data analysis pipelines.

METHODS

We review clinical mass spectrometry literature and prioritize intraoperatively applied modalities. We also survey the data analysis methods employed in these studies.

RESULTS

Our review outlines the advantages and shortcomings of mass spectrometry imaging and point-based tissue probing methods. For each modality, we identify statistical, linear transformation and machine learning techniques that demonstrate high performance in classifying cancerous tissues across several organ systems. A limited number of studies presented results captured intraoperatively.

CONCLUSION

Through continued research of data centric techniques, like mass spectrometry, and the development of robust analysis approaches, intraoperative margin assessment is becoming feasible.

SIGNIFICANCE

By establishing the relatively short history of mass spectrometry techniques applied to surgical studies, we hope to inform future applications and aid in the selection of suitable data analysis frameworks for the development of intraoperative margin detection technologies.

Targeted Metabolomic Approach to Assess the Reproducibility of Plasma Metabolites over a Four Month Period in a Free-Living Population

Metabolomics is increasingly applied to investigate diet–disease associations in nutrition research. However, studies of metabolite reproducibility are limited, which could hamper their use within epidemiologic studies. The objective of this study was to evaluate the metabolite reproducibility during 4 months in a free-living population. In the A-DIET Confirm study, fasting plasma and dietary data were collected once a month for 4 months. Metabolites were measured using liquid chromatography tandem mass spectrometry, and their reproducibility was estimated using the intraclass correlation coefficient (ICC). Regularized canonical correlation analysis (rCCA) was employed to examine the diet–metabolite associations. In total, 138 metabolites were measured, and median ICC values of 0.49 and 0.65 were found for amino acids and biogenic amines, respectively. Acylcarnitines, lysophosphatidylcholines, phosphatidylcholines, and sphingomyelins had median ICC values of 0.69, 0.66, 0.63, and 0.63, respectively. The median ICC for all metabolites was 0.62, and 54% of metabolites had ICC values ≥0.60. Additionally, the rCCA heat map revealed positive correlations between dairy/meat intake and specific lipids. In conclusion, more than half of the metabolites demonstrated good to excellent reproducibility. A single measurement per subject could appropriately reflect the metabolites’ long-term concentration levels and may also be sufficient for assessing disease risk in epidemiologic studies. The study data are deposited in MetaboLights (MTBLS3428 (www.ebi.ac.uk/metabolights)).

Nano liquid chromatography, an updated review

Low flow chromatography has a rich history of innovation but has yet to reach widespread implementation in bioanalytical applications. Improvements in pump technology, microfluidic connections, and nano-electrospray sources for mass spectrometry have laid the groundwork for broader application, and innovation in this space has accelerated in recent years. This article reviews the instrumentation used for nano-flow liquid chromatography , the types of columns employed, and strategies for multi-dimensionality of separations, which is key to the future state of the technique to the high-throughput needs of modern bioanalysis. An update of the current applications where nano-LC is widely used, such as proteomics and metabolomics, is discussed. But the trend towards biopharmaceutical development of increasingly complex, targeted, and potent therapeutics for the safe treatment of disease drives the need for ultimate selectivity and sensitivity of our analytical platforms for targeted quantitation in a regulated space. The selectivity needs are best addressed by mass spectrometric detection, especially at high resolutions, and exquisite sensitivity is provided by nano-electrospray ionization as the technology continues to evolve into an accessible, robust, and easy to use platform.

Review of -omics studies on mosquito-borne viruses of the Flavivirus genus

Arboviruses are transmitted by arthropods (arthropod-borne virus) which can be mosquitoes or other hematophagous arthropods, in which their life cycle occurs before transmission to other hosts. Arboviruses such as Dengue, Zika, Saint Louis Encephalitis, West Nile, Yellow Fever, Japanese Encephalitis, Rocio and Murray Valley Encephalitis viruses are some of the arboviruses transmitted biologically among vertebrate hosts by blood-taking vectors, mainly Aedes and Culex sp., and are associated with neurological, viscerotropic, and hemorrhagic reemerging diseases, posing as significant health and socioeconomic concern, as they become more and more adaptive to new environments, to arthropods vectors and human hosts. One of the main families that include mosquito-borne viruses is Flaviviridae, and here, we review the case of the Flavivirus genus, which comprises the viruses cited above, using a variety of research approaches published in literature, including genomics, transcriptomics, proteomics, metabolomics, etc., to better understand their structures as well as virus-host interactions, which are essential for development of future antiviral therapies.

Application of multivariate data analysis for food quality investigations: An example-based review

These days, large multivariate data sets are common in the food research area. This is not surprising as food quality, which is important for consumers, and its changes are the result of a complex interplay of multiple compounds and reactions. In order to comprehensively extract information from these data sets, proper data analysis tools should be applied. The application of multivariate data analysis (MVDA) is therefore highly recommended. However, at present the use of MVDA for food quality investigations is not yet fully explored. This paper focusses on a number of MVDA methods (PCA (Principal Component Analysis), PLS (Partial Least Squares Regression), PARAFAC (Parallel Factor Analysis) and ASCA (ANOVA Simultaneous Component Analysis)) useful for food quality investigations. The terminology, main steps and the theoretical basis of each method will be explained. As this is an example-based review, each method was applied on the same experimental data set to give the reader an idea about each selected MVDA method and to make a comparison between the outcomes. Numerous MVDA methods are available in literature. Which method to select depends on the data set and objective. PCA should be the first choice for data exploration of two-dimensional data. For predictive purposes, PLS is the most appropriate method. Given an underlying experimental design, ASCA takes into account both the relation between the different variables and the design factors. In case of a multi-way data set, PARAFAC can be used for data exploration. While these methods have already proven their value in research, there is a need to further explore their potential to investigate the complex interplay of compounds and reactions contributing to food quality. With this work we would like to encourage food scientists with no or limited knowledge of MVDA to get some first insights into the selected methods.

Metabolomics-based engineering for biofuel and bio-based chemical production in microalgae and cyanobacteria: A review

Metabolomics, an essential tool in modern synthetic biology based on the design-build-test-learn platform, is useful for obtaining a detailed understanding of cellular metabolic mechanisms through comprehensive analyses of the metabolite pool size and its dynamic changes. Metabolomics is critical to the design of a rational metabolic engineering strategy by determining the rate-limiting reaction and assimilated carbon distribution in a biosynthetic pathway of interest. Microalgae and cyanobacteria are promising photosynthetic producers of biofuels and bio-based chemicals, with high potential for developing a bioeconomic society through bio-based carbon neutral manufacturing. Metabolomics technologies optimized for photosynthetic organisms have been developed and utilized in various microalgal and cyanobacterial species. This review provides a concise overview of recent achievements in photosynthetic metabolomics, emphasizing the importance of microalgal and cyanobacterial cell factories that satisfy industrial requirements.

Salivary metabolite signatures of oral cancer and leukoplakia through gas chromatography-mass spectrometry

Background

Saliva contains a large array of metabolites, many of which can be informative for the detection of diseases. Gas chromatography-mass spectrometry (GC-MS) is a system that has long been used for metabolite profiling owing to its sensitivity, specificity, reproducibility and synchronized analysis; it has relatively broad coverage of compound classes including sugars, sugar alcohols, glycosides and lipophilic compounds.

Aim and Objectives

The present study was conducted to explore the use of GC-MS in assessing variation in salivary metabolites and to recognize the metabolites which can be used as disease diagnostic tools and metabolite markers for detection of oral squamous cell carcinoma.

Materials and Methods

The present study included clinically and histopathologically confirmed oral squamous cell carcinoma (OSCC) and oral leukoplakia patients (OLK) and the control group. Patients were divided into three groups: OSCC (n = 30), OLK (n = 30) and healthy individuals as controls (n = 30). Patients were refrained from eating, drinking, smoking or oral hygiene procedures for at least 1.5 h before the collection. Saliva was collected between 9.00 and 10.00 am. Samples were stored at -80°C. Filtered samples were used for GC-MS.

Results

Fifteen compounds differed significantly between control, OLK and OSCC. These metabolites were decanedioic acid, 2-methyloctacosane, eicosane, octane, 3,5-dimethyl, pentadecane, hentriacontane, 5, 5-diethylpentadecane, nonadecane, oxalic acid, 6-phenylundecanea, l-proline, 2-furancarboxamide, 2-isopropyl-5-methyl-1-heptanol, pentanoic acid, Docosane.

Conclusion

The findings of the study suggest the application of salivary metabolomics as a promising tool in the identification of tumor-specific biomarkers in early diagnosis and prediction of OSCC and oral leukoplakia. In future, standardizing the protocol for salivary analysis and overcoming some of the limitations will be helpful to establish salivary metabolomics as a reliable, the highly sensitive and specific method for clinical use as an independent diagnostic aid.

Dynamic metabolic change of cancer cells induced by natural killer cells at single-cell level studied by label-free mass cytometry

Natural killer cells (NK cells) are important immune cells which have attracted increasing attention in cancer immunotherapy. Due to the heterogeneity of cells, individual cancer cells show different resistance to NK cytotoxicity, which has been revealed by flow cytometry. Here we used label-free mass cytometry (CyESI-MS) as a new tool to analyze the metabolites in Human Hepatocellular Carcinoma (HepG2) cells at the single-cell level after the interaction with different numbers of NK92 MI cells. A large amount of chemical information from individual HepG2 cells was obtained showing the process of cell apoptosis induced by NK cells. Nineteen metabolites which consecutively change during cell apoptosis were revealed by calculating their average relative intensity. Four metabolic pathways were impacted during cell apoptosis which hit 4 metabolites including glutathione (GSH), creatine, glutamic acid and taurine. We found that the HepG2 cells could be divided into two phenotypes after co-culturing with NK cells according to the bimodal distribution of concentration of these 4 metabolites. The correlation between metabolites and different apoptotic pathways in the early apoptosis cell group was established by the 4 metabolites at the single-cell level. This is a new idea of using single-cell specific metabolites to reveal the metabolic heterogeneity in cell apoptosis which would be a powerful means for evaluating the cytotoxicity of NK cells.

Label-free mass cytometry is utilized to study the dynamic metabolic change during apoptosis in HepG2 cells induced by NK92 MI cells at the single-cell level. The metabolic heterogeneity of individual HepG2 cells during apoptosis was revealed.

Metabolomics reveals changes in metabolite profiles due to growth and metamorphosis during the ontogeny of the northern damselfly

Many insects have complex life cycles where a drastic ontogenetic change happens between the larval stages and the adult stage, i.e. metamorphosis. Damselflies (order Odonata, suborder Zygoptera) are widely distributed and ecologically important semi-aquatic insects with a complex life cycle. Phenotypic changes over damselfly ontogeny have been documented, however, if and how metabolite profiles are also changing is currently unknown. Here we used a metabolomics methodology to gain insights into the metabolic changes during the life cycle of the Northern damselfly (Coenagrion hastulatum). Hatchlings of wild-caught damselflies were reared in the laboratory and metabolomics analyses using liquid chromatography and gas chromatography coupled to mass spectrometry were carried out at three larval stages and on adult damselflies. Additionally, a subset of larvae was exposed to wastewater effluent to assess how metabolite profiles responded to an environmental stressor. A total of 212 compounds belonging to several classes (e.g. amino acids, fatty acids, sugars) were annotated. Across metamorphosis, we found that damselflies shifted from protein catabolism to lipid catabolism. Wastewater effluent exposure resulted in ontogenetic stage-dependent changes of individual metabolites, but not to a marked extent. Overall, our study is one of the first to describe changes of metabolite profiles during ontogeny of an insect, and it provides a first step towards a greater understanding of the physiological changes occurring during general insect-but especially damselfly-ontogeny.

Inorganic Matrices Assisted Laser Desorption/Ionization Mass Spectrometry for Metabolic Analysis in Bio-fluids

Metabolic analysis in bio-fluids interprets the end products in the bio-process, emerging as an irreplaceable disease diagnosis and monitoring platform. Laser desorption/ionization mass spectrometry (LDI MS) based metabolic analysis exhibits great potential for clinical applications in terms of high throughput, rapid signal readout, and minimal sample preparation. There are two essential elements to construct the LDI MS-based metabolic analysis: 1) well-designed nanomaterials as matrices; 2) machine learning algorithms for data analysis. This review highlights the development of various inorganic matrices to comprehend the advantages of LDI MS in metabolite detection and the recent diagnostic applications based on target metabolite detection and untargeted metabolic fingerprints in biological fluids.

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

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

Opening the Random Forest Black Box of the Metabolome by the Application of Surrogate Minimal Depth

For the untargeted analysis of the metabolome of biological samples with liquid chromatography–mass spectrometry (LC-MS), high-dimensional data sets containing many different metabolites are obtained. Since the utilization of these complex data is challenging, different machine learning approaches have been developed. Those methods are usually applied as black box classification tools, and detailed information about class differences that result from the complex interplay of the metabolites are not obtained. Here, we demonstrate that this information is accessible by the application of random forest (RF) approaches and especially by surrogate minimal depth (SMD) that is applied to metabolomics data for the first time. We show this by the selection of important features and the evaluation of their mutual impact on the multi-level classification of white asparagus regarding provenance and biological identity. SMD enables the identification of multiple features from the same metabolites and reveals meaningful biological relations, proving its high potential for the comprehensive utilization of high-dimensional metabolomics data.

Metabolomics facilitate the personalized management in inflammatory bowel disease

Inflammatory bowel disease (IBD) is a gastrointestinal disorder characterized by chronic relapsing inflammation and mucosal lesions. Reliable biomarkers for monitoring disease activity, predicting therapeutic response, and disease relapse are needed in the personalized management of IBD. Given the alterations in metabolomic profiles observed in patients with IBD, metabolomics, a new and developing technique for the qualitative and quantitative study of small metabolite molecules, offers another possibility for identifying candidate markers and promising predictive models. With increasing research on metabolomics, it is gradually considered that metabolomics will play a significant role in the management of IBD. In this review, we summarize the role of metabolomics in the assessment of disease activity, including endoscopic activity and histological activity, prediction of therapeutic response, prediction of relapse, and other aspects concerning disease management in IBD. Furthermore, we describe the limitations of metabolomics and highlight some solutions.

Development of simultaneous quantitative analysis of tricarboxylic acid cycle metabolites to identify specific metabolites in cancer cells by targeted metabolomic approach

The tricarboxylic acid (TCA) cycle is one of the most important pathways of energy metabolism, and the profiles of its components are influenced by factors such as diseases and diets. Therefore, the differences in metabolic profile of TCA cycle between healthy and cancer cells have been the focus of studies to understand pathological conditions. In this study, we developed a quantitative method to measure TCA cycle metabolites using LC-MS/MS to obtain useful metabolic profiles for development of diagnostic and therapeutic methods for cancer. We successfully analyzed 11 TCA cycle metabolites by LC MS/MS with high reproducibility by using a PFP column with 0.5% formic acid as a mobile phase. Next, we analyzed the concentration of TCA cycle metabolites in human cell lines (HaCaT: normal skin keratinocytes; A431: skin squamous carcinoma cells; SW480: colorectal cancer cells). We observed reduced concentration of succinate and increased concentration of citrate, 2-hydroxyglutarate, and glutamine in A431 cells as compared with HaCaT cells. On the other hand, decreased concentration of isocitrate, fumarate, and α-ketoglutarate and increased concentration of malate, glutamine, and glutamate in A431 cells were observed in comparison with SW480 cells. These findings suggested the possibility of identifying disease-specific metabolites and/or organ-specific metabolites by using this targeted metabolomic analysis.

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.

Metabolomics Work Flow and Analytics in Systems Biology

Metabolomics is an omics approach of systems biology that involves the development and assessment of large-scale, comprehensive biochemical analysis tools for metabolites in biological systems. This review describes the metabolomics workflow and provides an overview of current analytic tools used for the quantification of metabolic profiles. We explain analytic tools such as mass spectrometry (MS), nuclear magnetic resonance (NMR) spectroscopy, ionization techniques, and approaches for data extraction and analysis.

Statistical mediation of the relationships between chronological age and lipoproteins by nonessential amino acids in healthy men

Aging is a major risk factor for metabolic impairment that may lead to age-related diseases such as cardiovascular disease. Different mechanisms that may explain the interplay between aging and lipoproteins, and between aging and low-molecular-weight metabolites (LMWMs), in the metabolic dysregulation associated with age-related diseases have been described separately. Here, we statistically evaluated the possible mediation effects of LMWMs on the relationships between chronological age and lipoprotein concentrations in healthy men ranging from 19 to 75 years of age. Relative and absolute concentrations of LMWMs and lipoproteins, respectively, were assessed by nuclear magnetic resonance (NMR) spectroscopy. Multivariate linear regression and mediation analysis were conducted to explore the associations between age, lipoproteins and LMWMs. The statistical significance of the identified mediation effects was evaluated using the bootstrapping technique, and the identified mediation effects were validated on a publicly available dataset. Chronological age was statistically associated with five lipoprotein classes and subclasses. The mediation analysis showed that serine mediated 24.1% (95% CI: 22.9 – 24.7) of the effect of age on LDL-P, and glutamate mediated 17.9% (95% CI: 17.6 – 18.5) of the effect of age on large LDL-P. In the publicly available data, glutamate mediated the relationship between age and an NMR-derived surrogate of cholesterol. Our results suggest that the age-related increase in LDL particles may be mediated by a decrease in the nonessential amino acid glutamate. Future studies may contribute to a better understanding of the potential biological role of glutamate and LDL particles in aging mechanisms and age-related diseases.

The Preventive Effect of Cardiac Sympathetic Denervation Induced by 6-OHDA on Myocardial Ischemia-Reperfusion Injury: The Changes of lncRNA/circRNAs-miRNA-mRNA Network of the Upper Thoracic Spinal Cord in Rats

In this study, we investigated whether chemical 6-hydroxydopamine (6-OHDA) stimuli caused cardiac sympathetic denervation (SD), and we analyzed gene expression profiles to determine the changes in the lncRNA/circRNAs-miRNA-mRNA network in the affected spinal cord segments to identify putative target genes and molecular pathways in rats with myocardial ischemia–reperfusion injury (MIRI). Our results showed that cardiac sympathetic denervation induced by 6-OHDA alleviated MIRI. Compared with the ischemia reperfusion (IR, MIRI model) group, there were 148 upregulated and 51 downregulated mRNAs, 165 upregulated and 168 downregulated lncRNAs, 70 upregulated and 52 downregulated circRNAs, and 12 upregulated and 11 downregulated miRNAs in the upper thoracic spinal cord of the SD-IR group. Furthermore, we found that the differential genes related to cellular components were mainly enriched in extracellular and cortical cytoskeleton, and molecular functions were mainly enriched in chemokine activity. Pathway analysis showed that the differentially expressed genes were mainly related to the interaction of cytokines and cytokine receptors, sodium ion reabsorption, cysteine and methionine metabolism, mucoglycan biosynthesis, cGMP-PKG signaling pathway, and MAPK signaling pathway. In conclusion, the lncRNA/circRNAs-miRNA-mRNA networks in the upper thoracic spinal cord play an important role in the preventive effect of cardiac sympathetic denervation induced by 6-OHDA on MIRI, which offers new insights into the pathogenesis of MIRI and provides new targets for MIRI.

Metabolites involved in purine degradation, insulin resistance, and fatty acid oxidation are associated with prediction of Gestational diabetes in plasma

INTRODUCTION

Gestational diabetes mellitus (GDM) significantly increases maternal and fetal health risks, but factors predictive of GDM are poorly understood.

OBJECTIVES

Plasma metabolomics analyses were conducted in early pregnancy to identify potential metabolites associated with prediction of GDM.

METHODS

Sixty-eight pregnant women with overweight/obesity from a clinical trial of a lifestyle intervention were included. Participants who developed GDM (n = 34; GDM group) were matched on treatment group, age, body mass index, and ethnicity with those who did not develop GDM (n = 34; Non-GDM group). Blood draws were completed early in pregnancy (10-16 weeks). Plasma samples were analyzed by UPLC-MS using three metabolomics assays.

RESULTS

One hundred thirty moieties were identified. Thirteen metabolites including pyrimidine/purine derivatives involved in uric acid metabolism, carboxylic acids, fatty acylcarnitines, and sphingomyelins (SM) were different when comparing the GDM vs. the Non-GDM groups (p < 0.05). The most significant differences were elevations in the metabolites' hypoxanthine, xanthine and alpha-hydroxybutyrate (p < 0.002, adjusted p < 0.02) in GDM patients. A panel consisting of four metabolites: SM 14:0, hypoxanthine, alpha-hydroxybutyrate, and xanthine presented the highest diagnostic accuracy with an AUC = 0.833 (95% CI: 0.572686-0.893946), classifying as a "very good panel".

CONCLUSION

Plasma metabolites mainly involved in purine degradation, insulin resistance, and fatty acid oxidation, were altered in early pregnancy in connection with subsequent GDM development.

Sample-to-analysis platform for rapid intracellular mass spectrometry from small numbers of cells

Real-time, advanced diagnostics of the biochemical state within cells remains a significant challenge for research and development, production, and application of cell-based therapies. The fundamental biochemical processes and mechanisms of action of such advanced therapies are still largely unknown, including the critical quality attributes that correlate to therapeutic function, performance, and potency and the critical process parameters that impact quality throughout cell therapy manufacturing. An integrated microfluidic platform has been developed for in-line analysis of a small number of cells via direct infusion nano-electrospray ionization mass spectrometry. Central to this platform is a microfabricated cell processing device that prepares cells from limited sample volumes removed directly from cell culture systems. The sample-to-analysis workflow overcomes the labor intensive, time-consuming, and destructive nature of existing mass spectrometry approaches for analysis of cells. By providing rapid, high-throughput analyses of the intracellular state, this platform enables untargeted discovery of critical quality attributes and their real-time, in-process monitoring.

Mapping enzyme catalysis with metabolic biosensing

Enzymes are represented across a vast space of protein sequences and structural forms and have activities that far exceed the best chemical catalysts; however, engineering them to have novel or enhanced activity is limited by technologies for sensing product formation. Here, we describe a general and scalable approach for characterizing enzyme activity that uses the metabolism of the host cell as a biosensor by which to infer product formation. Since different products consume different molecules in their synthesis, they perturb host metabolism in unique ways that can be measured by mass spectrometry. This provides a general way by which to sense product formation, to discover unexpected products and map the effects of mutagenesis.

The testing of engineered enzymes represents a bottleneck. Here the authors report a screening method combining microfluidics and mass spectrometry, to map the catalysis of a mutated enzyme, characterise the range of products generated and recover the sequences of variants with desired activities.

Mass Spectrometry-Based Technology and Workflows for Studying the Chemistry of Fungal Endophyte Derived Bioactive Compounds

Bioactive compounds have gained substantial attention in research and have conferred great advancements in the industrial and pharmacological fields. Highly diverse fungi and their metabolome serve as a big platform to be explored for their diverse bioactive compounds. Omics tools coupled with bioinformatics, statistical, and well-developed algorithm tools have elucidated immense knowledge about fungal endophyte derived bioactive compounds. Further, these compounds are subjected to chromatography-gas chromatography and liquid chromatography (LC), spectroscopy-nuclear magnetic resonance (NMR), and "soft ionization" technique-matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF) based analytical techniques for structural characterization. The mass spectrometry (MS)-based approach, being highly sensitive, reproducible, and reliable, produces quick and high-profile identification. Coupling these techniques with MS has resulted in a descriptive account of the identification and quantification of fungal endophyte derived bioactive compounds. This paper emphasizes the workflows of the above-mentioned techniques, their advancement, and future directions to study the unraveled area of chemistry of fungal endophyte-derived bioactive compounds.

Single-Cell Multiomics Analysis for Drug Discovery

Given the heterogeneity seen in cell populations within biological systems, analysis of single cells is necessary for studying mechanisms that cannot be identified on a bulk population level. There are significant variations in the biological and physiological function of cell populations due to the functional differences within, as well as between, single species as a result of the specific proteome, transcriptome, and metabolome that are unique to each individual cell. Single-cell analysis proves crucial in providing a comprehensive understanding of the biological and physiological properties underlying human health and disease. Omics technologies can help to examine proteins (proteomics), RNA molecules (transcriptomics), and the chemical processes involving metabolites (metabolomics) in cells, in addition to genomes. In this review, we discuss the value of multiomics in drug discovery and the importance of single-cell multiomics measurements. We will provide examples of the benefits of applying single-cell omics technologies in drug discovery and development. Moreover, we intend to show how multiomics offers the opportunity to understand the detailed events which produce or prevent disease, and ways in which the separate omics disciplines complement each other to build a broader, deeper knowledge base.

In Vitro Anticancer Activity of Imperata cylindrica Root's Extract toward Human Cervical Cancer and Identification of Potential Bioactive Compounds

Imperata cylindrica is traditionally used to cure several diseases including cancer, wounds, and hypertension. The present study was designed to investigate the anticancer activity of the methanolic root extract of I. cylindrica (IC-MeOH). The water-soluble tetrazolium-1 and colony formation assays were used to check the proliferation ability of the cells. Cell apoptosis and cell cycle were measured by flow cytometry-based fluorescence-activated cell sorting. The ultrahigh-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) analysis was used for the metabolites profiling of IC-MeOH. Based on high-mass accuracy, spectral data, and previous reports, tentative compound identifications were assigned. Our findings revealed that IC-MeOH inhibited the proliferation of HeLa and CaSki cells. The plant extract was also found to induce a concentration- and time-dependent apoptosis and cell cycle arrest in the G0/G1 phase (IC50 value) in CaSki cell line. Analysis of IC-MeOH permitted the identification of 10 compounds already reported for their anticancer activity, epicatechin, curcumin, (-)-yatein, caffeic acid, myricetin, jatrorrhizine, harmaline, cinnamaldehyde, dobutamine, and syringin. In conclusion, IC-MeOH is a rich source of cytotoxic metabolites that inhibits human cervical cancer proliferation via apoptosis and cell cycle arrest.

Metabolomics to understand placental biology: Where are we now?

Metabolomics, the application of analytical chemistry methodologies to survey the chemical composition of a biological system, is used to globally profile and compare metabolites in one or more groups of samples. Given that metabolites are the terminal end-products of cellular metabolic processes, or 'phenotype' of a cell, tissue, or organism, metabolomics is valuable to the study of the maternal-fetal interface as it has the potential to reveal nuanced complexities of a biological system as well as differences over time or between individuals. The placenta acts as the primary site of maternal-fetal exchange, the success of which is paramount to growth and development of offspring during pregnancy and beyond. Although the study of metabolomics has proven moderately useful for the screening, diagnosis, and understanding of the pathophysiology of pregnancy complications, the placental metabolome in the context of a healthy pregnancy remains poorly characterized and understood. Herein, we discuss the technical aspects of metabolomics and review the current literature describing the placental metabolome in human and animal models, in the context of health and disease. Finally, we highlight areas for future opportunities in the emerging field of placental metabolomics.

The complex role of microbial metabolic activity in fossilization

Bacteria play an important role in the fossilization of soft tissues; their metabolic activities drive the destruction of the tissues and also strongly influence mineralization. Some environmental conditions, such as anoxia, cold temperatures, and high salinity, are considered widely to promote fossilization by modulating bacterial activity. However, bacteria are extremely diverse, and have developed metabolic adaptations to a wide range of stressful conditions. Therefore, the influence of the environment on bacterial activity, and of their metabolic activity on fossilization, is complex. A number of examples illustrate that simple, general assumptions about the role of bacteria in soft tissue fossilization cannot explain all preservational pathways: (i) experimental results show that soft tissues of cnidaria decay less in oxic than anoxic conditions, and in the fossil record are found more commonly in fossil sites deposited under oxic conditions rather than anoxic environments; (ii) siderite concretions, which often entomb soft tissue fossils, precipitate due to a complex mixture of sulfate- and iron reduction by some bacterial species, running counter to original theories that iron reduction is the primary driver of siderite concretion growth; (iii) arthropod brains, now widely accepted to be preserved in many Cambrian fossil sites, are one of the first structures to decay in taphonomic experiments, indicating that their fossilization processes are complex and influenced by bacterial activity. In order to expand our understanding of the complex process of bacterially driven soft tissue fossilization, more research needs to be done, on fossils themselves and in taphonomic experiments, to determine how the complex variation in microbial metabolic activity influences decay and mineralization.

The Role of Chromatographic and Electromigration Techniques in Foodomics

Foodomics is the discipline aimed at studying the prevention of diseases by food, identifying chemical, biological and biochemical food contaminants, determining changes in genetically modified foods, identifying biomarkers able to confirm the authenticity and quality of foods or studying the safety, quality and traceability of foods, among other issues. It is mainly based on the use of genomic, transcriptomic, proteomic and metabolomic tools, among others, in order to understand the effect of food on animals and humans at the level of genes, messenger ribonucleic acid, proteins and metabolites. Since the first definition of Foodomics, a reasonable number of works have shown the extremely high possibilities of this discipline, which is highly based on the use of advanced analytical hyphenated techniques - especially for proteomics and metabolomics. This book chapter aims at providing a general description of the role of chromatographic and electromigration techniques that are currently being applied to achieve the main objectives of Foodomics, particularly in the proteomic and metabolomic fields, since most published works have been focused on these approaches, and to highlight relevant applications.

Toxicometabolomics: Small Molecules to Answer Big Toxicological Questions

Given the high biological impact of classical and emerging toxicants, a sensitive and comprehensive assessment of the hazards and risks of these substances to organisms is urgently needed. In this sense, toxicometabolomics emerged as a new and growing field in life sciences, which use metabolomics to provide new sets of susceptibility, exposure, and/or effects biomarkers; and to characterize in detail the metabolic responses and altered biological pathways that various stressful stimuli cause in many organisms. The present review focuses on the analytical platforms and the typical workflow employed in toxicometabolomic studies, and gives an overview of recent exploratory research that applied metabolomics in various areas of toxicology.

Plasma metabolomics of autism spectrum disorder and influence of shared components in proband families

Prevalence of autism spectrum disorder (ASD) has been increasing in the United States in the past decades. The exact mechanisms remain enigmatic, and diagnosis of the disease still relies primarily on assessment of behavior. We first used a case-control design (75 idiopathic cases and 29 controls, enrolled at Boston Children's Hospital from 2007-2012) to identify plasma biomarkers of ASD through a metabolome-wide association study approach. Then we leveraged a family-based design (31 families) to investigate the influence of shared genetic and environmental components on the autism-associated features. Using untargeted high-resolution mass spectrometry metabolomics platforms, we detected 19 184 features. Of these, 191 were associated with ASD (false discovery rate < 0.05). We putatively annotated 30 features that had an odds ratio (OR) between <0.01 and 5.84. An identified endogenous metabolite, O-phosphotyrosine, was associated with an extremely low autism odds (OR 0.17; 95% confidence interval 0.06-0.39). We also found that glutathione metabolism was associated with ASD (P = 0.048). Correlations of the significant features between proband and parents were low (median = 0.09). Of the 30 annotated features, the median correlations within families (proband-parents) were -0.15 and 0.24 for the endogenous and exogenous metabolites, respectively. We hypothesize that, without feature identification, family-based correlation analysis of autism-associated features can be an alternative way to assist the prioritization of potentially diagnostic features. A panel of ASD diagnostic metabolic markers with high specificity could be derived upon further studies.

The potential for mitigation of methane emissions in ruminants through the application of metagenomics, metabolomics, and other -OMICS technologies

Ruminant supply chains contribute 5.7 gigatons of CO_2-eq per annum, which represents approximately 80% of the livestock sector emissions. One of the largest sources of emission in the ruminant sector is methane (CH₄), accounting for approximately 40% of the sectors total emissions. With climate change being a growing concern, emphasis is being put on reducing greenhouse gas emissions, including those from ruminant production. Various genetic and environmental factors influence cattle CH₄ production, such as breed, genetic makeup, diet, management practices, and physiological status of the host. The influence of genetic variability on CH₄ yield in ruminants indicates that genomic selection for reduced CH₄ emissions is possible. Although the microbiology of CH₄ production has been studied, further research is needed to identify key differences in the host and microbiome genomes and how they interact with one another. The advancement of “-omics” technologies, such as metabolomics and metagenomics, may provide valuable information in this regard. Improved understanding of genetic mechanisms associated with CH₄ production and the interaction between the microbiome profile and host genetics will increase the rate of genetic progress for reduced CH₄ emissions. Through a systems biology approach, various “-omics” technologies can be combined to unravel genomic regions and genetic markers associated with CH₄ production, which can then be used in selective breeding programs. This comprehensive review discusses current challenges in applying genomic selection for reduced CH₄ emissions, and the potential for “-omics” technologies, especially metabolomics and metagenomics, to minimize such challenges. The integration and evaluation of different levels of biological information using a systems biology approach is also discussed, which can assist in understanding the underlying genetic mechanisms and biology of CH₄ production traits in ruminants and aid in reducing agriculture’s overall environmental footprint.