Metabolomics in Systems Biology

Integrated metabolomics and gut microbiota analysis to explore the protective effects of Gushudan on postmenopausal osteoporosis rats via gut-bone axis

Postmenopausal osteoporosis (PMOP) was caused by significant deviations in gut microbiota and metabolites. Gushudan (GSD), a small traditional Chinese medicine formula, exerted therapeutic effects including kidney-nourishing and bone-strengthening properties. The therapeutic mechanism of GSD in alleviating kidney-yang deficiency syndrome and secondary osteoporosis was systematically investigated through metabolomics and network pharmacology. However, the mechanisms and impact on gut microbiota through which GSD mitigated PMOP remained to be elucidated. In this study, fecal metabolomics was integrated with gut microbiota analysis to comprehensively investigate modification in intestinal flora and metabolic profiles in PMOP rat models from the gut-bone axis framework. Therefore, the GC-MS-based method integrating non-targeted and targeted metabolomics was established to analyze fecal metabolites. The comprehensive analysis of gut microbial communities was performed using 16S rRNA on fecal samples. In the result, 20 potential biomarkers were successfully identified in the non-targeted metabolomics analysis. Subsequently, 12 metabolites related to amino acid metabolism, energy metabolism and bile acid biosynthesis were quantitatively. Then, ten gut microorganisms with significant changes were discovered through 16S rRNA. Furthermore, alterations in fecal metabolites demonstrated a significant correlation with dysbiosis within the gut microorganisms such as [Ruminococcus]_torques_group, Elusimicrobium, Intestinimonas and Papillibacter. GSD effectively modulated abnormal levels of metabolites such as glycine, lactic acid, succinic acid, cholesterol and deoxycholic acid. Specifically, GSD improved the abundance of [Ruminococcus]_torques_group, Elusimicrobium, Intestinimonas. In conclusion, the gut-bone axis was validated as a novel framework, and gut microbiota modulation was further identified as a promising therapeutic target for the prevention of PMOP.

Large-Scale Rice Mutant Establishment and High-Throughput Mutant Manipulation Help Advance Rice Functional Genomics

Rice (Oryza sativa L.) is a stable food for over half of the world population, contributing 50-80% of the daily calorie intake. The completion of rice genome sequencing marks a significant milestone in understanding functional genomics, yet the systematic identification of gene functions remains a bottleneck for rice improvement. Large-scale mutant libraries in which the functions of genes are lost or gained (e.g., through chemical/physical treatments, T-DNA, transposons, RNAi, CRISPR/Cas9) have proven to be powerful tools for the systematic linking of genotypes to phenotypes. So far, using different mutagenesis approaches, a million mutant lines have been established and about 5-10% of the predicted rice gene functions have been identified due to the high demands of labor and low-throughput utilization. DNA-barcoding-based large-scale mutagenesis offers unprecedented precision and scalability in functional genomics. This review summarizes large-scale loss-of-function and gain-of-function mutant library development approaches and emphasizes the integration of DNA barcoding for pooled analysis. Unique DNA barcodes can be tagged to transposons/retrotransposons, DNA constructs, miRNA/siRNA, gRNA, and cDNA, allowing for pooling analysis and the assignment of functions to genes that cause phenotype alterations. In addition, the integration of high-throughput phenotyping and OMICS technologies can accelerate the identification of gene functions.

Integrating metagenomics and metabolomics to study the gut microbiome and host relationships in sports across different energy systems

The gut microbiome plays a critical role in modulating host metabolism, influencing energy production, nutrient utilization, and overall physiological adaptation. In athletes, these microbial functions may be further specialized to meet the unique metabolic demands of different sports disciplines. This study explored the role of the gut microbiome in modulating host metabolism among Colombian athletes by comparing elite weightlifters (n = 16) and cyclists (n = 13) through integrative omics analysis. Fecal and plasma samples collected one month before an international event underwent metagenomic, metabolomic, and lipidomic profiling. Metagenomic analysis revealed significant microbial pathways, including L-arginine biosynthesis III and fatty acid biosynthesis initiation. Key metabolic pathways, such as phenylalanine, tyrosine, and tryptophan biosynthesis; arginine biosynthesis; and folate biosynthesis, were enriched in both athlete groups. Plasma metabolomics and lipidomics revealed distinct metabolic profiles and a separation between athlete types through multivariate models, with lipid-related pathways such as lipid droplet formation and glycolipid synthesis driving the differences. Notably, elevated carnitine, amino acid, and glycerolipid levels in weightlifters suggest energy system-specific metabolic adaptations. These findings underscore the complex relationship between the gut microbiota composition and metabolic responses tailored to athletic demands, laying the groundwork for personalized strategies to optimize performance. This research highlights the potential for targeted modulation of the gut microbiota as a basis for tailored interventions to support specific energy demands in athletic disciplines.

Advances in mass spectrometry for metabolomics: Strategies, challenges, and innovations in disease biomarker discovery

Mass spectrometry (MS) plays a crucial role in metabolomics, especially in the discovery of disease biomarkers. This review outlines strategies for identifying metabolites, emphasizing precise and detailed use of MS techniques. It explores various methods for quantification, discusses challenges encountered, and examines recent breakthroughs in biomarker discovery. In the field of diagnostics, MS has revolutionized approaches by enabling a deeper understanding of tissue-specific metabolic changes associated with disease. The reliability of results is ensured through robust experimental design and stringent system suitability criteria. In the past, data quality, standardization, and reproducibility were often overlooked despite their significant impact on MS-based metabolomics. Progress in this field heavily depends on continuous training and education. The review also highlights the emergence of innovative MS technologies and methodologies. MS has the potential to transform our understanding of metabolic landscapes, which is crucial for disease biomarker discovery. This article serves as an invaluable resource for researchers in metabolomics, presenting fresh perspectives and advancements that propels the field forward.

Plasma-based proteomic and metabolomic characterization of lung and lymph node metastases in cervical cancer patients

Metastasis is the leading cause of mortality in cervical cancer (CC), with a particular prevalence of lymph node and lung metastases. Patients with CC who have developed distant metastases typically face a poor prognosis, and there is a scarcity of non-invasive strategies for predicting CC metastasis. In this study, we utilized label-free proteomics and untargeted metabolomics to analyze plasma samples from 25 non-metastatic, 14 with lung metastasis, and 15 with lymph node metastasis CC patients. Pathway enrichment analysis revealed a shared inflammatory process between the two metastatic groups, while the central carbon metabolism in cancer showed distinct features in the lung metastasis cohort. Additionally, cholesterol metabolism, hypoxia-inducible factor 1, and ferroptosis signaling pathways were specifically altered in the lymph node metastasis group. Utilizing the receiver operating characteristic curve analysis and Random Forest algorithm, we identified two distinct biomarker panels for the prediction of lung metastasis and lymph node metastasis, respectively. The lung metastasis panel includes properdin, neural cell adhesion molecule 1, and keratin 6 A, whereas the lymph node metastasis panel consists of quiescin sulfhydryl oxidase 1, paraoxonase 1, and keratin 6 A. Each panel exhibited significant diagnostic potential, with high area under the curve (AUC) values for lung metastasis (training set: 0.989, testing set: 0.789) and lymph node metastasis (training set: 0.973, testing set: 0.900). This study conducted an integrated proteomic and metabolomic analysis to clarify the factors contributing to lung and lymph node metastases in CC and has successfully established two biomarker panels for their prediction.

Omics approaches to unravel insecticide resistance mechanism in Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae)

Bemisia tabaci (Gennadius) whitefly (BtWf) is an invasive pest that has already spread worldwide and caused major crop losses. Numerous strategies have been implemented to control their infestation, including the use of insecticides. However, prolonged insecticide exposures have evolved BtWf to resist these chemicals. Such resistance mechanism is known to be regulated at the molecular level and systems biology omics approaches could shed some light on understanding this regulation wholistically. In this review, we discuss the use of various omics techniques (genomics, transcriptomics, proteomics, and metabolomics) to unravel the mechanism of insecticide resistance in BtWf. We summarize key genes, enzymes, and metabolic regulation that are associated with the resistance mechanism and review their impact on BtWf resistance. Evidently, key enzymes involved in the detoxification system such as cytochrome P450 (CYP), glutathione S-transferases (GST), carboxylesterases (COE), UDP-glucuronosyltransferases (UGT), and ATP binding cassette transporters (ABC) family played key roles in the resistance. These genes/proteins can then serve as the foundation for other targeted techniques, such as gene silencing techniques using RNA interference and CRISPR. In the future, such techniques will be useful to knock down detoxifying genes and crucial neutralizing enzymes involved in the resistance mechanism, which could lead to solutions for coping against BtWf infestation.

Quantified Metabolomics and Lipidomics Profiles Reveal Serum Metabolic Alterations and Distinguished Metabolites of Seven Chronic Metabolic Diseases

The co-occurrence of multiple chronic metabolic diseases is highly prevalent, posing a huge health threat. Clarifying the metabolic associations between them, as well as identifying metabolites which allow discrimination between diseases, will provide new biological insights into their co-occurrence. Herein, we utilized targeted serum metabolomics and lipidomics covering over 700 metabolites to characterize metabolic alterations and associations related to seven chronic metabolic diseases (obesity, hypertension, hyperuricemia, hyperglycemia, hypercholesterolemia, hypertriglyceridemia, fatty liver) from 1626 participants. We identified 454 metabolites were shared among at least two chronic metabolic diseases, accounting for 73.3% of all 619 significant metabolite-disease associations. We found amino acids, lactic acid, 2-hydroxybutyric acid, triacylglycerols (TGs), and diacylglycerols (DGs) showed connectivity across multiple chronic metabolic diseases. Many carnitines were specifically associated with hyperuricemia. The hypercholesterolemia group showed obvious lipid metabolism disorder. Using logistic regression models, we further identified distinguished metabolites of seven chronic metabolic diseases, which exhibited satisfactory area under curve (AUC) values ranging from 0.848 to 1 in discovery and validation sets. Overall, quantitative metabolome and lipidome data sets revealed widespread and interconnected metabolic disorders among seven chronic metabolic diseases. The distinguished metabolites are useful for diagnosing chronic metabolic diseases and provide a reference value for further clinical intervention and management based on metabolomics strategy.

Blood metabolomic and postpartum depression: a mendelian randomization study

BACKGROUND

Postpartum depression is a complex mental health condition that often occurs after childbirth and is characterized by persistent sadness, anxiety, and fatigue. Recent research suggests a metabolic component to the disorder. This study aims to investigate the causal relationship between blood metabolites and postpartum depression using mendelian randomization (MR).

METHODS

This study used a bi-directional MR framework to investigate the causal relationship between 1,400 metabolic biomarkers and postpartum depression. We used two specific genome-wide association studies datasets: one with single nucleotide polymorphisms data from mothers diagnosed with postpartum depression and another with blood metabolite data, both of which focused on people of European ancestry. Genetic variants were chosen as instrumental variables from both datasets using strict criteria to improve the robustness of the MR analysis. The combination of these datasets enabled a thorough examination of genetic influences on metabolic profiles associated with postpartum depression. Statistical analyses were conducted using techniques such as inverse variance weighting, weighted median, and model-based estimation, which enabled rigorous causal inference from the observed associations. postpartum depression was defined using endpoint definitions approved by the FinnGen study's clinical expert groups, which included leading experts in their respective medical fields.

RESULTS

The MR analysis identified seven metabolites that could be linked to postpartum depression. Out of these, one metabolite was found to be protective, while six were associated with an increased risk of developing the condition. The results were consistent across multiple MR methods, indicating a significant correlation.

CONCLUSIONS

This study emphasizes the potential of metabolomics for understanding postpartum depression. The discovery of specific metabolites associated with the condition sheds new insights on its pathophysiology and opens up possibilities for future research into targeted treatment strategies.

Metabolomic profiling of the aqueous humor in patients with pediatric cataract

Pediatric cataract, including congenital and developmental cataract, is a kind of pediatric vision-threatening disease with extensive phenotypic heterogeneity and multiple mechanisms. We aimed to investigate the metabolite profile of aqueous humor (AH) in patients with pediatric cataracts, and identify underlying mutual correlations between differential metabolites. Metabolomic profiles of AH were analyzed and compared between pediatric cataract patients (n = 33) and age-related cataract patients without metabolic diseases (n = 29), using global untargeted metabolomics with ultra-high-performance liquid chromatography tandem mass spectrometry. Principal component analysis, partial least squares discriminant analysis and heat map were applied. Enriched pathway analysis was conducted using Kyoto Encyclopedia of Genes and Genomes. Receiver-operating characteristic (ROC) analyses were employed to select potential biomarkers. A total of 318 metabolites were identified, of which 54 differential metabolites (25 upregulated and 29 downregulated) were detected in pediatric cataract group compared with controls (variable importance of projection >1.0, fold change ≥1.5 or ≤ 0.667 and P < 0.05). A significant accumulation of N-Acetyl-Dl-glutamic acid was observed in pediatric cataract group. The differential metabolites were mainly enriched in histidine metabolism (increased L-Histidine and decreased 1-Methylhistamine) and the tryptophan metabolism (increased N-Formylkynurenine and L-Kynurenine). 5-Aminosalicylic acid showed strong positive mutual inter-correlation with L-Tyrosinemethylester and N,N-Diethylethanolamine, both of which were down-regulated in pediatric cataract group. The ROC analysis implied 11 metabolites served as potential biomarkers for pediatric cataract patients (all area under the ROC curve ≥0.900). These results illustrated novel potential metabolites and metabolic pathways in pediatric cataract, which provides new insights into the pathophysiology of pediatric cataract.

The role of phenomics and genomics in delineating the genetic basis of complex traits in millets

Millets, comprising a diverse group of small-seeded grains, have emerged as vital crops with immense nutritional, environmental, and economic significance. The comprehension of complex traits in millets, influenced by multifaceted genetic determinants, presents a compelling challenge and opportunity in agricultural research. This review delves into the transformative roles of phenomics and genomics in deciphering these intricate genetic architectures. On the phenomics front, high-throughput platforms generate rich datasets on plant morphology, physiology, and performance in diverse environments. This data, coupled with field trials and controlled conditions, helps to interpret how the environment interacts with genetics. Genomics provides the underlying blueprint for these complex traits. Genome sequencing and genotyping technologies have illuminated the millet genome landscape, revealing diverse gene pools and evolutionary relationships. Additionally, different omics approaches unveil the intricate information of gene expression, protein function, and metabolite accumulation driving phenotypic expression. This multi-omics approach is crucial for identifying candidate genes and unfolding the intricate pathways governing complex traits. The review highlights the synergy between phenomics and genomics. Genomically informed phenotyping targets specific traits, reducing the breeding size and cost. Conversely, phenomics identifies promising germplasm for genomic analysis, prioritizing variants with superior performance. This dynamic interplay accelerates breeding programs and facilitates the development of climate-smart, nutrient-rich millet varieties and hybrids. In conclusion, this review emphasizes the crucial roles of phenomics and genomics in unlocking the genetic enigma of millets.

Pan-Omics in Sheep: Unveiling Genetic Landscapes

Multi-omics-integrated analysis, known as panomics, represents an advanced methodology that harnesses various high-throughput technologies encompassing genomics, epigenomics, transcriptomics, proteomics, and metabolomics. Sheep, playing a pivotal role in agricultural sectors due to their substantial economic importance, have witnessed remarkable advancements in genetic breeding through the amalgamation of multiomics analyses, particularly with the evolution of high-throughput technologies. This integrative approach has established a robust theoretical foundation, enabling a deeper understanding of sheep genetics and fostering improvements in breeding strategies. The comprehensive insights obtained through this approach shed light on diverse facets of sheep development, including growth, reproduction, disease resistance, and the quality of livestock products. This review primarily focuses on the application of principal omics analysis technologies in sheep, emphasizing correlation studies between multiomics data and specific traits such as meat quality, wool characteristics, and reproductive features. Additionally, this paper anticipates forthcoming trends and potential developments in this field.

Multi-Omics Approaches to Improve Meat Quality and Taste Characteristics

With rapid advances in meat science in recent decades, changes in meat quality during the pre-slaughter phase of muscle growth and the post-slaughter process from muscle to meat have been investigated. Commonly used techniques have evolved from early physicochemical indicators such as meat color, tenderness, water holding capacity, flavor, and pH to various omic tools such as genomics, transcriptomics, proteomics, and metabolomics to explore fundamental molecular mechanisms and screen biomarkers related to meat quality and taste characteristics. This review highlights the application of omics and integrated multi-omics in meat quality and taste characteristics studies. It also discusses challenges and future perspectives of multi-omics technology to improve meat quality and taste. Consequently, multi-omics techniques can elucidate the molecular mechanisms responsible for changes of meat quality at transcriptome, proteome, and metabolome levels. In addition, the application of multi-omics technology has great potential for exploring and identifying biomarkers for meat quality and quality control that can make it easier to optimize production processes in the meat industry.

Exploring the roles of microorganisms and metabolites in the 30-year aging process of the dried pericarps of Citrus reticulata 'Chachi' based on high-throughput sequencing and comparative metabolomics

GuangChenpi (GCP), the dried pericarps of Citrus reticulata 'Chachi', has been consumed daily as a food and dietary supplement in China for centuries. Its health benefits are generally recognized to be dependent on storage time. However, the specific roles of microorganisms and metabolites during long-term storage are still unclear. In this study, comparative metabolomics and high-throughput sequencing techniques were used to investigate the effects of co-existing microorganisms on the metabolites in GCP stored from 1 to 30 years. In total, 386 metabolites were identified and characterized. Most compounds were flavonoids (37%), followed by phenolic acids (20%). Seventeen differentially upregulated metabolites were identified as potential key metabolites in GCP, and 8 of them were screened out as key active ingredients by Venn diagram comparative analyses and verified by network pharmacology and molecular docking. In addition, long-term storage could promote the accumulation of secondary metabolites. Regarding the GCP microbiota, Xeromyces dominated the whole 30-year aging process.Moreover, Spearman correlation analysis indicated that Bacillus thuringiensis and Xeromyces bisporus, the dominant bacterial and fungal species, were strongly associated with the key active metabolites. Our results suggested that the change of active ingredients caused by the dominant microbial is one of the mechanisms affecting the GCP aging process. Our study provides novel functional insights and research perspectives on microorganism-associated metabolite changes that may improve the GCP aging process.

Breeding and Genomic Approaches towards Development of Fusarium Wilt Resistance in Chickpea

Chickpea is an important leguminous crop with potential to provide dietary proteins to both humans and animals. It also ameliorates soil nitrogen through biological nitrogen fixation. The crop is affected by an array of biotic and abiotic factors. Among different biotic stresses, a major fungal disease called Fusarium wilt, caused by Fusarium oxysporum f. sp. ciceris (FOC), is responsible for low productivity in chickpea. To date, eight pathogenic races of FOC (race 0, 1A, and 1B/C, 2-6) have been reported worldwide. The development of resistant cultivars using different conventional breeding methods is very time consuming and depends upon the environment. Modern technologies can improve conventional methods to solve these major constraints. Understanding the molecular response of chickpea to Fusarium wilt can help to provide effective management strategies. The identification of molecular markers closely linked to genes/QTLs has provided great potential for chickpea improvement programs. Moreover, omics approaches, including transcriptomics, metabolomics, and proteomics give scientists a vast viewpoint of functional genomics. In this review, we will discuss the integration of all available strategies and provide comprehensive knowledge about chickpea plant defense against Fusarium wilt.

The evolving role of investigative toxicology in the pharmaceutical industry

For decades, preclinical toxicology was essentially a descriptive discipline in which treatment-related effects were carefully reported and used as a basis to calculate safety margins for drug candidates. In recent years, however, technological advances have increasingly enabled researchers to gain insights into toxicity mechanisms, supporting greater understanding of species relevance and translatability to humans, prediction of safety events, mitigation of side effects and development of safety biomarkers. Consequently, investigative (or mechanistic) toxicology has been gaining momentum and is now a key capability in the pharmaceutical industry. Here, we provide an overview of the current status of the field using case studies and discuss the potential impact of ongoing technological developments, based on a survey of investigative toxicologists from 14 European-based medium-sized to large pharmaceutical companies.

Metabolomic-Based Studies of the Intake of Virgin Olive Oil: A Comprehensive Review

Virgin olive oil (VOO) is a high-value product from the Mediterranean diet. Some health and nutritional benefits have been associated with its consumption, not only because of its monounsaturated-rich triacylglycerols but also due to its minor bioactive components. The search for specific metabolites related to VOO consumption may provide valuable information to identify the specific bioactive components and to understand possible molecular and metabolic mechanisms implicated in those health effects. In this regard, metabolomics, considered a key analytical tool in nutritional studies, offers a better understanding of the regulatory functions of food components on human nutrition, well-being, and health. For that reason, the aim of the present review is to summarize the available scientific evidence related to the metabolic effects of VOO or its minor bioactive compounds in human, animal, and in vitro studies using metabolomics approaches.

Metabolomics identifies shared lipid pathways in independent amyotrophic lateral sclerosis cohorts

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease lacking effective treatments. This is due, in part, to a complex and incompletely understood pathophysiology. To shed light, we conducted untargeted metabolomics on plasma from two independent cross-sectional ALS cohorts versus control participants to identify recurrent dysregulated metabolic pathways. Untargeted metabolomics was performed on plasma from two ALS cohorts (cohort 1, n = 125; cohort 2, n = 225) and healthy controls (cohort 1, n = 71; cohort 2, n = 104). Individual differential metabolites in ALS cases versus controls were assessed by Wilcoxon, adjusted logistic regression and partial least squares-discriminant analysis, while group lasso explored sub-pathway level differences. Adjustment parameters included age, sex and body mass index. Metabolomics pathway enrichment analysis was performed on metabolites selected using the above methods. Additionally, we conducted a sex sensitivity analysis due to sex imbalance in the cohort 2 control arm. Finally, a data-driven approach, differential network enrichment analysis (DNEA), was performed on a combined dataset to further identify important ALS metabolic pathways. Cohort 2 ALS participants were slightly older than the controls (64.0 versus 62.0 years, P = 0.009). Cohort 2 controls were over-represented in females (68%, P < 0.001). The most concordant cohort 1 and 2 pathways centred heavily on lipid sub-pathways, including complex and signalling lipid species and metabolic intermediates. There were differences in sub-pathways that were enriched in ALS females versus males, including in lipid sub-pathways. Finally, DNEA of the merged metabolite dataset of both ALS and control cohorts identified nine significant subnetworks; three centred on lipids and two encompassed a range of sub-pathways. In our analysis, we saw consistent and important shared metabolic sub-pathways in both ALS cohorts, particularly in lipids, further supporting their importance as ALS pathomechanisms and therapeutics targets.

Transcriptome and metabolome profiling of interspecific CSSLs reveals general and specific mechanisms of drought resistance in cotton

In order to understand the molecular mechanism of cotton's response to drought during the flowering and boll stage, transcriptomics and metabolomics were carried out for two introgression lines (drought-tolerant line: T307; drought-sensitive line: S48) which were screened from Gossypium hirsutum cv. 'Emian22' with some gene fragments imported from Gossypium barbadense acc. 3-79, under drought stress by withdrawing water at flowering and boll stage. Results showed that the basic drought response in cotton included a series of broad-spectrum responses, such as amino acid synthesis, hormone (abscisic acid, ABA) signal transduction, and mitogen-activated protein kinases signal transduction pathway, which activated in both drought-tolerant and drought-sensitive lines. However, the difference of their imported fragments and diminished sequences triggers endoplasmic reticulum (ER) protein processing, photosynthetic-related pathways (in leaves), and membrane solute transport (in roots) in drought-tolerant line T307, while these are missed or not activated in drought-sensitive line S48, reflecting the different drought tolerance of the two genotypes. Virus-induced gene silencing assay of drought-tolerant differentially expressed heat shock protein (HSP) genes (mainly in leaf) and ATP-binding cassette (ABC) transporter genes (mainly in roots) indicated that those genes play important role in cotton drought tolerant. Combined analysis of transcriptomics and metabolomics highlighted the important roles of ER-stress-related HSP genes and root-specific ABC transporter genes in plants drought tolerance. These results provide new insights into the molecular mechanisms underlying the drought stress adaptation in cotton.

ICP-MS based metallomics and GC-MS based metabolomics reveals the physiological and metabolic responses of Dendrobium huoshanense plants exposed to Fe3O4 nanoparticles

It is found that the growth of Dendrobium huoshanense was dependent on Fe3O4, while the bioavailability of plants to ordinary Fe3O4 was low on the earth. In order to improve the growth, quality and yield of D. huoshanense, we used Fe3O4 NPs (100 or 200 mg/L) that was easily absorbed by plants as nano-fertilizer to hydroponically treat seedlings of D. huoshanense for 3 weeks. Fe3O4 NPs induced not only earlier flowering and increased sugar content and photosynthesis, but also stressed to plants, increased MDA content and related antioxidant enzymes activities. Inductively Coupled Plasma Mass Spectrometry (ICP-MS) revealed that Fe3O4 NPs caused a significant accumulation of Fe and some other nutrient elements (Mn, Co, B, Mo) in stems of D. huoshanense. Metabolomics revealed that the metabolites were reprogrammed in D. huoshanense when under Fe3O4 NPs exposure. Fe3O4 NPs inhibited antioxidant defense-related pathways, demonstrating that Fe3O4 NPs have antioxidant capacity to protect D. huoshanense from damage. As the first study associating Fe3O4 NPs with the quality of D. huoshanense, it provided vital insights into the molecular mechanisms of how D. huoshanense responds to Fe3O4 NPs, ensuring the reasonable use of Fe3O4 NPs as nano-fertilizer.

Comprehensive plasma metabolomics and lipidomics of benign and malignant solitary pulmonary nodules

INTRODUCTION

Solitary pulmonary nodules (SPNs) are commonly found in imaging technologies, but are plagued by high false-positive rates.

OBJECTIVE

We aimed to identify metabolic alterations in SPN etiology and diagnosis using less invasive plasma metabolomics and lipidomics.

METHODS

In total, 1160 plasma samples were obtained from healthy volunteers (n = 280), benign SPNs (n = 157) and malignant SPNs (stage I, n = 723) patients enrolled from 5 independent centers. Gas chromatography-triple quadrupole mass spectrometry (GC‒MS) and liquid chromatography-Q Exactive Hybrid Quadrupole-Orbitrap mass spectrometry (LC‒MS) were used to analyze the samples for untargeted metabolomics and lipidomics.

RESULTS AND CONCLUSION

GC‒MS-based metabolomics revealed 1336 metabolic features, while LC‒MS-based lipidomics revealed 6088 and 2542 lipid features in the positive and negative ion modes, respectively. The metabolic and lipidic characteristics of healthy vs. benign or malignant SPNs exhibited substantial pattern differences. Of note, benign and malignant SPNs had no significant variations in circulating metabolic and lipidic markers and were validated in four other centers. This study demonstrates evidence of early metabolic alterations that can possibly distinguish SPNs from healthy controls, but not between benign and malignant SPNs.

Serum Untargeted Metabolomics Reveal Potential Biomarkers of Progression of Diabetic Retinopathy in Asians

Purpose: To reveal molecular mechanisms of diabetic retinopathy (DR) in Asians and facilitate the identification of new therapeutic targets through untargeted metabolomics. To determine the differences in serum metabolites and metabolic pathways between different stages of diabetic retinopathy in patients with type 2 diabetic mellitus (T2DM) and proliferative DR (PDR) and non-proliferative DR (NPDR) and identify differential metabolites between T2DM and DR (NPDR and PDR) patients. Methods: This prospective observational registration study described the differential metabolites between 45 T2DM patients and 15 control cases with no significant differences in clinical characteristics. Their biospecimens and clinical information were collected and recorded in their medical reports. DR phenotypes of the subjects were verified by retina specialists. Serum metabolites were analyzed using high-resolution mass spectrometry with liquid chromatography. Untargeted metabolomics was performed on serum samples from 15 T2DM patients, 15 non-proliferative diabetic retinopathy patients, 15 proliferative diabetic retinopathy patients, and 15 diabetic controls. Discriminatory metabolic features were identified through partial least squares discriminant analysis (PLS-DA), hierarchical clustering analysis (HCA), and generalized linear regression models. Result: Through untargeted metabolomics, 931 features (523 in positive and 408 in negative modes) with 102 common metabolites highly relevant to the presence of DR were detected. In the adjusted analysis, 67 metabolic features differed significantly between T2DM and NPDR patients. Pathway analysis revealed alterations in metabolisms of amino acids and fatty acids. Glutamate, phosphatidylcholine, and 13-hydroperoxyoctadeca-9,11-dienoic acid (13-PHODE) were key contributors to these pathway differences. A total of 171 features distinguished PDR patients from T2DM patients, and pathway analysis revealed alterations in amino acid metabolism, fatty acid metabolism, nitrogen metabolism, and tricarboxylic acid cycle. Aspartate, glutamate, glutamine, ornithine, N-acetyl-l-glutamate, N-acetyl-l-aspartate, citrate, succinate, N-(L-arginino)succinate, 2-oxoglutarate, 13-hydroperoxyoctadeca-9,11-dienoic acid, methionine, lysine, threonine, phenylalanine, N(pi)-methyl-l-histidine, phosphatidylcholine, and linoleate were major contributors to the pathway differences. Between NPDR patients and PDR patients, there were 79 significant differential metabolites. Enrichment pathway analysis showed changes in amino acid metabolism, fatty acid metabolism, pantothenate, and CoA biosynthesis. Aspartate, glutamine, N-acetyl-l-glutamate, N-acetyl-l-aspartate, pantothenate, dihomo-gamma-linolenate, docosahexaenoic acid, and icosapentaenoic acid were key factors for the differences of these pathways. Conclusion: This study demonstrated that the pathways of arginine biosynthesis metabolism, linoleic acid metabolism, alanine, aspartate, and glutamate metabolism, as well as d-glutamine and d-glutamate metabolism, were dysregulated in DR patients of the Asian population. Increased levels of glutamate, aspartate, glutamine, N-acetyl-l-glutamate, and N-acetyl-l-aspartate and decreased levels of dihomo-gamma-linolenate, docosahexaenoic, and icosapentaenoic were considered as the metabolic profile that could distinguish PDR from NPDR in Asians. Phosphatidylcholine and 13-PHODE were identified as two major novel metabolite markers in advanced stages of DR in our study.

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.

Blood Metabolomics Analysis Identifies Differential Serum Metabolites in Elite and Sub-elite Swimmers

Objective: Metabolites in body fluids, such as lactate, glucose, and creatinine, have been measured by conventional methods to evaluate physical function and performance or athletic status. The objectives of the current study were to explore the novel metabolite biomarkers in professional swimmers with different competition levels using nuclear magnetic resonance (NMR) metabolomics, and try to establish a model to identify the athletic status or predict the competitive potential.

Methods: Serum samples were collected from 103 elite and 84 sub-elite level Chinese professional swimmers, and were profiled by NMR analysis.

Results: Out of the thirty-six serum metabolites profiled, ten were associated with the athletic status of swimmers (with p < 0.05). When compared with sub-elite swimmers, elite swimmers had higher levels of high-density lipoprotein (HDL), unsaturated fatty acid, lactic acid, and methanol. Elite swimmers had lower levels of isoleucine, 3-hydroxybutyric acid, acetoacetate, glutamine, glycine, and α-glucose. A model with four metabolites, including HDL, glutamine, methanol, and α-glucose, was established to predict athletic status by adjusting with different covariates. The area under the curve (AUC) of the best model was 0.904 (95% CI: 0.862-0.947), with a sensitivity and specificity of 75.5 and 90.2%, respectively.

Conclusion: We have identified ten metabolite biomarkers with differentially expressed levels between elite and sub-elite swimmers, the differences could result from genetic or sports level between the two cohorts. A model with four metabolites has successfully differentiated professional swimmers with different competitive levels.

Rhizosphere Tripartite Interactions and PGPR-Mediated Metabolic Reprogramming towards ISR and Plant Priming: A Metabolomics Review

Plant growth-promoting rhizobacteria (PGPR) are beneficial microorganisms colonising the rhizosphere. PGPR are involved in plant growth promotion and plant priming against biotic and abiotic stresses. Plant-microbe interactions occur through chemical communications in the rhizosphere and a tripartite interaction mechanism between plants, pathogenic microbes and plant-beneficial microbes has been defined. However, comprehensive information on the rhizosphere communications between plants and microbes, the tripartite interactions and the biochemical implications of these interactions on the plant metabolome is minimal and not yet widely available nor well understood. Furthermore, the mechanistic nature of PGPR effects on induced systemic resistance (ISR) and priming in plants at the molecular and metabolic levels is yet to be fully elucidated. As such, research investigating chemical communication in the rhizosphere is currently underway. Over the past decades, metabolomics approaches have been extensively used in describing the detailed metabolome of organisms and have allowed the understanding of metabolic reprogramming in plants due to tripartite interactions. Here, we review communication systems between plants and microorganisms in the rhizosphere that lead to plant growth stimulation and priming/induced resistance and the applications of metabolomics in understanding these complex tripartite interactions.

A comprehensive analysis of age-related metabolomics and transcriptomics reveals metabolic alterations in rat bone marrow mesenchymal stem cells

The functions of stem cells decline progressively with aging, and some metabolic changes occur during the process. However, the molecular mechanisms of stem cell aging remain unclear. In this study, the combined application of metabolomics and transcriptomics technologies can effectively describe the possible molecular mechanisms of rat bone marrow mesenchymal stem cell (BMSC) senescence. Metabolomic profiles revealed 23 differential metabolites which were abundant in “glycerophospholipid metabolism”, “linoleic acid metabolism” and “biosynthesis of unsaturated fatty acids”. In addition, transcriptomics analysis identified 590 genes with enormously differential expressions in young and old BMSCs. KEGG enrichment analyses showed that metabolism-related pathways in BMSC senescence had stronger responses. Furthermore, the integrated analysis of the interactions between the differentially expressed genes (DEGs) and metabolites indicated the differential genes related to lipid metabolism of Scd, Scd2, Dgat2, Fads2, Lpin1, Gpat3, Acaa2, Lpcat3, Pcyt2 and Pla2g4a may be closely associated with the aging of BMSCs. Finally, Scd2 was identified as the most significant DEG, and Scd2 over-expression could alleviate cellular senescence in aged BMSCs. In conclusion, this work provides a validated understanding that the DEGs and metabolites related to lipid metabolism present more apparent changes in the senescence of rat BMSCs.

Age-Related Neurometabolomic Signature of Mouse Brain

Neurometabolites are the ultimate gene products in the brain and the most precise biomolecular indicators of brain endophenotypes. Metabolomics is the only "omics" that provides a moment-to-moment "snapshot" of brain circuits' biochemical activities in response to external stimuli within the context of specific genetic variations. Although the expression levels of neurometabolites are highly dynamic, the underlying metabolic processes are tightly regulated during brain development, maturation, and aging. Therefore, this study aimed to identify mouse brain metabolic profiles in neonatal and adult stages and reconstruct both the active metabolic network and the metabolic pathway functioning. Using high-throughput metabolomics and bioinformatics analyses, we show that the neonatal mouse brain has its distinct metabolomic signature, which differs from the adult brain. Furthermore, lipid metabolites showed the most profound changes between the neonatal and adult brain, with some lipid species reaching 1000-fold changes. There were trends of age-dependent increases and decreases among lipids and non-lipid metabolites, respectively. A few lipid metabolites such as HexCers and SHexCers were almost absent in neonatal brains, whereas other non-lipid metabolites such as homoarginine were absent in the adult brains. Several molecules that act as neurotransmitters/neuromodulators showed age-dependent levels, with adenosine and GABA exhibiting around 100- and 10-fold increases in the adult compared with the neonatal brain. Of particular interest is the observation that purine and pyrimidines nucleobases exhibited opposite age-dependent changes. Bioinformatics analysis revealed an enrichment of lipid biosynthesis pathways in metabolites, whose levels increased in adult brains. In contrast, pathways involved in the metabolism of amino acids, nucleobases, glucose (glycolysis), tricarboxylic acid cycle (TCA) were enriched in metabolites whose levels were higher in the neonatal brains. Many of these pathways are associated with pathological conditions, which can be predicted as early as the neonatal stage. Our study provides an initial age-related biochemical directory of the mouse brain and warrants further studies to identify temporal brain metabolome across the lifespan, particularly during adolescence and aging. Such neurometabolomic data may provide important insight about the onset and progression of neurological/psychiatric disorders and may ultimately lead to the development of precise diagnostic biomarkers and more effective preventive/therapeutic strategies.

Untargeted metabolomics approach to discriminate mistletoe commercial products

Mistletoe (Viscum album L.) is used in German-speaking European countries in the field of integrative oncology linking conventional and complementary medicine therapies to improve quality of life. Various companies sell extracts, fermented or not, for injection by subcutaneous or intra-tumoral route with a regulatory status of anthroposophic medicinal products (European Medicinal Agency (EMA) assessment status). These companies as well as anthroposophical physicians argue that complex matrices composed of many molecules in mixture are necessary for activity and that the host tree of the mistletoe parasitic plant is the main determining factor for this matrix composition. The critical point is that parenteral devices of European mistletoe extracts do not have a standard chemical composition regulated by EMA quality guidelines, because they are not drugs, regulatory speaking. However, the mechanism of mistletoe's anticancer activity and its effectiveness in treating and supporting cancer patients are not fully understood. Because of this lack of transparency and knowledge regarding the matrix chemical composition, we undertook an untargeted metabolomics study of several mistletoe extracts to explore and compare their fingerprints by LC-(HR)MS(/MS) and 1H-NMR. Unexpectedly, we showed that the composition was primarily driven by the manufacturer/preparation method rather than the different host trees. This differential composition may cause differences in immunostimulating and anti-cancer activities of the different commercially available mistletoe extracts as illustrated by structure-activity relationships based on LC-MS/MS and 1H-NMR identifications completed by docking experiments. In conclusion, in order to move towards an evidence-based medicine use of mistletoe, it is a priority to bring rigor and quality, chemically speaking.

The effect of strength-endurance training on serum and urine metabolic profiles of female adolescent volleyball athletes

Aim

Limited investigations on metabolic responses to exercise training in female adolescent volleyball athletes exist. The aim of this study was to obtain serum and urine metabolite markers in female adolescent volleyball athletes within 2-week strength-endurance training using a metabolomics approach coupled with biochemical analysis, which would be potential biomarkers for evaluating the physiological state of athletes.

Methods

Twelve female adolescent volleyball athletes were recruited for 2-week strength-endurance training. Differential serum and urine metabolic profiles between the pre- and post-training group were obtained on gas chromatography coupled to mass spectrometry (GC-MS) and data subsequently underwent orthogonal partial least-squares analysis (OPLS).

Results

Strength-endurance training exerted a significant influence on the athletes' serum and urine metabolic profiles. The changed metabolites were primarily involved in energy metabolism, lipid metabolism and amino acids metabolism. Results support the hypothesis that female athletes displayed an increased propensity to oxidize lipids as the major energy source. Exposure to strength-endurance training also led to a significant increase in cortisol, but a decrease in testosterone, indicating disordered hormone adjustment. Exercise-induced oxidative stress occurred, as was evidenced by the decrease in reduced glutathione, and increases in blood malondialdehyde and oxidized glutathione. Since the muscle damage markers creatine kinase and lactate dehydrogenase did not show significant changes, the training might not cause cell membrane damage and the athletes did not cross the adaptive injury level.

Conclusion

By measurement of endogenous metabolites, the metabolomics study has the potential to reveal the global physiological changes in response to exercise training.

Applications of Multi-Omics Technologies for Crop Improvement

Multiple "omics" approaches have emerged as successful technologies for plant systems over the last few decades. Advances in next-generation sequencing (NGS) have paved a way for a new generation of different omics, such as genomics, transcriptomics, and proteomics. However, metabolomics, ionomics, and phenomics have also been well-documented in crop science. Multi-omics approaches with high throughput techniques have played an important role in elucidating growth, senescence, yield, and the responses to biotic and abiotic stress in numerous crops. These omics approaches have been implemented in some important crops including wheat (Triticum aestivum L.), soybean (Glycine max), tomato (Solanum lycopersicum), barley (Hordeum vulgare L.), maize (Zea mays L.), millet (Setaria italica L.), cotton (Gossypium hirsutum L.), Medicago truncatula, and rice (Oryza sativa L.). The integration of functional genomics with other omics highlights the relationships between crop genomes and phenotypes under specific physiological and environmental conditions. The purpose of this review is to dissect the role and integration of multi-omics technologies for crop breeding science. We highlight the applications of various omics approaches, such as genomics, transcriptomics, proteomics, metabolomics, phenomics, and ionomics, and the implementation of robust methods to improve crop genetics and breeding science. Potential challenges that confront the integration of multi-omics with regard to the functional analysis of genes and their networks as well as the development of potential traits for crop improvement are discussed. The panomics platform allows for the integration of complex omics to construct models that can be used to predict complex traits. Systems biology integration with multi-omics datasets can enhance our understanding of molecular regulator networks for crop improvement. In this context, we suggest the integration of entire omics by employing the "phenotype to genotype" and "genotype to phenotype" concept. Hence, top-down (phenotype to genotype) and bottom-up (genotype to phenotype) model through integration of multi-omics with systems biology may be beneficial for crop breeding improvement under conditions of environmental stresses.

Real-Time Volatilomics: A Novel Approach for Analyzing Biological Samples

The use of the 'omics techniques in environmental research has become common-place. The most widely implemented of these include metabolomics, proteomics, genomics, and transcriptomics. In recent years, a similar approach has also been taken with the analysis of volatiles from biological samples, giving rise to the so-called 'volatilomics' in plant analysis. Developments in direct infusion mass spectrometry (DI-MS) techniques have made it possible to monitor the changes in the composition of volatile flux from parts of plants, single specimens, and entire ecosystems in real-time. The application of these techniques enables a unique insight into the dynamic metabolic processes that occur in plants. Here, we provide an overview of the use of DI-MS in real-time volatilomics research involving plants.

Metabolomic profile of diabetic retinopathy: a GC-TOFMS-based approach using vitreous and aqueous humor

AIM

To identify the potential metabolite markers in diabetic retinopathy (DR) by using gas chromatography coupled with time-of-flight mass spectrometry (GC-TOFMS).

METHODS

GC-TOFMS spectra were acquired from vitreous and aqueous humor (AH) samples of patients with DR and non-diabetic participants. Comparative analysis was used to elucidate the distinct metabolites of DR. Metabolic pathway was employed to explicate the metabolic reprogramming pathways involved in DR. Logistic regression and receiver-operating characteristic analyses were carried out to select and validate the biomarker metabolites and establish a therapeutic model.

RESULTS

Comparative analysis showed a clear separation between disease and control groups. Eight differentiating metabolites from AH and 15 differentiating metabolites from vitreous were highlighted. Out of these 23 metabolites, 11 novel metabolites have not been detected previously. Pathway analysis identified nine pathways (three in AH and six in vitreous) as the major disturbed pathways associated with DR. The abnormal of gluconeogenesis, ascorbate-aldarate metabolism, valine-leucine-isoleucine biosynthesis, and arginine-proline metabolism might weigh the most in the development of DR. The AUC of the logistic regression model established by D-2,3-Dihydroxypropanoic acid, isocitric acid, fructose 6-phosphate, and L-Lactic acid in AH was 0.965. The AUC established by pyroglutamic acid and pyruvic acid in vitreous was 0.951.

CONCLUSIONS

These findings have expanded our understanding of identified metabolites and revealed for the first time some novel metabolites in DR. These results may provide useful information to explore the mechanism and may eventually allow the development of metabolic biomarkers for prognosis and novel therapeutic strategies for the management of DR.

Clinical insights gained through metabolomic analysis of human breast milk

Introduction: Among the OMICS technologies, that have emerged in recent years, metabolomics has allowed relevant step forwards in clinical research. Several improvements in disease diagnosis and clinical management have been permitted, even in neonatology. Among potentially evaluable biofluids, breast milk (BM) results are highly interesting, representing a fluid of conjunction between mothers newborns, describing their interaction.Areas covered: in this review, updating a previous review article, we discuss research articles and reviews on BM metabolomics and found in MEDLINE using metabolomics, breast milk, neonatal nutrition, breastfeeding, human milk composition, and preterm neonates as keywords.Expert opinion: Our research group has a profound interest in metabolomics research. In 2012, we published the first metabolomic analysis on BM samples, reporting interesting data on its composition and relevant differences with formula milk (FM), useful to improve FM composition. As confirmed by successive studies, such technology can detect the specific BM composition and its dependence on several variables, including lactation stage, gestational age, maternal or environmental conditions. Moreover, since BM contaminants or drug levels can be detected, metabolomics also results useful to determine BM safety. These are only a few practical applications of BM analysis, which will be reviewed in this paper.

Metabolomic Profile of Posner-Schlossman Syndrome: A Gas Chromatography Time-of-Flight Mass Spectrometry-Based Approach Using Aqueous Humor

The Posner-Schlossman syndrome (PSS) is a disease with clinically recurrent unilateral anterior uveitis with markedly elevated intraocular pressure (IOP) and subsequent progression to optic neuropathy. Retrospective studies have reported increased annual incidence of PSS, especially in China. While currently, the clinical management of PSS is still challenging. Metabolomics is considered to be a sensitive approach for the development of novel targeted therapeutics because of its direct elucidation of pathophysiological mechanisms. Therefore, we adopted gas chromatography time-of-flight mass spectrometry (GC-TOF-MS) technology-based non-targeted metabolomics approach to measure comprehensive metabolic profiles of aqueous humor (AH) samples obtained from patients with PSS, with an aim to demonstrate the underlying pathophysiology, identify potential biomarkers specific to PSS, and develop effective treatment strategies. A comparative analysis was used to indicate the distinct metabolites of PSS. Pathway analysis was conducted using MetaboAnalyst 4.0 to explore the metabolic reprogramming pathways involved in PSS. Logistic regression and receiver-operating characteristic (ROC) analyses were employed to evaluate the diagnostic capability of selected metabolites. Comparative analysis revealed a clear separation between PSS and control groups. Fourteen novel differentiating metabolites from AH samples obtained from patients with PSS were highlighted. Pathway analysis identified 11 carbohydrate, amino acid metabolism and energy metabolism pathways as the major disturbed pathways associated with PSS. The abnormal lysine degradation metabolism, valine-leucine-isoleucine biosynthesis, and citrate circle were considered to weigh the most in the development of PSS. The ROC analysis implied that the combination of glycine and homogentisic acid could serve as potential biomarkers for the discrimination of control and PSS groups. In conclusion, these results revealed for the first time the identity of important metabolites and pathways contributing to the development/progression of PSS, enabled the better understanding of the mechanism of PSS, and might lead to the development of metabolic biomarkers and novel therapeutic strategies to restrict the development/progression of PSS.

Cellular Metabolomics Reveal the Mechanism Underlying the Anti-Atherosclerotic Effects of Aspirin Eugenol Ester on Vascular Endothelial Dysfunction

Aspirin eugenol ester (AEE) possesses anti-thrombotic, anti-atherosclerotic and anti-oxidative effects. The study aims to clarify the mechanism underlying the anti-atherosclerotic effects of AEE on vascular endothelial dysfunction. Both the high-fat diet (HFD)-induced atherosclerotic rat model and the H2O2-induced human umbilical vein endothelial cells (HUVECs) model were used to investigate the effects of AEE on vascular endothelial dysfunction. UPLC/QTOF-MS coupled with a multivariate data analysis method were used to profile the variations in the metabolites of HUVECs in response to different treatments. Pretreatment of HUVECs with AEE significantly ameliorated H2O2-induced apoptosis, the overexpression of E-selectin and VCAM-1, and the adhesion of THP-1 cells. Putative endogenous biomarkers associated with the inhibition of endothelial dysfunction were identified in HUVECs pretreated with AEE in the absence or presence of H2O2, and these biomarkers were involved in important metabolic pathways, including amino acid metabolism, carbohydrate metabolism, and glutathione metabolism. Moreover, in vivo, AEE also significantly reduced vascular endothelial dysfunction and decreased the overexpression of VCAM-1 and E-selectin. Based on our findings, the mechanism underlying the anti-atherosclerotic effects of AEE might be related to a reduction in vascular endothelial dysfunction mediated by ameliorating alterations in metabolism, inhibiting oxidative stress, and decreasing the expression of adhesion molecules.