Guide to Metabolomics Analysis: A Bioinformatics Workflow

Multiomics to Characterize the Molecular Events Underlying Impaired Glucose Tolerance in FXR-Knockout Mice

The prevalence of different metabolic syndromes has grown globally, and the farnesoid X receptor (FXR), a metabolic homeostat for glucose, lipid, and bile acid metabolisms, may serve an important role in the progression of metabolic disorders. Glucose intolerance by FXR deficiency was previously reported and observed in our study, but the underlying biology remained unclear. To investigate the ambiguity, we collected the nontargeted profiles of the fecal metaproteome, serum metabolome, and liver proteome in Fxr-null (Fxr-/-) and wild-type (WT) mice with LC-HRMS. FXR deficiency showed a global impact on the different molecular levels we monitored, suggesting its serious disruption in the gut microbiota, hepatic metabolism, and circulating biomolecules. The network and enrichment analyses of the dysregulated metabolites and proteins suggested the perturbation of carbohydrate and lipid metabolism by FXR deficiency. Fxr-/- mice presented lower levels of hepatic proteins involved in glycogenesis. The impairment of glycogenesis by an FXR deficiency may leave glucose to accumulate in the circulation, which may deteriorate glucose tolerance. Lipid metabolism was dysregulated by FXR deficiency in a structural-dependent manner. Fatty acid β-oxidations were alleviated, but cholesterol metabolism was promoted by an FXR deficiency. Together, we explored the molecular events associated with glucose intolerance by impaired FXR with integrated novel multiomic data.

Advancements, Challenges, and clinical implications of integration of metabolomics technologies in diabetic nephropathy

BACKGROUND

Diabetic nephropathy (DN), a severe complication of diabetes, involves a range of renal abnormalities driven by metabolic derangements. Metabolomics, revealing dynamic metabolic shifts in diseases like DN and offering insights into personalized treatment strategies, emerges as a promising tool for improved diagnostics and therapies.

METHODS

We conducted an extensive literature review to examine how metabolomics contributes to the study of DN and the challenges associated with its implementation in clinical practice. We identified and assessed relevant studies that utilized metabolomics methods, including nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) to assess their efficacy in diagnosing DN.

RESULTS

Metabolomics unveils key pathways in DN progression, highlighting glucose metabolism, dyslipidemia, and mitochondrial dysfunction. Biomarkers like glycated albumin and free fatty acids offer insights into DN nuances, guiding potential treatments. Metabolomics detects small-molecule metabolites, revealing disease-specific patterns for personalized care.

CONCLUSION

Metabolomics offers valuable insights into the molecular mechanisms underlying DN progression and holds promise for personalized medicine approaches. Further research in this field is warranted to elucidate additional metabolic pathways and identify novel biomarkers for early detection and targeted therapeutic interventions in DN.

Metabolomics for dental caries diagnosis: Past, present, and future

Dental caries, a prevalent global infectious condition affecting over 95% of adults, remains elusive in its precise etiology. Addressing the complex dynamics of caries demands a thorough exploration of taxonomic, potential, active, and encoded functions within the oral ecosystem. Metabolomic profiling emerges as a crucial tool, offering immediate insights into microecosystem physiology and linking directly to the phenotype. Identified metabolites, indicative of caries status, play a pivotal role in unraveling the metabolic processes underlying the disease. Despite challenges in metabolite variability, the use of metabolomics, particularly via mass spectrometry and nuclear magnetic resonance spectroscopy, holds promise in caries research. This review comprehensively examines metabolomics in caries prevention, diagnosis, and treatment, highlighting distinct metabolite expression patterns and their associations with disease-related bacterial communities. Pioneering in approach, it integrates singular and combinatory metabolomics methodologies, diverse biofluids, and study designs, critically evaluating prior limitations while offering expert insights for future investigations. By synthesizing existing knowledge, this review significantly advances our comprehension of caries, providing a foundation for improved prevention and treatment strategies.

Combined transcriptomic and metabolomic analysis revealed that pH changes affected the expression of carbohydrate and ribosome biogenesis-related genes in Aspergillus niger SICU-33

The process of carbohydrate metabolism and genetic information transfer is an important part of the study on the effects of the external environment on microbial growth and development. As one of the most significant environmental parameters, pH has an important effect on mycelial growth. In this study, the effects of environmental pH on the growth and nutrient composition of Aspergillus niger (A. niger) filaments were determined. The pH values of the medium were 5, 7, and 9, respectively, and the molecular mechanism was further investigated by transcriptomics and metabolomics methods. The results showed that pH 5 and 9 significantly inhibited filament growth and polysaccharide accumulation of A. niger. Further, the mycelium biomass of A. niger and the crude polysaccharide content was higher when the medium's pH was 7. The DEGs related to ribosome biogenesis were the most abundant, and the downregulated expression of genes encoding XRN1, RRM, and RIO1 affected protein translation, modification, and carbohydrate metabolism in fungi. The dynamic changes of pargyline and choline were in response to the oxidative metabolism of A. niger SICU-33. The ribophorin_I enzymes and DL-lactate may be important substances related to pH changes during carbohydrate metabolism of A.niger SICU-33. The results of this study provide useful transcriptomic and metabolomic information for further analyzing the bioinformatic characteristics of A. niger and improving the application in ecological agricultural fermentation.

Integrating metabolomics into reproduction: Sperm metabolism and fertility enhancement in pigs

The last decades of research have revealed that many other factors besides gamete genomes are able to determine the reproductive outcomes. Indeed, paternal factors have been observed to be capable of modulating multiple crucial features of the reproductive process, such as sperm physiology, the maternal environment and, even, the offspring health. These recent advances have been encompassed with the emergence of OMICS technologies, as they comprehensively characterise the molecular composition of biological systems. The present narrative review aimed to take a closer look at the potential of these technologies in the field of reproductive biology. This literature revision shows that most studies up to date have followed a non-targeted approach to screen mammalian seminal plasma (SP) and sperm metabolite composition through different metabolome platforms. These studies have proposed metabolites of multiple natures as potential in vivo fertility biomarkers. Yet, targeted approaches can be used to answer specific biological question, and their power is exemplified herein. For instance, metabolomic studies have uncovered not only that glycolysis is the main ATP energy source of pig sperm, but also that sperm metabolism can trigger DNA damage, hence compromise embryo development. In conclusion, this review shows the potential of both non-targeted and targeted metabolomics for the discovery of cell pathways that govern the reproductive process. Understanding these systems could help make progress in different areas, including livestock efficient breeding, the improvement of artificial reproductive technologies, and the development of biomarkers for infertility detection.

Effects of taurine on metabolomics of bovine mammary epithelial cells under high temperature conditions

High temperature induces heat stress, adversely affecting the growth and lactation performance of cows. Research has shown the protective effect of taurine against hepatotoxicity both in vivo and in vitro. This study aimed to investigate the effect of taurine on the metabolomics of mammary epithelial cells of dairy cows under high-temperature conditions. Mammary epithelial cells were exposed to 0 mmol/L (HS, control), 8 mmol/L (HT-8), and 32 mmol/L (HT-32) of taurine, then incubated at 42°C for 6 h. Metabolomics analysis was conducted using Liquid Chromatograph Mass Spectrometer (LC-MS). Compared with the HS group, 2,873 and 3,243 metabolites were detected in the HT-8 group in positive and negative ion modes. Among these, 108 and 97 metabolites were significantly upregulated in positive and negative ion modes, while 60 and 166 metabolites were downregulated. Notably, 15 different metabolites such as palmitic acid, adenine and hypoxanthine were screened out in the HT-8 group. Compared with the HS group, 2,873 and 3,243 metabolites were, respectively, detected in the HT-32 group in the positive and negative ion modes. Among those metabolites, 206 metabolites were significantly up-regulated, while 206 metabolites were significantly downregulated in the positive mode. On the other hand, 497 metabolites were significantly upregulated in the negative mode, while 517 metabolites were reported to be downregulated. Noteworthy, 30 distinct metabolites, such as palmitic acid, phytosphingosine, hypoxanthine, nonanoic acid, and octanoic acid, were screened out in the HT-32 group. KEGG enrichment analysis showed that these metabolites were mainly involved in lipid metabolism, purine metabolism and other biological processes. Overall, our study indicates that taurine supplementation alters the metabolites primarily associated with purine metabolism, lipid metabolism and other pathways to alleviate heat stress in bovine mammary epithelial cells.

Metabolomics and Proteomics in Prostate Cancer Research: Overview, Analytical Techniques, Data Analysis, and Recent Clinical Applications

Prostate cancer (PCa) is a significant global contributor to mortality, predominantly affecting males aged 65 and above. The field of omics has recently gained traction due to its capacity to provide profound insights into the biochemical mechanisms underlying conditions like prostate cancer. This involves the identification and quantification of low-molecular-weight metabolites and proteins acting as crucial biochemical signals for early detection, therapy assessment, and target identification. A spectrum of analytical methods is employed to discern and measure these molecules, revealing their altered biological pathways within diseased contexts. Metabolomics and proteomics generate refined data subjected to detailed statistical analysis through sophisticated software, yielding substantive insights. This review aims to underscore the major contributions of multi-omics to PCa research, covering its core principles, its role in tumor biology characterization, biomarker discovery, prognostic studies, various analytical technologies such as mass spectrometry and Nuclear Magnetic Resonance, data processing, and recent clinical applications made possible by an integrative "omics" approach. This approach seeks to address the challenges associated with current PCa treatments. Hence, our research endeavors to demonstrate the valuable applications of these potent tools in investigations, offering significant potential for understanding the complex biochemical environment of prostate cancer and advancing tailored therapeutic approaches for further development.

How Much Storage Precision Can Be Lost: Guidance for Near-Lossless Compression of Untargeted Metabolomics Mass Spectrometry Data

Several lossy compressors have achieved superior compression rates for mass spectrometry (MS) data at the cost of storage precision. Currently, the impacts of precision losses on MS data processing have not been thoroughly evaluated, which is critical for the future development of lossy compressors. We first evaluated different storage precision (32 bit and 64 bit) in lossless mzML files. We then applied 10 truncation transformations to generate precision-lossy files: five relative errors for intensities and five absolute errors for m/z values. MZmine3 and XCMS were used for feature detection and GNPS for compound annotation. Lastly, we compared Precision, Recall, F1 - score, and file sizes between lossy files and lossless files under different conditions. Overall, we revealed that the discrepancy between 32 and 64 bit precision was under 1%. We proposed an absolute m/z error of 10-4 and a relative intensity error of 2 × 10-2, adhering to a 5% error threshold (F1 - scores above 95%). For a stricter 1% error threshold (F1 - scores above 99%), an absolute m/z error of 2 × 10-5 and a relative intensity error of 2 × 10-3 were advised. This guidance aims to help researchers improve lossy compression algorithms and minimize the negative effects of precision losses on downstream data processing.

Highly sensitive serum volatolomic biomarkers for pancreatic cancer diagnosis

The discovery of new diagnostic tools for the early detection of diseases with poor prognosis such as pancreatic adenocarcinoma (PAC) is of high importance. The results from a control-case study (20 PAC patients, 19 healthy controls) for the search of new biomarkers of pancreatic cancer based in differences in the serum volatolome are presented in this work. Volatolomics were performed following a non-targeted HS-SPME-GC/MS approach, and a total of 433 volatile organic compounds (VOCs) was detected in the human serum samples. Of these, 125 VOC indexes showed a significant variation when controls and patients were compared (p-value < 0.05). Bonferroni corrected p-values < 0.05 were found for 40 features. PCA analysis showed the control-PAC discrimination capability of VOCs in serum, and PLS-DA was performed to select the best candidate biomarkers for the diagnosis of PAC. For the 40 selected VOCs, calculated areas under the curve (AUC) ranged from 0.98 to 0.85, and 11 of them were successfully validated using an independent set of samples (5 PAC patients, 5 healthy controls). Four of the proposed PAC biomarkers were identified as toluene, 2-ethyl-1-hexanol, pentylbenzene, and butoxymethylbenzene. Combinations of the identified PAC biomarkers were tested and showed AUC > 0.90, with the more promising candidate being butoxymethylbenzene (AUC = 0.98).

From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies

In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for drug discovery, is underpinned by big data, a transformative force in the current era. Omics data, characterized by its heterogeneity and enormity, has ushered biological and biomedical research into the big data domain. Acknowledging the significance of integrating diverse omics data strata, known as multi-omics studies, researchers delve into the intricate interrelationships among various omics layers. This review navigates the expansive omics landscape, showcasing tailored assays for each molecular layer through genomes to metabolomes. The sheer volume of data generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging as robust tools. These datasets not only refine disease classification but also enhance diagnostics and foster the development of targeted therapeutic strategies. Through the integration of high-throughput data, the review focuses on targeting and modeling multiple disease-regulated networks, validating interactions with multiple targets, and enhancing therapeutic potential using network pharmacology approaches. Ultimately, this exploration aims to illuminate the transformative impact of multi-omics in the big data era, shaping the future of biological research.

Understanding PFAS toxicity through cell culture metabolomics: Current applications and future perspectives

Per- and polyfluoroalkyl substances (PFAS), ubiquitous environmental contaminants, pose significant challenges to ecosystems and human health. While cell cultures have emerged as new approach methodologies (NAMs) in ecotoxicity research, metabolomics is an emerging technique used to characterize the small-molecule metabolites present in cells and to understand their role in various biological processes. Integration of metabolomics with cell cultures, known as cell culture metabolomics, provides a novel and robust tool to unravel the complex molecular responses induced by PFAS exposure. In vitro testing also reduces reliance on animal testing, aligning with ethical and regulatory imperatives. The current review summarizes key findings from recent studies utilizing cell culture metabolomics to investigate PFAS toxicity, highlighting alterations in metabolic pathways, biomarker identification, and the potential linkages between metabolic perturbations. Additionally, the paper discusses different types of cell cultures and metabolomics methods used for studies of environmental contaminants and particularly PFAS. Future perspectives on the combination of metabolomics with other advanced technologies, such as single-cell metabolomics (SCM), imaging mass spectrometry (IMS), extracellular flux analysis (EFA), and multi-omics are also explored, which offers a holistic understanding of environmental contaminants. The synthesis of current knowledge and identification of research gaps provide a foundation for future investigations that aim to elucidate the complexities of PFAS-induced cellular responses and contribute to the development of effective strategies for mitigating their adverse effects on human health.

Psat1-generated α-ketoglutarate and glutamine promote muscle stem cell activation and regeneration

By satisfying bioenergetic demands, generating biomass, and providing metabolites serving as cofactors for chromatin modifiers, metabolism regulates adult stem cell biology. Here, we report that a branch of glycolysis, the serine biosynthesis pathway (SBP), is activated in regenerating muscle stem cells (MuSCs). Gene inactivation and metabolomics revealed that Psat1, one of the three SBP enzymes, controls MuSC activation and expansion of myogenic progenitors through production of the metabolite α-ketoglutarate (α-KG) and α-KG-generated glutamine. Psat1 ablation resulted in defective expansion of MuSCs and impaired regeneration. Psat1, α-KG, and glutamine were reduced in MuSCs of old mice. α-KG or glutamine re-established appropriate muscle regeneration of adult conditional Psat1 -/- mice and of old mice. These findings contribute insights into the metabolic role of Psat1 during muscle regeneration and suggest α-KG and glutamine as potential therapeutic interventions to ameliorate muscle regeneration during aging.

Bridging the gap between omics research and dental practice

AIM

The burgeoning field of omics research has witnessed exponential growth in both medicine and dentistry. However, despite more than a decade of advancements, clinical dentistry, particularly in Low- and Middle-Income Countries (LMICs), has seen limited progress in integrating omics-based approaches into routine practice. This review aims to provide a comprehensive overview of the integration of omics approaches in dentistry, focusing on the challenges and opportunities for translating research findings into clinical practice.

METHODS

we conducted a literature review using key databases to provide a brief overview of the history of genomics in dentistry. Additionally, we summarised recent breakthroughs in omics relevant to oral health practitioners, emphasising the inadequate translation of omics research into clinical practice.

RESULTS

Despite significant growth in omics research in both medicine and dentistry, its translation into routine clinical practice in dentistry remains limited. We summarise recent breakthroughs in omics and highlight the gap between research advancements and clinical implementation.

DISCUSSION AND CONCLUSION

The integration of omics approaches holds promise for enhancing diagnostics, personalised treatment strategies, and preventive measures in dental practice, ushering in a new era of precision oral healthcare. However, several challenges, including infrastructure limitations, cost-effectiveness, and education gaps, hinder the widespread adoption of omics-based approaches in clinical dentistry. A strong commitment to transforming dentistry is required to embrace this transition. This shift has the potential to revolutionise oral healthcare by advancing precision diagnostics and treatment strategies tailored to individual patient needs.

Holomics - a user-friendly R shiny application for multi-omics data integration and analysis

An organism's observable traits, or phenotype, result from intricate interactions among genes, proteins, metabolites and the environment. External factors, such as associated microorganisms, along with biotic and abiotic stressors, can significantly impact this complex biological system, influencing processes like growth, development and productivity. A comprehensive analysis of the entire biological system and its interactions is thus crucial to identify key components that support adaptation to stressors and to discover biomarkers applicable in breeding programs or disease diagnostics. Since the genomics era, several other 'omics' disciplines have emerged, and recent advances in high-throughput technologies have facilitated the generation of additional omics datasets. While traditionally analyzed individually, the last decade has seen an increase in multi-omics data integration and analysis strategies aimed at achieving a holistic understanding of interactions across different biological layers. Despite these advances, the analysis of multi-omics data is still challenging due to their scale, complexity, high dimensionality and multimodality. To address these challenges, a number of analytical tools and strategies have been developed, including clustering and differential equations, which require advanced knowledge in bioinformatics and statistics. Therefore, this study recognizes the need for user-friendly tools by introducing Holomics, an accessible and easy-to-use R shiny application with multi-omics functions tailored for scientists with limited bioinformatics knowledge. Holomics provides a well-defined workflow, starting with the upload and pre-filtering of single-omics data, which are then further refined by single-omics analysis focusing on key features. Subsequently, these reduced datasets are subjected to multi-omics analyses to unveil correlations between 2-n datasets. This paper concludes with a real-world case study where microbiomics, transcriptomics and metabolomics data from previous studies that elucidate factors associated with improved sugar beet storability are integrated using Holomics. The results are discussed in the context of the biological background, underscoring the importance of multi-omics insights. This example not only highlights the versatility of Holomics in handling different types of omics data, but also validates its consistency by reproducing findings from preceding single-omics studies.

Current Trends and Challenges of Microbiome Research in Bladder Cancer

PURPOSE OF THE REVIEW

Microbiome research has provided valuable insights into the associations between microbial communities and bladder cancer. However, this field faces significant challenges that hinder the interpretation, generalization, and translation of findings into clinical practice. This review aims to elucidate these challenges and highlight the importance of addressing them for the advancement of microbiome research in bladder cancer.

RECENT FINDINGS

Recent findings underscore the complexities involved in microbiome research, particularly in the context of bladder cancer. Challenges include low microbial biomass in urine samples, potential contamination issues during collection and processing, variability in sequencing methods and primer selection, and the difficulty of establishing causality between microbiota and bladder cancer. Studies have shown the impact of sample storage conditions and DNA isolation kits on microbiome analysis, emphasizing the need for standardization. Additionally, variations in urine collection methods can introduce contamination and affect results. The choice of 16S rRNA gene amplicon sequencing or shotgun metagenomic sequencing introduces technical challenges, including primer selection and sequencing read length. Establishing causality between the microbiota and bladder cancer requires experimental methods like fecal microbiota transplantation and human microbiota-associated murine models, which face their own set of challenges. Translating microbiome research into therapeutic applications is hindered by methodological variability, incomplete understanding of bioactive molecules, imperfect animal models, and the inherent heterogeneity of microbiome communities among individuals. Microbiome research in bladder cancer presents significant challenges stemming from technical and conceptual complexities. Addressing these challenges through standardization, improved experimental models, and advanced analytical approaches is essential for advancing our understanding of the microbiome's role in bladder cancer and its potential clinical applications. Achieving this goal can lead to improved patient outcomes and novel therapeutic strategies in the future.

Potential Mechanisms of Precision Nutrition-Based Interventions for Managing Obesity

Precision nutrition (PN) considers multiple individual-level and environmental characteristics or variables to better inform dietary strategies and interventions for optimizing health, including managing obesity and metabolic disorders. Here, we review the evidence on potential mechanisms-including ones to identify individuals most likely to respond-that can be leveraged in the development of PN interventions addressing obesity. We conducted a review of the literature and included laboratory, animal, and human studies evaluating biochemical and genetic data, completed and ongoing clinical trials, and public programs in this review. Our analysis describes the potential mechanisms related to 6 domains including genetic predisposition, circadian rhythms, physical activity and sedentary behavior, metabolomics, the gut microbiome, and behavioral and socioeconomic characteristics, i.e., the factors that can be leveraged to design PN-based interventions to prevent and treat obesity-related outcomes such as weight loss or metabolic health as laid out by the NIH 2030 Strategic Plan for Nutrition Research. For example, single nucleotide polymorphisms can modify responses to certain dietary interventions, and epigenetic modulation of obesity risk via physical activity patterns and macronutrient intake have also been demonstrated. Additionally, we identified limitations including questions of equitable implementation across a limited number of clinical trials. These include the limited ability of current PN interventions to address systemic influences such as supply chains and food distribution, healthcare systems, racial or cultural inequities, and economic disparities, particularly when designing and implementing PN interventions in low- and middle-income communities. PN has the potential to help manage obesity by addressing intra- and inter-individual variation as well as context, as opposed to "one-size fits all" approaches though there is limited clinical trial evidence to date.

Comprehensive Analysis of 11 Species of Euodia (Rutaceae) by Untargeted LC-IT-TOF/MS Metabolomics and In Vitro Functional Methods

The Euodia genus comprises numerous untapped medicinal plants that warrant thorough evaluation for their potential as valuable natural sources of herbal medicine or food flavorings. In this study, untargeted metabolomics and in vitro functional methods were employed to analyze fruit extracts from 11 significant species of the Euodia genus. An investigation of the distribution of metabolites (quinolone and indole quinazoline alkaloids) in these species indicated that E. rutaecarpa (Euodia rutaecarpa) was the most widely distributed species, followed by E. compacta (Euodia compacta), E. glabrifolia (Euodia glabrifolia), E. austrosinensis (Euodia austrosinensis), and E. fargesii (Euodia fargesii). There have been reports on the close correlation between indole quinazoline alkaloids and their anti-tumor activity, especially in E. rutaecarpa fruits which exhibit effectiveness against various types of cancer, such as SGC-7901, Hela, A549, and other cancer cell lines. Additionally, the E. rutaecarpa plant contains indole quinazoline alkaloids, which possess remarkable antibacterial properties. Our results offer novel insights into the utilization of Euodia resources in the pharmaceutical industry.

[Research Progress on Tumor Metabolic Biomarkers in Liquid Biopsy of Lung Cancer]

Liquid biopsy is gradually being applied in the clinical diagnosis and treatment of lung cancer. At present, with the development of metabolomics, more and more metabolic biomarkers are considered as potential sensitive markers reflecting the occurrence and development of tumors. This article summarizes the changes in the main metabolic pathways of lung cancer, including glucose metabolism, amino acid metabolism, lipid metabolism, sphingolipid metabolism, glycerophospholipid metabolism, and purine metabolism. Meanwhile, this article reviews the role of metabolic biomarkers in the early diagnosis of lung cancer, predicting disease progression, and evaluating the efficacy of chemotherapy and immunotherapy, aiming to provide effective biomarkers for tumor diagnosis and treatment.
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Phenotypic and metabolomic characteristics of mouse models of metabolic associated steatohepatitis

BACKGROUND

Metabolic associated steatohepatitis (MASH) is metabolic disease that may progress to cirrhosis and hepatocellular carcinoma. Mouse models of diet-induced MASH, which is characterized by the high levels of fats, sugars, and cholesterol in diets, are commonly used in research. However, mouse models accurately reflecting the progression of MASH in humans remain to be established. Studies have explored the potential use of serological metabolites as biomarkers of MASH severity in relation to human MASH.

METHODS

We performed a comparative analysis of three mouse models of diet-induced MASH in terms of phenotypic and metabolomic characteristics; MASH was induced using different diets: a high-fat diet; a Western diet; and a high-fat, high-cholesterol diet. Liver cirrhosis was diagnosed using standard clinical approaches (e.g., METAVIR score, hyaluronan level, and collagen deposition level). Mouse serum samples were subjected to nuclear magnetic resonance spectroscopy-based metabolomic profiling followed by bioinformatic analyses. Metabolomic analysis of a retrospective cohort of patients with hepatocellular carcinoma was performed; the corresponding cirrhosis scores were also evaluated.

RESULTS

Using clinically relevant quantitative diagnostic methods, the severity of MASH was evaluated. Regarding metabolomics, the number of lipoprotein metabolites increased with both diet and MASH progression. Notably, the levels of very low-density lipoprotein (VLDL) and low-density lipoprotein (LDL) significantly increased with fibrosis progression. During the development of diet-induced MASH in mice, the strongest upregulation of expression was noted for VLDL receptor. Metabolomic analysis of a retrospective cohort of patients with cirrhosis indicated lipoproteins (e.g., VLDL and LDL) as predominant biomarkers of cirrhosis.

CONCLUSIONS

Our findings provide insight into the pathophysiology and metabolomics of experimental MASH and its relevance to human MASH. The observed upregulation of lipoprotein expression reveals a feedforward mechanism for MASH development that may be targeted for the development of noninvasive diagnosis.

PathBank 2.0-the pathway database for model organism metabolomics

PathBank (https://pathbank.org) and its predecessor database, the Small Molecule Pathway Database (SMPDB), have been providing comprehensive metabolite pathway information for the metabolomics community since 2010. Over the past 14 years, these pathway databases have grown and evolved significantly to meet the needs of the metabolomics community and respond to continuing changes in computing technology. This year's update, PathBank 2.0, brings a number of important improvements and upgrades that should make the database more useful and more appealing to a larger cross-section of users. In particular, these improvements include: (i) a significant increase in the number of primary or canonical pathways (from 1720 to 6951); (ii) a massive increase in the total number of pathways (from 110 234 to 605 359); (iii) significant improvements to the quality of pathway diagrams and pathway descriptions; (iv) a strong emphasis on drug metabolism and drug mechanism pathways; (v) making most pathway images more slide-compatible and manuscript-compatible; (vi) adding tools to support better pathway filtering and selecting through a more complete pathway taxonomy; (vii) adding pathway analysis tools for visualizing and calculating pathway enrichment. Many other minor improvements and updates to the content, the interface and general performance of the PathBank website have also been made. Overall, we believe these upgrades and updates should greatly enhance PathBank's ease of use and its potential applications for interpreting metabolomics data.

Untargeted metabolomic in basil cell cultures - a case study of Ocimum basilicum L. var. minimum Alef

AIMS

Due to the lack of experimental databases, together with the chemical complexity and the dynamic nature of plants' metabolome, most of the metabolites in complex biological materials (like plant in vitro tissue cultures) are not-annotated, unidentified metabolites. In this study, a method for further metabolite characterization and classification based on the UPLC-HESI-HRMS/MS approach for small-leaved basil (Ocimum basilicum L. var. minimum Alef.) callus and cell suspension culture is presented.

KEY RESULTS

A total of 2168 metabolic features were detected, out of which the database for exact mass metabolic profiling for 1949 metabolites is presented here since there is no available database dedicated to O. basilicum. We further focused on secondary metabolites (particularly phenolic compounds). The presence of 60 different phenolic compounds belonging mainly to the groups of flavonoids, glycosides, terpenoids, and phenolic acids is confirmed. By comparing relative abundances of phenolic compounds from callus culture and cell suspension culture, both grown on two types of media, via svd-PCА, univariate analysis, post-hoc tests, and heatmapping of metabolites, we provided a practical example of how resources presented here can be further applied in tissue culture-based basil metabolomics studies.

UTILITY

This study represents the first approach toward routine targeted investigation of secondary metabolites in basil in vitro cultures and provides various opportunities for new-generation analyses.

Integrated metabolomics and proteomics analysis of plasma lipid metabolism in Parkinson's disease

INTRODUCTION

Metabolomics and proteomics are two growing fields of science which may shed light on the molecular mechanisms that contribute to neurodegenerative diseases. Studies focusing on these aspects can reveal specific metabolites and proteins that can halt or reverse the progressive neurodegenerative process leading to dopaminergic cell death in the brain.

AREAS COVERED

In this article, an overview of the current status of metabolomic and proteomic profiling in the neurodegenerative disease such as Parkinson's disease (PD) is presented. We discuss the importance of state-of-the-art metabolomics and proteomics using advanced analytical methodologies and their potential for discovering new biomarkers in PD. We critically review the research to date, highlighting how metabolomics and proteomics can have an important impact on early disease diagnosis, future therapy development and the identification of new biomarkers. Finally, we will discuss interactions between lipids and α-synuclein (SNCA) and also consider the role of SNCA in lipid metabolism.

EXPERT OPINION

Metabolomic and proteomic studies contribute to understanding the biological basis of PD pathogenesis, identifying potential biomarkers and introducing new therapeutic strategies. The complexity and multifactorial nature of this disease requires a comprehensive approach, which can be achieved by integrating just these two omic studies.

Determination of bile acids in serum of pigs exposed to polychlorinated biphenyls by liquid chromatography-mass spectrometry

Exposure to polychlorinated biphenyls (PCBs) has been linked to dyslipidemia. Under acute exposure to PCBs, it has been observed that the secretion of bile acids (BAs) can be impacted, limiting (indirectly) lipid absorption in the gut. In this context, two non-targeted metabolomics studies on pig serum have recently suggested that BA concentrations may fluctuate under exposure to current non-dioxin-like (NDL)-PCB levels in food, reflecting the acute effects of such chronic exposure. The objective of this research is to implement a targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for BA analysis in order to validate the findings of previous metabolomics studies, in which BA levels in serum samples from pigs exposed to environmental doses of NDL-PCBs were highlighted to be affected. The proposed LC-MS method involves the use of a C18-pentafluorophenyl LC column, which is not usually selected for the separation of BAs, but shows better performance for the separation of isomers than typical C18 columns. This LC-MS method shows excellent analytical performance such as low limits of detection (LODs) (≤1 ng/mL for most BAs) and good linearity (R2 > 0.994), while no matrix effect was observed. A total of 13 BAs have been quantified, while further BA isomers could be detected and semi-quantified. The application of this targeted LC-MS method confirmed previous findings, suggesting that exposure to low doses of NDL-PCBs decreases the concentration of BAs (i.e., glycochenodeoxycholic acid, hyodeoxycholic acid and taurochenodeoxycholic acid) while the effect on the precursors (cholic acid and chenodeoxycholic acid) is less pronounced.

Metabolomic-Based Approaches for Endometrial Cancer Diagnosis and Prognosis: A Review

Endometrial cancer, the most prevalent gynecological malignancy in developed countries, is experiencing a sustained rise in both its incidence and mortality rates, primarily attributed to extended life expectancy and lifestyle factors. Currently, the absence of precise diagnostic tools hampers the effective management of the expanding population of women at risk of developing this disease. Furthermore, patients diagnosed with endometrial cancer require precise risk stratification to align with optimal treatment planning. Metabolomics technology offers a unique insight into the molecular landscape of endometrial cancer, providing a promising approach to address these unmet needs. This comprehensive literature review initiates with an overview of metabolomic technologies and their intrinsic workflow components, aiming to establish a fundamental understanding for the readers. Subsequently, a detailed exploration of the existing body of research is undertaken with the objective of identifying metabolite biomarkers capable of enhancing current strategies for endometrial cancer diagnosis, prognosis, and recurrence monitoring. Metabolomics holds vast potential to revolutionize the management of endometrial cancer by providing accuracy and valuable insights into crucial aspects.

Functional groups matter: metabolomics analysis of Escherichia coli exposed to trans-cinnamic acid and its derivatives unveils common and unique targets

Phenolic acids are derivatives of benzoic and cinnamic acids, which possess important biological activities at certain concentrations. Trans-cinnamic acid (t-CA) and its derivatives, such as p-coumaric acid (p-CA) and ferulic acid (FA) have been shown to have antibacterial activity against various Gram-positive and -negative bacteria. However, there is limited information available concerning the antibacterial mode of action of these phenolic acids. In this study, we aimed to ascertain metabolic alterations associated with exposure to t-CA, p-CA, and FA in Escherichia coli BW25113 using a nuclear magnetic resonance (NMR)-based metabolomics approach. The results showed that t-CA, p-CA, and FA treatments led to significant changes (p < 0.05) in the concentration of 42, 55, and 74% of the identified metabolites in E. coli, respectively. Partial least-squares discriminant analysis (PLS-DA) revealed a clear separation between control and phenolic acid groups with regard to metabolic response. Moreover, it was found that FA and p-CA treatment groups were clustered closely together but separated from the t-CA treatment group. Arginine, putrescine, cadaverine, galactose, and sucrose had the greatest impact on group differentiation. Quantitative pathway analysis demonstrated that arginine and proline, pyrimidine, glutathione, and galactose metabolisms, as well as aminoacyl-tRNA and arginine biosyntheses, were markedly affected by all phenolic acids. Finally, the H2O2 content of E. coli cells was significantly increased in response to t-CA and p-CA whereas all phenolic acids caused a dramatic increase in the number of apurinic/apyrimidinic sites. Overall, this study suggests that the metabolic response of E. coli cells to t-CA is relatively different from that to p-CA and FA. However, all phenolic acids had a certain impact on oxidative/antioxidant status, genomic stability, arginine-related pathways, and nucleic acid metabolism.

Matrix Linear Models for connecting metabolite composition to individual characteristics

High-throughput metabolomics data provide a detailed molecular window into biological processes. We consider the problem of assessing how the association of metabolite levels with individual (sample) characteristics such as sex or treatment may depend on metabolite characteristics such as pathway. Typically this is one in a two-step process: In the first step we assess the association of each metabolite with individual characteristics. In the second step an enrichment analysis is performed by metabolite characteristics among significant associations. We combine the two steps using a bilinear model based on the matrix linear model (MLM) framework we have previously developed for high-throughput genetic screens. Our framework can estimate relationships in metabolites sharing known characteristics, whether categorical (such as type of lipid or pathway) or numerical (such as number of double bonds in triglycerides). We demonstrate how MLM offers flexibility and interpretability by applying our method to three metabolomic studies. We show that our approach can separate the contribution of the overlapping triglycerides characteristics, such as the number of double bonds and the number of carbon atoms. The proposed method have been implemented in the open-source Julia package, MatrixLM. Data analysis scripts with example data analyses are also available.

Metabolomic Signatures of Treatment Response in Bladder Cancer

Bladder cancer (BC) stands as one of the most prevalent urological malignancies, with over 500 thousand newly diagnosed cases annually. Treatment decisions in BC depend on factors like the risk of recurrence, the type of tumor, and the stage of the disease. While standard therapeutic approaches encompass transurethral resection of the bladder tumor, radical cystectomy, and chemo- or immunotherapy, these methods exhibit limited efficacy in mitigating the aggressive and recurrent nature of bladder tumors. To overcome this challenge, it is crucial to develop innovative methods for monitoring and predicting treatment responses among patients with BC. Metabolomics is gaining recognition as a promising approach for discovering biomarkers. It has the potential to reveal metabolic disruptions that precisely reflect how BC patients respond to particular treatments, providing a revolutionary method to improve accuracy in monitoring and predicting outcomes. In this article, we present a comprehensive review of studies employing metabolomics approaches to investigate the metabolic responses associated with different treatment modalities for BC. The review encompasses an exploration of various models, samples, and analytical techniques applied in this context. Special emphasis is placed on the reported changes in metabolite levels derived from these studies, highlighting their potential as biomarkers for personalized medicine in BC.

Identification and validation of diagnostic biomarkers for intrahepatic cholestasis of pregnancy based on untargeted and targeted metabolomics analyses of urine metabolite profiles

BACKGROUND

Intrahepatic cholestasis of pregnancy (ICP) is a prevalent pregnancy-specific complication that presents with maternal itching and elevated serum bile acid levels. ICP is associated with unfavorable pregnancy outcomes, severely decreasing the pregnant woman's quality of life. Timely identification of ICP is crucial for effective management and improved outcomes.

METHODS

We collected urine samples from 8 patients with ICP and 8 healthy individuals. We used Liquid Chromatography-Mass Spectrometry (LC-MS) to detect metabolite expression levels, then conducted a series of bioinformatic analyses to explore the potential biological meanings of differentially expressed metabolites, and preliminarily discovered several candidate biomarkers. To validate these candidate biomarkers, we performed Gas Chromatography-Mass Spectrometry (GC-MS) detection and analyzed their diagnostic values using receiver operating characteristic (ROC) curve.

RESULTS

Untargeted metabolomics data showed that 6129 positive peaks and 6218 negative peaks were extracted from each specimen. OPLS-DA analysis and the heat map for cluster analysis showed satisfactory capability in discriminating ICP specimens from controls. Subsequent analysis extracted 64 significantly differentially expressed metabolites, which could be potential biomarkers for diagnosis of ICP. Based on the KEGG enrichment analyses, six candidate biomarkers were preliminarily identified. Two most promising biomarkers (3-hydroxypropionic acid and uracil) were validated by targeted metabolomics analyses with the area under the curve (AUC) of 0.920 and 0.850 respectively.

CONCLUSION

Based on preliminary screening from untargeted metabolomics and subsequent validation through targeted metabolomics, 3-hydroxypropionic acid and uracil were identified as promising diagnostic biomarkers for ICP.

Metabolomics of early blight (Alternaria solani) susceptible tomato (Solanum lycopersicum) unfolds key biomarker metabolites and involved metabolic pathways

Tomato (Solanum lycopersicum) is among the most important commercial horticultural crops worldwide. The crop quality and production is largely hampered due to the fungal pathogen Alternaria solani causing necrotrophic foliage early blight disease. Crop plants usually respond to the biotic challenges with altered metabolic composition and physiological perturbations. We have deciphered altered metabolite composition, modulated metabolic pathways and identified metabolite biomarkers in A. solani-challenged susceptible tomato variety Kashi Aman using Liquid Chromatography-Mass Spectrometry (LC-MS) based metabolomics. Alteration in the metabolite feature composition of pathogen-challenged (m/z 9405) and non-challenged (m/z 9667) plant leaves including 8487 infection-exclusive and 8742 non-infection exclusive features was observed. Functional annotation revealed putatively annotated metabolites and pathway mapping indicated their enrichment in metabolic pathways, biosynthesis of secondary metabolites, ubiquinone and terpenoid-quinones, brassinosteroids, steroids, terpenoids, phenylpropanoids, carotenoids, oxy/sphingolipids and metabolism of biotin and porphyrin. PCA, multivariate PLS-DA and OPLS-DA analysis showed sample discrimination. Significantly up regulated 481 and down regulated 548 metabolite features were identified based on the fold change (threshold ≥ 2.0). OPLS-DA model based on variable importance in projection (VIP scores) and FC threshold (> 2.0) revealed 41 up regulated discriminant metabolite features annotated as sphingosine, fecosterol, melatonin, serotonin, glucose 6-phosphate, zeatin, dihydrozeatin and zeatin-β-D-glucoside. Similarly, 23 down regulated discriminant metabolites included histidinol, 4-aminobutyraldehyde, propanoate, tyramine and linalool. Melatonin and serotonin in the leaves were the two indoleamines being reported for the first time in tomato in response to the early blight pathogen. Receiver operating characteristic (ROC)-based biomarker analysis identified apigenin-7-glucoside, uridine, adenosyl-homocysteine, cGMP, tyrosine, pantothenic acid, riboflavin (as up regulated) and adenosine, homocyctine and azmaline (as down regulated) biomarkers. These results could aid in the development of metabolite-quantitative trait loci (mQTL). Furthermore, stress-induced biosynthetic pathways may be the potential targets for modifications through breeding programs or genetic engineering for improving crop performance in the fields.

Exploring Human Metabolome after Wine Intake-A Review

Wine has a rich history dating back to 2200 BC, originally recognized for its medicinal properties. Today, with the aid of advanced technologies like metabolomics and sophisticated analytical techniques, we have gained remarkable insights into the molecular-level changes induced by wine consumption in the human organism. This review embarks on a comprehensive exploration of the alterations in human metabolome associated with wine consumption. A great number of 51 studies from the last 25 years were reviewed; these studies systematically investigated shifts in metabolic profiles within blood, urine, and feces samples, encompassing both short-term and long-term studies of the consumption of wine and wine derivatives. Significant metabolic alterations were observed in a wide variety of metabolites belonging to different compound classes, such as phenolic compounds, lipids, organic acids, and amino acids, among others. Within these classes, both endogenous metabolites as well as diet-related metabolites that exhibited up-regulation or down-regulation following wine consumption were included. The up-regulation of short-chain fatty acids and the down-regulation of sphingomyelins after wine intake, as well as the up-regulation of gut microbial fermentation metabolites like vanillic and syringic acid are some of the most important findings reported in the reviewed literature. Our results confirm the intact passage of certain wine compounds, such as tartaric acid and other wine acids, to the human organism. In an era where the health effects of wine consumption are of growing interest, this review offers a holistic perspective on the metabolic underpinnings of this centuries-old tradition.

The Role of the Nuclear Receptor FXR in Arsenic-Induced Glucose Intolerance in Mice

Inorganic arsenic in drinking water is prioritized as a top environmental contaminant by the World Health Organization, with over 230 million people potentially being exposed. Arsenic toxicity has been well documented and is associated with a plethora of human diseases, including diabetes, as established in numerous animal and epidemiological studies. Our previous study revealed that arsenic exposure leads to the inhibition of nuclear receptors, including LXR/RXR. To this end, FXR is a nuclear receptor central to glucose and lipid metabolism. However, limited studies are available for understanding arsenic exposure-FXR interactions. Herein, we report that FXR knockout mice developed more profound glucose intolerance than wild-type mice upon arsenic exposure, supporting the regulatory role of FXR in arsenic-induced glucose intolerance. We further exposed mice to arsenic and tested if GW4064, a FXR agonist, could improve glucose intolerance and dysregulation of hepatic proteins and serum metabolites. Our data showed arsenic-induced glucose intolerance was remarkably diminished by GW4064, accompanied by a significant ratio of alleviation of dysregulation in hepatic proteins (83%) and annotated serum metabolites (58%). In particular, hepatic proteins "rescued" from arsenic toxicity by GW4064 featured members of glucose and lipid utilization. For instance, the expression of PCK1, a candidate gene for diabetes and obesity that facilitates gluconeogenesis, was repressed under arsenic exposure in the liver, but revived with the GW4064 supplement. Together, our comprehensive dataset indicates FXR plays a key role and may serve as a potential therapeutic for arsenic-induced metabolic disorders.

Precision fermentation with mass spectrometry-based spent media analysis

Optimization and monitoring of bioprocesses requires the measurement of several process parameters and quality attributes. Mass spectrometry (MS)-based techniques such as those coupled to gas chromatography (GCMS) and liquid Chromatography (LCMS) enable the simultaneous measurement of hundreds of metabolites with high sensitivity. When applied to spent media, such metabolome analysis can help determine the sequence of substrate uptake and metabolite secretion, consequently facilitating better design of initial media and feeding strategy. Furthermore, the analysis of metabolite diversity and abundance from spent media will aid the determination of metabolic phases of the culture and the identification of metabolites as surrogate markers for product titer and quality. This review covers the recent advances in metabolomics analysis applied to the development and monitoring of bioprocesses. In this regard, we recommend a stepwise workflow and guidelines that a bioprocesses engineer can adopt to develop and optimize a fermentation process using spent media analysis. Finally, we show examples of how the use of MS can revolutionize the design and monitoring of bioprocesses.

The metabolomic plasma profile of patients with Duchenne muscular dystrophy: providing new evidence for its pathogenesis

BACKGROUND

Duchenne muscular dystrophy (DMD) is a fatal genetic muscle-wasting disease that affects 1 in 5000 male births with no current cure. Despite great progress has been made in the research of DMD, its underlying pathological mechanism based on the metabolomics is still worthy of further study. Therefore, it is necessary to gain a deeper understanding of the mechanisms or pathogenesis underlying DMD, which may reveal potential therapeutic targets and/or biomarkers.

RESULTS

Plasma samples from 42 patients with DMD from a natural history study and 40 age-matched healthy volunteers were subjected to a liquid chromatography-mass spectrometry-based non-targeted metabolomics approach. Acquired metabolic data were evaluated by principal component analysis, partial least squares-discriminant analysis, and metabolic pathway analysis to explore distinctive metabolic patterns in patients with DMD. Differentially expressed metabolites were identified using publicly available and integrated databases. By comparing the DMD and healthy control groups, 25 differential metabolites were detected, including amino acids, unsaturated fatty acids, carnitine, lipids, and metabolites related to the gut microbiota. Correspondingly, linoleic acid metabolism, D-glutamine and D-glutamate metabolism, glycerophospholipid metabolism, and alanine, aspartate, and glutamate metabolism were significantly altered in patients with DMD, compared with those of healthy volunteers.

CONCLUSIONS

Our study demonstrated the abnormal metabolism of amino acids, energy, and lipids in patients with DMD, consistent with pathological features, such as recurrent muscle necrosis and regeneration, interstitial fibrosis, and fat replacement. Additionally, we found that metabolites of intestinal flora were disordered in DMD patients, providing support for treatment of intestinal microbia disturbance in DMD diseases. Our study provides a new research strategy for understanding the pathogenesis of DMD.

Recent Review on Selected Xenobiotics and Their Impacts on Gut Microbiome and Metabolome

As it is well known, the gut is one of the primary sites in any host for xenobiotics, and the many microbial metabolites responsible for the interactions between the gut microbiome and the host. However, there is a growing concern about the negative impacts on human health induced by toxic xenobiotics. Metabolomics, broadly including lipidomics, is an emerging approach to studying thousands of metabolites in parallel. In this review, we summarized recent advancements in mass spectrometry (MS) technologies in metabolomics. In addition, we reviewed recent applications of MS-based metabolomics for the investigation of toxic effects of xenobiotics on microbial and host metabolism. It was demonstrated that metabolomics, gut microbiome profiling, and their combination have a high potential to identify metabolic and microbial markers of xenobiotic exposure and determine its mechanism. Further, there is increasing evidence supporting that reprogramming the gut microbiome could be a promising approach to the intervention of xenobiotic toxicity.

Exploring the Microbiome in Human Reproductive Tract: High-Throughput Methods for the Taxonomic Characterization of Microorganisms

Microorganisms are important due to their widespread presence and multifaceted roles across various domains of life, ecology, and industries. In humans, they underlie the proper functioning of multiple systems crucial to well-being, including immunological and metabolic functions. Emerging research addressing the presence and roles of microorganisms within human reproduction is increasingly relevant. Studies implementing new methodologies (e.g., to investigate vaginal, uterine, and semen microenvironments) can now provide relevant insights into fertility, reproductive health, or pregnancy outcomes. In that sense, cutting-edge sequencing techniques, as well as others such as meta-metabolomics, culturomics, and meta-proteomics, are becoming more popular and accessible worldwide, allowing the characterization of microbiomes at unprecedented resolution. However, they frequently involve rather complex laboratory protocols and bioinformatics analyses, for which researchers may lack the required expertise. A suitable pipeline would successfully enable both taxonomic classification and functional profiling of the microbiome, providing easy-to-understand biological interpretations. However, the selection of an appropriate methodology would be crucial, as it directly impacts the reproducibility, accuracy, and quality of the results and observations. This review focuses on the different current microbiome-related techniques in the context of human reproduction, encompassing niches like vagina, endometrium, and seminal fluid. The most standard and reliable methods are 16S rRNA gene sequencing, metagenomics, and meta-transcriptomics, together with complementary approaches including meta-proteomics, meta-metabolomics, and culturomics. Finally, we also offer case examples and general recommendations about the most appropriate methods and workflows and discuss strengths and shortcomings for each technique.

Role of untargeted omics biomarkers of exposure and effect for tobacco research

Tobacco research remains a clear priority to improve individual and population health, and has recently become more complex with emerging combustible and noncombustible tobacco products. The use of omics methods in prevention and cessation studies are intended to identify new biomarkers for risk, compared risks related to other products and never use, and compliance for cessation and reinitation. to assess the relative effects of tobacco products to each other. They are important for the prediction of reinitiation of tobacco use and relapse prevention. In the research setting, both technical and clinical validation is required, which presents a number of complexities in the omics methodologies from biospecimen collection and sample preparation to data collection and analysis. When the results identify differences in omics features, networks or pathways, it is unclear if the results are toxic effects, a healthy response to a toxic exposure or neither. The use of surrogate biospecimens (e.g., urine, blood, sputum or nasal) may or may not reflect target organs such as the lung or bladder. This review describes the approaches for the use of omics in tobacco research and provides examples of prior studies, along with the strengths and limitations of the various methods. To date, there is little consistency in results, likely due to small number of studies, limitations in study size, the variability in the analytic platforms and bioinformatic pipelines, differences in biospecimen collection and/or human subject study design. Given the demonstrated value for the use of omics in clinical medicine, it is anticipated that the use in tobacco research will be similarly productive.

Untargeted MS-Based Metabolomic Analysis of Termite Gut-Associated Streptomycetes with Antifungal Activity against Pyrrhoderma noxium

Pyrrhoderma noxium is a plant fungal pathogen that induces the disease of brown root rot in a large variety of tree species. It is currently infecting many of the amenity trees within Brisbane City of Queensland, Australia. Steering away from harmful chemical fungicides, biological control agents offer environmentally friendly alternatives. Streptomycetes are known for their production of novel bioactive secondary metabolites with biocontrol potential, particularly, streptomycete symbionts isolated from unique ecological niches. In this study, 37 termite gut-associated actinomycete isolates were identified using molecular methods and screened against P. noxium. A majority of the isolates belonged to the genus Streptomyces, and 15 isolates exhibited strong antifungal activity with up to 98.5% mycelial inhibition of the fungal pathogen. MS/MS molecular networking analysis of the isolates' fermentation extracts revealed several chemical classes with polyketides being among the most abundant. Most of the metabolites, however, did not have matches to the GNPS database, indicating potential novel antifungal compounds in the active extracts obtained from the isolates. Pathway enrichment and overrepresentation analyses revealed pathways relating to polyketide antibiotic production, among other antibiotic pathways, further confirming the biosynthetic potential of the termite gut-associated streptomycetes with biocontrol potential against P. noxium.

Multi-omics in HIV: searching insights to understand immunological non-response in PLHIV

Antiretroviral therapy (ART) induces persistent suppression of HIV-1 replication and gradual recovery of T-cell counts, and consequently, morbidity and mortality from HIV-related illnesses have been significantly reduced. However, in approximately 30% of people living with HIV (PLHIV) on ART, CD4+ T-cell counts fail to normalize despite ART and complete suppression of HIV viral load, resulting in severe immune dysfunction, which may represent an increased risk of clinical progression to AIDS and non-AIDS events as well as increased mortality. These patients are referred to as "immune inadequate responders", "immunodiscordant responders" or "immune nonresponders (INR)". The molecular mechanisms underlying poor CD4+ T-cell recovery are still unclear. In this sense, the use of omics sciences has shed light on possible factors involved in the activity and metabolic dysregulation of immune cells during the failure of CD4+ T-cell recovery in INR. Moreover, identification of key molecules by omics approaches allows for the proposal of potential biomarkers or therapeutic targets to improve CD4+ T-cell recovery and the quality of life of these patients. Hence, this review aimed to summarize the information obtained through different omics concerning the molecular factors and pathways associated with the INR phenotype to better understand the complexity of this immunological status in HIV infection.

Integrative metabolomics science in Alzheimer's disease: Relevance and future perspectives

Alzheimer's disease (AD) is determined by various pathophysiological mechanisms starting 10-25 years before the onset of clinical symptoms. As multiple functionally interconnected molecular/cellular pathways appear disrupted in AD, the exploitation of high-throughput unbiased omics sciences is critical to elucidating the precise pathogenesis of AD. Among different omics, metabolomics is a fast-growing discipline allowing for the simultaneous detection and quantification of hundreds/thousands of perturbed metabolites in tissues or biofluids, reproducing the fluctuations of multiple networks affected by a disease. Here, we seek to critically depict the main metabolomics methodologies with the aim of identifying new potential AD biomarkers and further elucidating AD pathophysiological mechanisms. From a systems biology perspective, as metabolic alterations can occur before the development of clinical signs, metabolomics - coupled with existing accessible biomarkers used for AD screening and diagnosis - can support early disease diagnosis and help develop individualized treatment plans. Presently, the majority of metabolomic analyses emphasized that lipid metabolism is the most consistently altered pathway in AD pathogenesis. The possibility that metabolomics may reveal crucial steps in AD pathogenesis is undermined by the difficulty in discriminating between the causal or epiphenomenal or compensatory nature of metabolic findings.

A comparison of different machine-learning techniques for the selection of a panel of metabolites allowing early detection of brain tumors

Metabolomics combined with machine learning methods (MLMs), is a powerful tool for searching novel diagnostic panels. This study was intended to use targeted plasma metabolomics and advanced MLMs to develop strategies for diagnosing brain tumors. Measurement of 188 metabolites was performed on plasma samples collected from 95 patients with gliomas (grade I-IV), 70 with meningioma, and 71 healthy individuals as a control group. Four predictive models to diagnose glioma were prepared using 10 MLMs and a conventional approach. Based on the cross-validation results of the created models, the F1-scores were calculated, then obtained values were compared. Subsequently, the best algorithm was applied to perform five comparisons involving gliomas, meningiomas, and controls. The best results were obtained using the newly developed hybrid evolutionary heterogeneous decision tree (EvoHDTree) algorithm, which was validated using Leave-One-Out Cross-Validation, resulting in an F1-score for all comparisons in the range of 0.476-0.948 and the area under the ROC curves ranging from 0.660 to 0.873. Brain tumor diagnostic panels were constructed with unique metabolites, which reduces the likelihood of misdiagnosis. This study proposes a novel interdisciplinary method for brain tumor diagnosis based on metabolomics and EvoHDTree, exhibiting significant predictive coefficients.

Statistical methods and resources for biomarker discovery using metabolomics

Metabolomics is a dynamic tool for elucidating biochemical changes in human health and disease. Metabolic profiles provide a close insight into physiological states and are highly volatile to genetic and environmental perturbations. Variation in metabolic profiles can inform mechanisms of pathology, providing potential biomarkers for diagnosis and assessment of the risk of contracting a disease. With the advancement of high-throughput technologies, large-scale metabolomics data sources have become abundant. As such, careful statistical analysis of intricate metabolomics data is essential for deriving relevant and robust results that can be deployed in real-life clinical settings. Multiple tools have been developed for both data analysis and interpretations. In this review, we survey statistical approaches and corresponding statistical tools that are available for discovery of biomarkers using metabolomics.

Ultra-high performance liquid chromatography high-resolution mass spectrometry for metabolomic analysis of dental calculus from Duke Alessandro Farnese and Maria D'Aviz

Dental calculus is a valuable resource for the reconstruction of dietary habits and oral microbiome of past populations. In 2020 the remains of Duke Alessandro Farnese and his wife Maria D'Aviz were exhumed to get novel insights into the causes of death. This study aimed to investigate the dental calculus metabolome of the noble couple by untargeted metabolomics. The pulverized samples were decalcified in a water-formic acid mixture, extracted using methanol/acetonitrile and analyzed by ultra-high performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS) using a reversed-phase separation followed by electrospray ionization and full scan in positive and negative ion mode. Waters Synapt-G2-Si High-Definition hybrid quadrupole time-of-flight mass spectrometer was used. Significant features were then identified using MS^E acquisition mode, recording information on exact mass precursor and fragment ions within the same run. This approach, together with data pre-treatment and multivariate statistical analysis allowed for the identification of compounds able to differentiate between the investigated samples. More than 200 metabolites were identified, being fatty acids, alcohols, aldehydes, phosphatidylcholines, phosphatidylglycerols, ceramides and phosphatidylserines the most abundant classes. Metabolites deriving from food, bacteria and fungi were also determined, providing information on the habits and oral health status of the couple.

Volatile organic compounds for early detection of prostate cancer from urine

Prostate cancer (PCa) is one of the most common cancers in men worldwide. Early diagnosis of PCa is extremely challenging due to the lack of effective diagnostic methods. The study presented here aims to evaluate whether urine volatile organic compounds (VOCs) can be used as an emerging diagnostic biomarker for PCa. Gas chromatography-ion mobility spectrometry (GC-IMS) was used to detect VOCs in urine samples from 66 patients with PCa and to comparatively analyze samples from 87 patients with non-cancerous controls (NCs). A total of 86 substance peak heights were detected in urine samples from all patients. Analysis using four machine learning algorithms suggested that the diagnosis of PCa could be effectively facilitated. Ultimately, diagnostic models were constructed based on the four VOCs selected. The AUC for the RF and SVM model were 0.955 and 0.981, respectively. Both the NN and DT diagnostic models also achieved an AUC of 0.8 or more, but their sensitivity or specificity was poor compared to the RF and SVM models.

Untargeted Metabolomics Analysis for Studying Differences in High-Quality Colombian Cocoa Beans

Colombia is a producer of fine cocoa, according to the International Cocoa Organization; however, most of its exports are in the ordinary cocoa category. To remedy this situation, several national organizations are working to create technological platforms for small producers to certify the quality of their beans. The objective of this study was to identify differential chemical markers in 36 cocoa bean samples from five Colombian departments and associate them with cocoa quality properties. For this purpose, a non-targeted metabolomics approach was performed using UHPLC-HRMS, along with sensory and physicochemical analyses. The 36 samples did not differ in sensory quality, polyphenol content, and theobromine/caffeine ratio. However, the multivariate statistical analysis allowed us to differentiate the samples into four clusters. In addition, a similar grouping of the samples was also observed in the physical analyses. The metabolites responsible for such clustering were investigated with univariate statistical analysis and presumptively identified by comparison of experimental mass spectra with those reported in databases. Alkaloids, flavonoids, terpenoids, peptides, quinolines, and sulfur compounds were identified as discriminants between sample groups. Here, it was presented the metabolic profiles as an important chemical feature for further studies in quality control and more specific characterization of fine cocoa.

3-tert-Butyl-4-hydroxyanisole perturbs renal lipid metabolism in vitro by targeting androgen receptor-regulated de novo lipogenesis

The widespread usage of 3-tert-butyl-4-hydroxyanisole (3-BHA) as an anthropogenic antioxidant has caused considerable environmental contamination and frequent detection in diverse human-derived samples. 3-BHA can promote adipogenesis and impair hepatic lipid metabolism, while its effects on renal lipid homeostasis remain to be uncertain. Herein, using the human kidney 2 (HK-2) cell experiments, 3-BHA was found to cause a significant reduction in lipid accumulation of the HK-2 cells in both exposure concentration- and duration-dependent manners. Exposure to 3-BHA lowered the transcriptional expressions of sterol regulatory element-binding protein 1 (SREBP1) and acetyl-CoA carboxylase (ACC), as well as ACC activity, indicating the inhibition in the process of de novo lipogenesis in HK-2 cells. On this basis, the mechanism study suggested that the reduced glucose absorption and accelerated glycolysis were concomitantly involved. The antagonism of 3-BHA on the transactivation of androgen receptor (AR) contributed to the lowered de novo lipogenesis and the consequent intracellular lipid reduction. The metabolomics data further confirmed the imbalance of lipid homeostasis and dysregulation of de novo lipogenesis. The new findings on the impaired renal lipid metabolism induced by 3-BHA warranted proper care about the usage of this chemical as a food additive.

Measuring the effect of climate change in Antarctic microbial communities: toward novel experimental approaches

The Antarctic continent is undergoing a rapid warming, affecting microbial communities throughout its ecosystems. This continent is a natural laboratory for studying the effect of climate change, however, assessing the microbial communities' responses to environmental changes is challenging from a methodological point of view. We suggest novel experimental designs, including multivariable assessments that apply multiomics methods in combination with continuous environmental data recording and new warming simulation systems. Moreover, we propose that climate change studies in Antarctica should consider three main objectives, including descriptive studies, short-term temporary adaptation studies, and long-term adaptive evolution studies. This will help us to understand and manage the effects of climate change on the Earth.

To metabolomics and beyond: a technological portfolio to investigate cancer metabolism

Tumour cells have exquisite flexibility in reprogramming their metabolism in order to support tumour initiation, progression, metastasis and resistance to therapies. These reprogrammed activities include a complete rewiring of the bioenergetic, biosynthetic and redox status to sustain the increased energetic demand of the cells. Over the last decades, the cancer metabolism field has seen an explosion of new biochemical technologies giving more tools than ever before to navigate this complexity. Within a cell or a tissue, the metabolites constitute the direct signature of the molecular phenotype and thus their profiling has concrete clinical applications in oncology. Metabolomics and fluxomics, are key technological approaches that mainly revolutionized the field enabling researchers to have both a qualitative and mechanistic model of the biochemical activities in cancer. Furthermore, the upgrade from bulk to single-cell analysis technologies provided unprecedented opportunity to investigate cancer biology at cellular resolution allowing an in depth quantitative analysis of complex and heterogenous diseases. More recently, the advent of functional genomic screening allowed the identification of molecular pathways, cellular processes, biomarkers and novel therapeutic targets that in concert with other technologies allow patient stratification and identification of new treatment regimens. This review is intended to be a guide for researchers to cancer metabolism, highlighting current and emerging technologies, emphasizing advantages, disadvantages and applications with the potential of leading the development of innovative anti-cancer therapies.

Controlling the confounding effect of metabolic gene expression to identify actual metabolite targets in microsatellite instability cancers

BACKGROUND

The metabolome is the best representation of cancer phenotypes. Gene expression can be considered a confounding covariate affecting metabolite levels. Data integration across metabolomics and genomics to establish the biological relevance of cancer metabolism is challenging. This study aimed to eliminate the confounding effect of metabolic gene expression to reflect actual metabolite levels in microsatellite instability (MSI) cancers.

METHODS

In this study, we propose a new strategy using covariate-adjusted tensor classification in high dimensions (CATCH) models to integrate metabolite and metabolic gene expression data to classify MSI and microsatellite stability (MSS) cancers. We used datasets from the Cancer Cell Line Encyclopedia (CCLE) phase II project and treated metabolomic data as tensor predictors and data on gene expression of metabolic enzymes as confounding covariates.

RESULTS

The CATCH model performed well, with high accuracy (0.82), sensitivity (0.66), specificity (0.88), precision (0.65), and F1 score (0.65). Seven metabolite features adjusted for metabolic gene expression, namely, 3-phosphoglycerate, 6-phosphogluconate, cholesterol ester, lysophosphatidylethanolamine (LPE), phosphatidylcholine, reduced glutathione, and sarcosine, were found in MSI cancers. Only one metabolite, Hippurate, was present in MSS cancers. The gene expression of phosphofructokinase 1 (PFKP), which is involved in the glycolytic pathway, was related to 3-phosphoglycerate. ALDH4A1 and GPT2 were associated with sarcosine. LPE was associated with the expression of CHPT1, which is involved in lipid metabolism. The glycolysis, nucleotide, glutamate, and lipid metabolic pathways were enriched in MSI cancers.

CONCLUSIONS

We propose an effective CATCH model for predicting MSI cancer status. By controlling the confounding effect of metabolic gene expression, we identified cancer metabolic biomarkers and therapeutic targets. In addition, we provided the possible biology and genetics of MSI cancer metabolism.

Serum metabolomic profiles in BALB/c mice induced by Babesia microti infection

Introduction

The protozoan parasite Babesia microti is the primary cause of human babesiosis. This parasite invades and multiplies inside red blood cells (RBCs), and infections differ significantly based on the age and immune competency of the host. The aim of this study was to investigate the use of serum metabolic profiling to identify systemic metabolic variations between B. microti-infected mice and noninfected controls.

Methods

A serum metabolomics analysis of BALB/c mice that had been intraperitoneally injected with 10⁷ B. microti-infected RBCs was performed. Serum samples from the early infected group (2 days postinfection), the acutely infected group (9 days postinfection), and the noninfected group were collected and evaluated using a liquid chromatography-mass spectrometry (LC-MS) platform. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA) identified metabolomic profiles that differentiated the B. microti-infected and noninfected groups.

Results

Our results confirm that the serum metabolome is significantly influenced by acute B. microti infection and show that infection results in dysregulation of metabolic pathways and perturbation of metabolites. Acutely infected mice displayed perturbations in metabolites associated with taurine and hypotaurine metabolism, histidine metabolism, and arachidonic acid metabolism. Taurocholic acid, anserine, and arachidonic acid may be potential candidates as serological biomarkers for diagnosing B. microti infection at the acute stage. These metabolites could be further examined for their role in disease complexity.

Discussion

Our findings demonstrate that the acute stage of B. microti infection induces abnormalities in the metabolites present in mouse serum and provide new insight into the mechanisms involved in systemic metabolic changes that occur during B. microti infection.