Metabolic analysis as a driver for discovery, diagnosis, and therapy

A metabolomics pipeline highlights microbial metabolism in bloodstream infections

The growth of antimicrobial resistance (AMR) highlights an urgent need to identify bacterial pathogenic functions that may be targets for clinical intervention. Although severe infections profoundly alter host metabolism, prior studies have largely ignored microbial metabolism in this context. Here, we describe an iterative, comparative metabolomics pipeline to uncover microbial metabolic features in the complex setting of a host and apply it to investigate gram-negative bloodstream infection (BSI) in patients. We find elevated levels of bacterially derived acetylated polyamines during BSI and discover the enzyme responsible for their production (SpeG). Blocking SpeG activity reduces bacterial proliferation and slows pathogenesis. Reduction of SpeG activity also enhances bacterial membrane permeability and increases intracellular antibiotic accumulation, allowing us to overcome AMR in culture and in vivo. This study highlights how tools to study pathogen metabolism in the natural context of infection can reveal and prioritize therapeutic strategies for addressing challenging infections.

Homogentisic acid metabolism inhibits papillary thyroid carcinoma proliferation through ROS and p21-induced cell cycle arrest

Thyroid cancer is one of the most common primary endocrine malignancies worldwide, and papillary thyroid carcinoma (PTC) is the predominant histological type observed therein. Although PTC has been studied extensively, our understanding of the altered metabolism and metabolic profile of PTC tumors is limited. We identified that the content of metabolite homogentisic acid (HGA) in PTC tissues was lower than that in adjacent non-cancerous tissues. We evaluated the potential of HGA as a novel molecular marker in the diagnosis of PTC tumors, as well as its ability to indicate the degree of malignancy. Studies have further shown that HGA contributes to reactive oxygen species (ROS) associated oxidative stress, leading to toxicity and inhibition of proliferation. In addition, HGA caused an increase in p21 expression levels in PTC cells and induced G1 arrest. Moreover, we found that the low HGA content in PTC tumors was due to the low expression levels of tyrosine aminotransferase (TAT) and p-hydroxyphenylpyruvate hydroxylase (HPD), which catalyze the conversion of tyrosine to HGA. The low expression levels of TAT and HPD are strongly associated with a higher probability of PTC tumor invasion and metastasis. Our study demonstrates that HGA could be used to diagnose PTC and provides mechanisms linking altered HGA levels to the biological behavior of PTC tumors.

Stable Isotope Tracing Analysis in Cancer Research: Advancements and Challenges in Identifying Dysregulated Cancer Metabolism and Treatment Strategies

Metabolic reprogramming is a hallmark of cancer, driving the development of therapies targeting cancer metabolism. Stable isotope tracing has emerged as a widely adopted tool for monitoring cancer metabolism both in vitro and in vivo. Advances in instrumentation and the development of new tracers, metabolite databases, and data analysis tools have expanded the scope of cancer metabolism studies across these scales. In this review, we explore the latest advancements in metabolic analysis, spanning from experimental design in stable isotope-labeling metabolomics to sophisticated data analysis techniques. We highlight successful applications in cancer research, particularly focusing on ongoing clinical trials utilizing stable isotope tracing to characterize disease progression, treatment responses, and potential mechanisms of resistance to anticancer therapies. Furthermore, we outline key challenges and discuss potential strategies to address them, aiming to enhance our understanding of the biochemical basis of cancer metabolism.

Engineering focusing on cancer

While cancer research and care have benefited from revolutionary advances in the ability to manipulate and study living systems, the field is limited by a lack of synergy to leverage the power of engineering approaches. Cancer engineering is an emerging subfield of biomedical engineering that unifies engineering and cancer biology to better understand, diagnose, and treat cancer. We highlight cancer engineering's unique challenges, the importance of creating dedicated centers and departments that enable translational collaboration, and educational approaches to arm a new generation of scientists with engineering expertise and a fundamental understanding of cancer biology to transform clinical cancer care.

Metabolomics for Clinical Biomarker Discovery and Therapeutic Target Identification

As links between genotype and phenotype, small-molecule metabolites are attractive biomarkers for disease diagnosis, prognosis, classification, drug screening and treatment, insight into understanding disease pathology and identifying potential targets. Metabolomics technology is crucial for discovering targets of small-molecule metabolites involved in disease phenotype. Mass spectrometry-based metabolomics has implemented in applications in various fields including target discovery, explanation of disease mechanisms and compound screening. It is used to analyze the physiological or pathological states of the organism by investigating the changes in endogenous small-molecule metabolites and associated metabolism from complex metabolic pathways in biological samples. The present review provides a critical update of high-throughput functional metabolomics techniques and diverse applications, and recommends the use of mass spectrometry-based metabolomics for discovering small-molecule metabolite signatures that provide valuable insights into metabolic targets. We also recommend using mass spectrometry-based metabolomics as a powerful tool for identifying and understanding metabolic patterns, metabolic targets and for efficacy evaluation of herbal medicine.

Unveiling the methionine cycle: a key metabolic signature and NR4A2 as a methionine-responsive oncogene in esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is a deadly malignancy with notable metabolic reprogramming, yet the pivotal metabolic feature driving ESCC progression remains elusive. Here, we show that methionine cycle exhibits robust activation in ESCC and is reversely associated with patient survival. ESCC cells readily harness exogenous methionine to generate S-adenosyl-methionine (SAM), thus promoting cell proliferation. Mechanistically, methionine augments METTL3-mediated RNA m6A methylation through SAM and revises gene expression. Integrative omics analysis highlights the potent influence of methionine/SAM on NR4A2 expression in a tumor-specific manner, mediated by the IGF2BP2-dependent stabilization of methylated NR4A2 mRNA. We demonstrate that NR4A2 facilitates ESCC growth and negatively impacts patient survival. We further identify celecoxib as an effective inhibitor of NR4A2, offering promise as a new anti-ESCC agent. In summary, our findings underscore the active methionine cycle as a critical metabolic characteristic in ESCC, and pinpoint NR4A2 as a novel methionine-responsive oncogene, thereby presenting a compelling target potentially superior to methionine restriction.

HCMMD: systematic evaluation of metabolites in body fluids as liquid biopsy biomarker for human cancers

Metabolomics is a rapidly expanding field in systems biology used to measure alterations of metabolites and identify metabolic biomarkers in response to disease processes. The discovery of metabolic biomarkers can improve early diagnosis, prognostic prediction, and therapeutic intervention for cancers. However, there are currently no databases that provide a comprehensive evaluation of the relationship between metabolites and cancer processes. In this review, we summarize reported metabolites in body fluids across pan-cancers and characterize their clinical applications in liquid biopsy. We conducted a search for metabolic biomarkers using the keywords ("metabolomics" OR "metabolite") AND "cancer" in PubMed. Of the 22,254 articles retrieved, 792 were deemed potentially relevant for further review. Ultimately, we included data from 573,300 samples and 17,083 metabolic biomarkers. We collected information on cancer types, sample size, the human metabolome database (HMDB) ID, metabolic pathway, area under the curve (AUC), sensitivity and specificity of metabolites, sample source, detection method, and clinical features were collected. Finally, we developed a user-friendly online database, the Human Cancer Metabolic Markers Database (HCMMD), which allows users to query, browse, and download metabolite information. In conclusion, HCMMD provides an important resource to assist researchers in reviewing metabolic biomarkers for diagnosis and progression of cancers.

Metabolic difference between patient-derived xenograft model of pancreatic ductal adenocarcinoma and corresponding primary tumor

BACKGROUND

Patients-derived xenograft (PDX) model have been widely used for tumor biological and pathological studies. However, the metabolic similarity of PDX tumor to the primary cancer (PC) is still unknown.

METHODS

In present study, we established PDX model by engrafting primary tumor of pancreatic ductal adenocarcinoma (PDAC), and then compared the tumor metabolomics of PC, the first generation of PDX tumor (PDXG1), and the third generation of PDX tumor (PDXG3) by using 1H NMR spectroscopy. Then, we assessed the differences in response to chemotherapy between PDXG1 and PDXG3 and corresponding metabolomic differences in drug-resistant tumor tissues. To evaluate the metabolomic similarity of PDX to PC, we also compared the metabolomic difference of cell-derived xenograft (CDX) vs. PC and PDX vs. PC.

RESULTS

After engraftment, PDXG1 tumor had a low level of lactate, pyruvate, citrate and multiple amino acids (AAs) compared with PC. Metabolite sets enrichment and metabolic pathway analyses implied that glycolysis metabolisms were suppressed in PDXG1 tumor, and tricarboxylic acid cycle (TCA)-associated anaplerosis pathways, such as amino acids metabolisms, were enhanced. Then, after multiple passages of PDX, the altered glycolysis and TCA-associated anaplerosis pathways were partially recovered. Although no significant difference was observed in the response of PDXG1 and PDXG3 to chemotherapy, the difference in glycolysis and amino acids metabolism between PDXG1 and PDXG3 could still be maintained. In addition, the metabolomic difference between PC and CDX models were much larger than that of PDX model and PC, indicating that PDX model still retain more metabolic characteristics of primary tumor which is more suitable for tumor-associated metabolism research.

CONCLUSIONS

Compared with primary tumor, PDX models have obvious difference in metabolomic level. These findings can help us design in vivo tumor metabolomics research legitimately and analyze the underlying mechanism of tumor metabolic biology thoughtfully.

Plasma metabolomic analysis reveals the metabolic characteristics and potential diagnostic biomarkers of spinal tuberculosis

Objectives

This study aimed to conduct a non-targeted metabolomic analysis of plasma from patients with spinal tuberculosis (STB) to systematically elucidate the metabolomic alterations associated with STB, and explore potential diagnostic biomarkers for STB.

Methods

From January 2020 to January 2022, 30 patients with spinal tuberculosis (STBs) clinically diagnosed at the General Hospital of Ningxia Medical University and 30 age- and sex-matched healthy controls (HCs) were selected for this study. Using ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS) based metabolomics, we analyzed the metabolic profiles of 60 plasma samples. Statistical analyses, pathway enrichment, and receiver operating characteristic (ROC) analyses were performed to screen and evaluate potential diagnostic biomarkers.

Results

Metabolomic profiling revealed distinct alterations between the STBs and HCs cohorts. A total of 1635 differential metabolites were screened, functionally clustered, and annotated. The results showed that the differential metabolites were enriched in sphingolipid metabolism, tuberculosis, cutin, suberine and wax biosynthesis, beta-alanine metabolism, methane metabolism, and other pathways. Through the random forest algorithm, LysoPE (18:1(11Z)/0:0), 8-Demethyl-8-formylriboflavin 5'-phosphate, Glutaminyl-Gamma-glutamate, (2R)-O-Phospho-3-sulfolactate, and LysoPE (P-16:0/0:0) were determined to have high independent diagnostic value.

Conclusions

STBs exhibited significantly altered metabolite profiles compared with HCs. Here, we provide a global metabolomic profile and identify potential diagnostic biomarkers of STB. Five potential independent diagnostic biomarkers with high diagnostic value were screened. This study provides novel insights into the pathogenesis, diagnosis, and treatment strategies of STB.

A preliminary metabolomics study of the database for biological samples of schizophrenia among Chinese ethnic minorities

BACKGROUND

Schizophrenia (SCZ) is a profound mental disorder with a multifactorial etiology, including genetics, environmental factors, and demographic influences such as ethnicity and geography. Among these, the studies of SCZ also shows racial and regional differences.

METHODS

We first established a database of biological samples for SCZ in China's ethnic minorities, followed by a serum metabolomic analysis of SCZ patients from various ethnic groups within the same region using the LC-HRMS platform.

RESULTS

Analysis identified 47 metabolites associated with SCZ, with 46 showing significant differences between Miao and Han SCZ patients. These metabolites, primarily fatty acids, amino acids, benzene, and derivatives, are involved in fatty acid metabolism pathways. Notably, L-Carnitine, L-Cystine, Aspartylphenylalanine, and Methionine sulfoxide demonstrated greater diagnostic efficacy in Miao SCZ patients compared to Han SCZ patients.

CONCLUSION

Preliminary findings suggest that there are differences in metabolic levels among SCZ patients of different ethnicities in the same region, offering insights for developing objective diagnostic or therapeutic monitoring strategies that incorporate ethnic considerations of SCZ.

Recent Progress in Mass Spectrometry-Based Single-Cell Metabolic Analysis

Single-cell analysis enables the measurement of biomolecules at the level of individual cells, facilitating in-depth investigations into cellular heterogeneity and precise interpretation of the related biological mechanisms. Among these biomolecules, cellular metabolites exhibit remarkable sensitivity to environmental and biochemical changes, unveiling a hidden world underlying cellular heterogeneity and allowing for the determination of cell physiological states. However, the metabolic analysis of single cells is challenging due to the extremely low concentrations, substantial content variations, and rapid turnover rates of cellular metabolites. Mass spectrometry (MS), characterized by its high sensitivity, wide dynamic range, and excellent selectivity, is employed in single-cell metabolic analysis. This review focuses on recent advances and applications of MS-based single-cell metabolic analysis, encompassing three key steps of single-cell isolation, detection, and application. It is anticipated that MS will bring profound implications in biomedical practices, serving as advanced tools to depict the single-cell metabolic landscape.

Distinct mechanisms by which Nrf1 and Nrf2 as drug targets contribute to the anticancer efficacy of cisplatin on hepatoma cells

Cisplatin (cis-Dichlorodiamineplatinum[II], CDDP) is generally accepted as a platinum-based alkylating agent type of the DNA-damaging anticancer drug, which is widely administrated in clinical treatment of many solid tumors. The pharmacological effect of CDDP is mainly achieved by replacing the chloride ion (Cl-) in its structure with H2O to form active substances with the strong electrophilic properties and then react with any nucleophilic molecules, primarily leading to genomic DNA damage and subsequent cell death. In this process, those target genes driven by the consensus electrophilic and/or antioxidant response elements (EpREs/AREs) in their promoter regions are also activated or repressed by CDDP. Thereby, we here examined the expression profiling of such genes regulated by two principal antioxidant transcription factors Nrf1 and Nrf2 (both encoded by Nfe2l1 and Nfe2l2, respectively) in diverse cellular signaling responses to this intervention. The results demonstrated distinct cellular metabolisms, molecular pathways and signaling response mechanisms by which Nrf1 and Nrf2 as the drug targets differentially contribute to the anticancer efficacy of CDDP on hepatoma cells and xenograft tumor mice. Interestingly, the role of Nrf1, rather than Nrf2, is required for the anticancer effect of CDDP, to suppress malignant behavior of HepG2 cells by differentially monitoring multi-hierarchical signaling to gene regulatory networks. To our surprise, it was found there exists a closer relationship of Nrf1α than Nrf2 with DNA repair, but the hyperactive Nrf2 in Nrf1α-∕- cells manifests a strong correlation with its resistance to CDDP, albeit their mechanistic details remain elusive.

Quantification of metabolic activity from isotope tracing data using automated methodology

Isotope tracing is a widely used technique to study metabolic activities by introducing heavy labeled nutrients into living cells and organisms. However, interpreting isotope tracing data is often heuristic, and application of automated methods using artificial intelligence is limited due to the paucity of evaluative knowledge. Our study developed a new pipeline that efficiently predicts metabolic activity in expansive metabolic networks and systematically quantifies flux uncertainty of traditional computational methods. We further developed an algorithm adept at significantly reducing this uncertainty, enabling robust evaluations of metabolic activity with limited data. Using this technology, we discovered highly reprogrammed mitochondria-cytosol exchange cycles in tumor tissue of patients, and observed similar metabolic patterns influenced by nutritional conditions in cancer cells. Thus, our refined methodology provides robust automated quantification of metabolism allowing for new insight into metabolic network activity.

Cerebrospinal Fluid Metabolomic Pattern of Different Pituitary Stalk Lesions

OBJECTIVE

To describe the cerebrospinal fluid (CSF) metabolomic pattern of pituitary stalk lesions.

METHODS

CSF was collected from patients with different pituitary stalk lesions treated at Peking Union Medical College Hospital: germ cell tumor (GCT, n = 27); hypophysitis (n = 10); and Langerhans cell histiocytosis (LCH) or Erdheim-Chester disease (ECD) (LCH + ECD, n = 10). The CSF metabolome profiles were characterized by liquid chromatography-mass spectrometry (LC-MS).

RESULTS

There were 44 metabolites that significantly differed between patients with GCT and those with hypophysitis (P < .05). Between patients with GCT with CSF level of beta subunit of human chorionic gonadotrophin (β-hCG) < 5 mIU/mL and those with hypophysitis, there were 15 differential metabolites (P < .05, fold change > 1.5 or < 1/1.5). All of the metabolites had an area under the curve (AUC) above 0.7. There were 9 metabolites that significantly differed between patients with GCT and those with LCH + ECD (P < .05) and 7 metabolites had significant differences between GCT (CSF β-hCG < 5 mIU/mL) and LCH + ECD (P < .05, fold change > 1.5 or < 1/1.5). We found 6 metabolites that were significantly different between patients with hypophysitis and those with LCH + ECD (P < .05) and 5 of these had fold change more than 1.5 or less than 1/1.5. Three metabolites, 5-deoxydiplosporin, cloversaponin I, and phytosphingosine, showed excellent capabilities to differentiate the 3 disease categories. Furthermore, we identified 67 metabolites associated with clinical test results (ρ > 0.2, P < .05) and 29 metabolites showed strong correlation (ρ > 0.4, P < .05).

CONCLUSION

Our study is the first to systematically investigate the metabolomics of CSF in different pituitary stalk lesions. CSF metabolomics is a useful strategy for biomarker discovery.

Modelling cancer metabolism in vitro: current improvements and future challenges

Advances in cancer biology over the past decades have revealed that metabolic adaptation of cancer cells is an essential aspect of tumorigenesis. However, recent insights into tumour metabolism in vivo have revealed dissimilarities with results obtained in vitro. This is partly due to the reductionism of in vitro cancer models that struggle to reproduce the complexity of tumour tissues. This review describes some of the discrepancies in cancer cell metabolism between in vitro and in vivo conditions, and presents current methodological approaches and tools used to bridge the gap with the clinically relevant microenvironment. As such, these approaches should generate new knowledge that could be more effectively translated into therapeutic opportunities.

Urine and serum metabolomic analysis of endometrial cancer diagnosis and classification based on ultra-performance liquid chromatography mass spectrometry

OBJECTIVE

This study aimed to reveal the urinary and serum metabolic pattern of endometrial cancer (EC) and establish diagnostic models to identify EC from controls, high-risk from low-risk EC, and type II from type I EC.

METHOD

This study included 146 EC patients (comprising 79 low-risk and 67 high-risk patients, including 124 type I and 22 type II) and 59 controls. The serum and urine samples were analyzed using ultraperformance liquid chromatography mass spectrometry. Analysis was used to elucidate the distinct metabolites and altered metabolic pathways. Receiver operating characteristic (ROC) analyses were employed to discover and validate the potential biomarker models.

RESULTS

Serum and urine metabolomes displayed significant differences between EC and controls, with metabolites related to amino acid and nicotinamide metabolisms. The serum and urine panels distinguished these two groups with Area Under the Curve (AUC) of 0.821 and 0.902, respectively. The panel consisting of serum and urine metabolites demonstrated the best predictive ability (AUC = 0.953 and 0.976 in discovering and validation group). In comparing high-risk and low risk EC, differential metabolites were enriched in purine and glutamine metabolism. The AUC values for serum and urine panels were 0.818, and 0.843, respectively. The combined panel exhibited better predictive accuracy (0.881 in discovering group and 0.936 in external validation). In the comparison between type I and type II group, altered folic acid metabolism was identified. The serum, urine and combined panels discriminated these two groups with the AUC of 0.829, 0.913 and 0.922, respectively.

CONCLUSION

The combined urine and serum metabolome effectively revealed the metabolic patterns in EC patients, offering valuable diagnostic models for EC diagnosis and classification.

Potential roles of HSYA in attenuating sepsis-induced liver injury through multi-omics analysis

Liver injury is a strong independent predictor of mortality in patients with sepsis, in which gut dysbiosis plays a crucial role. Hydroxyl safflower yellow A (HSYA), an important component of safflower, has been used to treat liver injury in animal models. However, its role in sepsis-induced liver dysfunction and the specific molecular mechanisms remain unclear. In the current study, we first discussed the discrepancy in the gut microbiota between the cecal ligation puncture (CLP) and HSYA groups using 16 S RNA sequencing. Our data demonstrated that HSYA supplementation significantly decreased the relative abundance of Proteobacteria, Firmicutes, and Campylobacterota, and further decreased the abundance of Bacteroidota, suggesting that the protective effects of HSYA against sepsis-induced liver injury may be partially attributed to the alteration of these bacteria. In addition, the metabolomic data identified 823 differentially expressed metabolites associated with sepsis-induced liver injury. After HSYA supplementation, the levels of 56 metabolites were restored to sham-like levels. Transcriptomic analysis revealed 4990 differentially expressed genes (DEGs) between the sham and CLP groups, and after HSYA injection, 1613 genes were modulated. Comprehensive analysis demonstrated that the enrichment pathways of the 903 DEGs mainly focused on inflammatory responses, amino acid metabolism, and Lipid reactions. In conclusion, our study revealed the potential mechanism of action of HSYA in sepsis-induced liver injury through a comprehensive analysis of 16 S RNA sequencing, metabolomics, and transcriptomics, thus providing a theoretical basis for further clinical applications of HSYA.

Development of a single mobile phase for LC-IM-MS-based discovery lipidomics and metabolic phenotyping: Application to methapyrilene hepatotoxicity in the rat

The untargeted global profiling of endogenous metabolites and lipids has the potential to increase knowledge and understanding in many areas of biology. LC-MS/MS is a key technology for such analyses however, several different LC methodologies, using different mobile phase compositions, are required to cover the diversity in polarity and analyte structure encountered in biological samples. Most notably many lipid screening methods make use of isopropanol (IPA) as a major component of mobile phases employed for comprehensive lipidomic profiling. In order to increase laboratory efficiency, and minimize opportunities for errors, a suite of methods, based on a single acetonitrile (ACN)-aqueous buffer mobile phase combination, has been developed. This mobile phase can be used for hydrophobic interaction liquid chromatography on an amide stationary phase (for polar analytes), reversed-phase (RP) LC analysis on a C8 stationary phase (for moderately polar-non-polar compounds) and RPLC using a CSH phenyl-hexyl bonded column (for lipids). All of these sub 10 minute separations had good throughput and reproducibility with CV's of analyte response <25 % whilst eliminating the need for complex mobile phase preparation and the use of IPA as an organic modifier for lipidomics. Advantages of removing IPA and replacing it with the ACN-based method were a 58 % increase in peak capacity for lipids, with improved resolution for the di- and triglycerides and cholesterol esters compared to current methods. Compared to the IPA-containing solvent system the ACN-based mobile phase also resulted in a 61 % increase in lipid feature detection. The utility of this "universal" mobile phase approach was demonstrated by its application to a rat toxicology study investigating the consequences of methapyrilene administration through on the endogenous metabolite profiles of plasma and urine. Methapyrilene and its metabolites were also profiled in these samples.

Standard operating procedure to reveal prostate cancer specific volatile organic molecules by infrared spectroscopy

The growing number of prostate cancer cases is a real concern in modern society. Over 1.4 million new cases and about 400 thousand (>26%) deaths were registered worldwide in 2020 due to prostate cancer. The high mortality rate of prostate cancer is due to the lack of reliable early detection of the disease. Till now the most reliable diagnosis of cancer is tissue biopsy, which is an invasive process. A non-invasive or minimally invasive technique could lead to a diagnostic tool that will allow for saving or prolonging the lifespan of millions of lives. Metabolite-based diagnostics may have a better chance of early cancer detection. However, reliable detection techniques need to be developed. Infrared spectroscopy based gaseous-biofluid holds great promise towards the development of non-invasive diagnostics. A pilot study based on breath analysis by infrared spectroscopy showed promising results in distinguishing prostate cancer patients from healthy volunteers. Details of the spectral metabolic analysis are presented.

Inter-organ crosstalk during development and progression of type 2 diabetes mellitus

Type 2 diabetes mellitus (T2DM) is characterized by tissue-specific insulin resistance and pancreatic β-cell dysfunction, which result from the interplay of local abnormalities within different tissues and systemic dysregulation of tissue crosstalk. The main local mechanisms comprise metabolic (lipid) signalling, altered mitochondrial metabolism with oxidative stress, endoplasmic reticulum stress and local inflammation. While the role of endocrine dysregulation in T2DM pathogenesis is well established, other forms of inter-organ crosstalk deserve closer investigation to better understand the multifactorial transition from normoglycaemia to hyperglycaemia. This narrative Review addresses the impact of certain tissue-specific messenger systems, such as metabolites, peptides and proteins and microRNAs, their secretion patterns and possible alternative transport mechanisms, such as extracellular vesicles (exosomes). The focus is on the effects of these messengers on distant organs during the development of T2DM and progression to its complications. Starting from the adipose tissue as a major organ relevant to T2DM pathophysiology, the discussion is expanded to other key tissues, such as skeletal muscle, liver, the endocrine pancreas and the intestine. Subsequently, this Review also sheds light on the potential of multimarker panels derived from these biomarkers and related multi-omics for the prediction of risk and progression of T2DM, novel diabetes mellitus subtypes and/or endotypes and T2DM-related complications.

Metabolic pathway analysis using stable isotopes in patients with cancer

Metabolic reprogramming is central to malignant transformation and cancer cell growth. How tumours use nutrients and the relative rates of reprogrammed pathways are areas of intense investigation. Tumour metabolism is determined by a complex and incompletely defined combination of factors intrinsic and extrinsic to cancer cells. This complexity increases the value of assessing cancer metabolism in disease-relevant microenvironments, including in patients with cancer. Stable-isotope tracing is an informative, versatile method for probing tumour metabolism in vivo. It has been used extensively in preclinical models of cancer and, with increasing frequency, in patients with cancer. In this Review, we describe approaches for using in vivo isotope tracing to define fuel preferences and pathway engagement in tumours, along with some of the principles that have emerged from this work. Stable-isotope infusions reported so far have revealed that in humans, tumours use a diverse set of nutrients to supply central metabolic pathways, including the tricarboxylic acid cycle and amino acid synthesis. Emerging data suggest that some activities detected by stable-isotope tracing correlate with poor clinical outcomes and may drive cancer progression. We also discuss current challenges in isotope tracing, including comparisons of in vivo and in vitro models, and opportunities for future discovery in tumour metabolism.

Metabolic mechanisms in physiological and pathological cardiac hypertrophy: new paradigms and challenges

Cardiac metabolism is vital for heart function. Given that cardiac contraction requires a continuous supply of ATP in large quantities, the role of fuel metabolism in the heart has been mostly considered from the perspective of energy production. However, the consequence of metabolic remodelling in the failing heart is not limited to a compromised energy supply. The rewired metabolic network generates metabolites that can directly regulate signalling cascades, protein function, gene transcription and epigenetic modifications, thereby affecting the overall stress response of the heart. In addition, metabolic changes in both cardiomyocytes and non-cardiomyocytes contribute to the development of cardiac pathologies. In this Review, we first summarize how energy metabolism is altered in cardiac hypertrophy and heart failure of different aetiologies, followed by a discussion of emerging concepts in cardiac metabolic remodelling, that is, the non-energy-generating function of metabolism. We highlight challenges and open questions in these areas and finish with a brief perspective on how mechanistic research can be translated into therapies for heart failure.

Genomic medicine in neonatal care: progress and challenges

During the neonatal period, many genetic disorders present and contribute to neonatal morbidity and mortality. Genomic medicine-the use of genomic information in clinical care- has the potential to significantly reduce morbidity and mortality in the neonatal period and improve outcomes for this population. Diagnostic genomic testing for symptomatic newborns, especially rapid testing, has been shown to be feasible and have diagnostic and clinical utility, particularly in the short-term. Ongoing studies are assessing the feasibility and utility, including personal utility, of implementation in diverse populations. Genomic screening for asymptomatic newborns has also been studied, and the acceptability and feasibility of such an approach remains an active area of investigation. Emerging precision therapies, with examples even at the "n-of-1" level, highlight the promise of precision diagnostics to lead to early intervention and improve outcomes. To sustainably implement genomic medicine in neonatal care in an ethical, effective, and equitable manner, we need to ensure access to genetics and genomics knowledge, access to genomic tests, which is currently limited by payors, feasible processes for ordering these tests, and access to follow up in the clinical and research realms. Future studies will provide further insight into enablers and barriers to optimize implementation strategies.

Cyclin D1 extensively reprograms metabolism to support biosynthetic pathways in hepatocytes

Cell proliferation requires metabolic reprogramming to accommodate biosynthesis of new cell components, and similar alterations occur in cancer cells. However, the mechanisms linking the cell cycle machinery to metabolism are not well defined. Cyclin D1, along with its main partner cyclin-dependent kinase 4 (Cdk4), is a pivotal cell cycle regulator and driver oncogene that is overexpressed in many cancers. Here, we examine hepatocyte proliferation to define novel effects of cyclin D1 on biosynthetic metabolism. Metabolomic studies reveal that cyclin D1 broadly promotes biosynthetic pathways including glycolysis, the pentose phosphate pathway, and the purine and pyrimidine nucleotide synthesis in hepatocytes. Proteomic analyses demonstrate that overexpressed cyclin D1 binds to numerous metabolic enzymes including those involved in glycolysis and pyrimidine synthesis. In the glycolysis pathway, cyclin D1 activates aldolase and GAPDH, and these proteins are phosphorylated by cyclin D1/Cdk4 in vitro. De novo pyrimidine synthesis is particularly dependent on cyclin D1. Cyclin D1/Cdk4 phosphorylates the initial enzyme of this pathway, carbamoyl-phosphate synthetase 2, aspartate transcarbamylase, and dihydroorotase (CAD), and metabolomic analysis indicates that cyclin D1 depletion markedly reduces the activity of this enzyme. Pharmacologic inhibition of Cdk4 along with the downstream pyrimidine synthesis enzyme dihydroorotate dehydrogenase synergistically inhibits proliferation and survival of hepatocellular carcinoma cells. These studies demonstrate that cyclin D1 promotes a broad network of biosynthetic pathways in hepatocytes, and this model may provide insights into potential metabolic vulnerabilities in cancer cells.

Systemic perturbations in amino acids/amino acid derivatives and tryptophan pathway metabolites associated with murine influenza A virus infection

BACKGROUND

Influenza A virus (IAV) is the only influenza virus causing flu pandemics (i.e., global epidemics of flu disease). Influenza (the flu) is a highly contagious disease that can be deadly, especially in high-risk groups. Worldwide, these annual epidemics are estimated to result in about 3 to 5 million cases of severe illness and in about 290,000 to 650,000 respiratory deaths. We intend to reveal the effect of IAV infection on the host's metabolism, immune response, and neurotoxicity by using a mouse IAV infection model.

METHODS

51 metabolites of murine blood plasma (33 amino acids/amino acid derivatives (AADs) and 18 metabolites of the tryptophan pathway) were analyzed by using Ultra-High-Performance Liquid Chromatography-Mass Spectrometry with Electrospray Ionization at the acute (7 days post-infection (dpi)), resolution (14 dpi), and recovery (21 dpi) stages of the virus infection in comparison with controls.

RESULTS

Among the 33 biogenic amino acids/AADs, the levels of five amino acids/AADs (1-methylhistidine, 5-oxoproline, α-aminobutyric acid, glutamine, and taurine) increased by 7 dpi, whereas the levels of ten amino acids/AADs (4-hydroxyproline, alanine, arginine, asparagine, cysteine, citrulline, glycine, methionine, proline, and tyrosine) decreased. By 14 dpi, the levels of one AAD (3-methylhistidine) increased, whereas the levels of five amino acids/AADs (α-aminobutyric acid, aminoadipic acid, methionine, threonine, valine) decreased. Among the 18 metabolites from the tryptophan pathway, the levels of kynurenine, quinolinic acid, hydroxykynurenine increased by 7 dpi, whereas the levels of indole-3-acetic acid and nicotinamide riboside decreased.

CONCLUSIONS

Our data may facilitate understanding the molecular mechanisms of host responses to IAV infection and provide a basis for discovering potential new mechanistic, diagnostic, and prognostic biomarkers and therapeutic targets for IAV infection.

Gut microbiota combined with metabolome dissects long-term nanoplastics exposure-induced disturbed spermatogenesis

As the concerned emerging pollutants, several lines of evidence have indicated that nanoplastics (NPs) lead to reproductive toxicity. However, the biological mechanism underlying NPs disturbed spermatogenesis remains largely unknown. Therefore, we aimed to reveal the potential mechanism of impaired spermatogenesis caused by long-term NPs exposure from the perspective of integrated metabolome and microbiome analysis. After 12 weeks of gavage of polystyrene nanoplastics (PS-NPs) and animo-modified polystyrene nanoplastics (Amino-NPs), a well-designed two-exposure stages experimental condition. We found that NPs exposure induced apparent abnormal spermatogenesis, which appeared more severe in the Amino-NPs group. Mechanistically, 14 floras associated with glucose and lipid metabolism were significantly altered, as evidenced by 16 S rRNA sequencing. Testicular metabolome revealed that the Top 50 changed metabolites were also enriched in lipid metabolism. Subsequently, the combined gut microbiome and metabolome analysis uncovered the strong correlations between Klebsiella, Blautia, Parabacteroides, and lipid metabolites (e.g., PC, LysoPC and GPCho). We speculate that the dysbiosis of gut microbiota-related disturbed lipid metabolism may be responsible for long-term NPs-induced damaged spermatogenesis, which provides new insights into NPs-induced dysregulated spermatogenesis.

Urine metabolomics for assessing fertility-sparing treatment efficacy in endometrial cancer: a non-invasive approach using ultra-performance liquid chromatography mass spectrometry

OBJECTIVE

This study aimed to reveal the urine metabolic change of endometrial cancer (EC) patients during fertility-sparing treatment and establish non-invasive predictive models to identify patients with complete remission (CR).

METHOD

This study enrolled 20 EC patients prior to treatment (PT) and 22 patients with CR, aged 25-40 years. Eligibility criteria consisted of stage IA high-grade EC, lesions confined to endometrium, normal hepatic and renal function, normal urine test, no contraindication for fertility-sparing treatment and no prior therapy. Urine samples were analyzed using ultraperformance liquid chromatography mass spectrometry (UPLC-MS), a technique chosen for its high sensitivity and resolution, allows for rapid, accurate identification and quantification of metabolites, providing a comprehensive metabolic profile and facilitating the discovery of potential biomarkers. Analytical techniques were employed to determine distinct metabolites and altered metabolic pathways. The statistical analyses were performed using univariate and multivariate analyses, logistic regression and receiver operating characteristic (ROC) curves to discover and validate the potential biomarker models.

RESULTS

A total of 108 different urine metabolomes were identified between CR and PT groups. These metabolites were enriched in ascorbate and aldarate metabolism, one carbon pool by folate, and some amino acid metabolisms pathways. A panel consisting of Baicalin, 5beta-1,3,7 (11)-Eudesmatrien-8-one, Indolylacryloylglycine, Edulitine, and Physapubenolide were selected as biomarkers, which demonstrated the best predictive ability with the AUC values of 0.982/0.851 in training/10-fold-cross-validation group, achieving a sensitivity of 0.975 and specificity of 0.967, respectively.

CONCLUSION

The urine metabolic analysis revealed the metabolic changes in EC patients during the fertility-sparing treatment. The predictive biomarkers present great potential diagnostic value in fertility-sparing treatments for EC patients, offering a less invasive means of monitoring treatment efficacy. Further research should explore the mechanistic underpinnings of these metabolic changes and validate the biomarker panel in larger, diverse populations due to the small sample size and single-institution nature of our study.

LC-MS/MS based metabolomic analysis of serum from patients with cerebrovascular stenosis

Cerebrovascular stenosis (CVS) is the main cause of ischemic stroke, which greatly threatens human life. Hence, it's important to perform early screenings for CVS. Metabolomics is an emerging omics approach that has great advantages in disease screening and diagnosis. Therefore, we aim to elucidate the correlation between CVS and metabolomics, which can aid in conducting CVS screening at an early stage. Patients with CVS in Beijing Hospital were included in the study. A total of 36 participants, including 18 patients diagnosed with CVS and 18 healthy individuals, were recruited at Beijing Hospital between May 2022 and October 2021. The serum samples were analyzed for liquid chromatography-tandem mass spectrometry (LC-MS/MS). Then, multivariate statistical methods, including principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) were performed. Differential metabolites were obtained and demonstrated by volcano plot and heatmap. The study recruited 36 participants, including 18 patients with CVS and 18 healthy participants. A total of 150 metabolites were identified. Multivariate statistical analysis revealed significant differences between patients and healthy participants. Furthermore, 30 serum metabolites levels differed significantly between two groups. Differential metabolites were enriched in phenylalanine, tyrosine, and tryptophan biosynthesis; primary bile acid biosynthesis, and other pathways. This study identified differential metabolites in patients with CVS and elucidated the relevant metabolic pathways. Thus, these findings aid in the study of the pathogenesis of CVS and its early diagnosis. DATA AVAILABILITY STATEMENT: The datasets generated for this study are available on request to the corresponding author.

Changes in Plasma Pyruvate and TCA Cycle Metabolites upon Increased Hepatic Fatty Acid Oxidation and Ketogenesis in Male Wistar Rats

Altered hepatic mitochondrial fatty acid β-oxidation and associated tricarboxylic acid (TCA) cycle activity contributes to lifestyle-related diseases, and circulating biomarkers reflecting these changes could have disease prognostic value. This study aimed to determine hepatic and systemic changes in TCA-cycle-related metabolites upon the selective pharmacologic enhancement of mitochondrial fatty acid β-oxidation in the liver, and to elucidate the mechanisms and potential markers of hepatic mitochondrial activity. Male Wistar rats were treated with 3-thia fatty acids (e.g., tetradecylthioacetic acid (TTA)), which target mitochondrial biogenesis, mitochondrial fatty acid β-oxidation, and ketogenesis predominantly in the liver. Hepatic and plasma concentrations of TCA cycle intermediates and anaplerotic substrates (LC-MS/MS), plasma ketones (colorimetric assay), and acylcarnitines (HPLC-MS/MS), along with associated TCA-cycle-related gene expression (qPCR) and enzyme activities, were determined. TTA-induced hepatic fatty acid β-oxidation resulted in an increased ratio of plasma ketone bodies/nonesterified fatty acid (NEFA), lower plasma malonyl-CoA levels, and a higher ratio of plasma acetylcarnitine/palmitoylcarnitine (C2/C16). These changes were associated with decreased hepatic and increased plasma pyruvate concentrations, and increased plasma concentrations of succinate, malate, and 2-hydroxyglutarate. Expression of several genes encoding TCA cycle enzymes and the malate-oxoglutarate carrier (Slc25a11), glutamate dehydrogenase (Gdh), and malic enzyme (Mdh1 and Mdh2) were significantly increased. In conclusion, the induction of hepatic mitochondrial fatty acid β-oxidation by 3-thia fatty acids lowered hepatic pyruvate while increasing plasma pyruvate, as well as succinate, malate, and 2-hydroxyglutarate.

Identification and targeting of microbial putrescine acetylation in bloodstream infections

The growth of antimicrobial resistance (AMR) has highlighted an urgent need to identify bacterial pathogenic functions that may be targets for clinical intervention. Although severe bacterial infections profoundly alter host metabolism, prior studies have largely ignored alterations in microbial metabolism in this context. Performing metabolomics on patient and mouse plasma samples, we identify elevated levels of bacterially-derived N-acetylputrescine during gram-negative bloodstream infections (BSI), with higher levels associated with worse clinical outcomes. We discover that SpeG is the bacterial enzyme responsible for acetylating putrescine and show that blocking its activity reduces bacterial proliferation and slows pathogenesis. Reduction of SpeG activity enhances bacterial membrane permeability and results in increased intracellular accumulation of antibiotics, allowing us to overcome AMR of clinical isolates both in culture and in vivo. This study highlights how studying pathogen metabolism in the natural context of infection can reveal new therapeutic strategies for addressing challenging infections.

Integrative omics approaches to advance rare disease diagnostics

Over the past decade high-throughput DNA sequencing approaches, namely whole exome and whole genome sequencing became a standard procedure in Mendelian disease diagnostics. Implementation of these technologies greatly facilitated diagnostics and shifted the analysis paradigm from variant identification to prioritisation and evaluation. The diagnostic rates vary widely depending on the cohort size, heterogeneity and disease and range from around 30% to 50% leaving the majority of patients undiagnosed. Advances in omics technologies and computational analysis provide an opportunity to increase these unfavourable rates by providing evidence for disease-causing variant validation and prioritisation. This review aims to provide an overview of the current application of several omics technologies including RNA-sequencing, proteomics, metabolomics and DNA-methylation profiling for diagnostics of rare genetic diseases in general and inborn errors of metabolism in particular.

Combining Experimental with Computational Infrared and Mass Spectra for High-Throughput Nontargeted Chemical Structure Identification

The inability to identify the structures of most metabolites detected in environmental or biological samples limits the utility of nontargeted metabolomics. The most widely used analytical approaches combine mass spectrometry and machine learning methods to rank candidate structures contained in large chemical databases. Given the large chemical space typically searched, the use of additional orthogonal data may improve the identification rates and reliability. Here, we present results of combining experimental and computational mass and IR spectral data for high-throughput nontargeted chemical structure identification. Experimental MS/MS and gas-phase IR data for 148 test compounds were obtained from NIST. Candidate structures for each of the test compounds were obtained from PubChem (mean = 4444 candidate structures per test compound). Our workflow used CSI:FingerID to initially score and rank the candidate structures. The top 1000 ranked candidates were subsequently used for IR spectra prediction, scoring, and ranking using density functional theory (DFT-IR). Final ranking of the candidates was based on a composite score calculated as the average of the CSI:FingerID and DFT-IR rankings. This approach resulted in the correct identification of 88 of the 148 test compounds (59%). 129 of the 148 test compounds (87%) were ranked within the top 20 candidates. These identification rates are the highest yet reported when candidate structures are used from PubChem. Combining experimental and computational MS/MS and IR spectral data is a potentially powerful option for prioritizing candidates for final structure verification.

Characterizing cancer metabolism from bulk and single-cell RNA-seq data using METAFlux

Cells often alter metabolic strategies under nutrient-deprived conditions to support their survival and growth. Characterizing metabolic reprogramming in the tumor microenvironment (TME) is of emerging importance in cancer research and patient care. However, recent technologies only measure a subset of metabolites and cannot provide in situ measurements. Computational methods such as flux balance analysis (FBA) have been developed to estimate metabolic flux from bulk RNA-seq data and can potentially be extended to single-cell RNA-seq (scRNA-seq) data. However, it is unclear how reliable current methods are, particularly in TME characterization. Here, we present a computational framework METAFlux (METAbolic Flux balance analysis) to infer metabolic fluxes from bulk or single-cell transcriptomic data. Large-scale experiments using cell-lines, the cancer genome atlas (TCGA), and scRNA-seq data obtained from diverse cancer and immunotherapeutic contexts, including CAR-NK cell therapy, have validated METAFlux's capability to characterize metabolic heterogeneity and metabolic interaction amongst cell types.

Emerging therapies in cancer metabolism

Metabolic reprogramming in cancer is not only a biological hallmark but also reveals treatment vulnerabilities. Numerous metabolic molecules have shown promise as treatment targets to impede tumor progression in preclinical studies, with some advancing to clinical trials. However, the intricacy and adaptability of metabolic networks hinder the effectiveness of metabolic therapies. This review summarizes the metabolic targets for cancer treatment and provides an overview of the current status of clinical trials targeting cancer metabolism. Additionally, we decipher crucial factors that limit the efficacy of metabolism-based therapies and propose future directions. With advances in integrating multi-omics, single-cell, and spatial technologies, as well as the ability to track metabolic adaptation more precisely and dynamically, clinicians can personalize metabolic therapies for improved cancer treatment.

Metabolomics in drug research and development: The recent advances in technologies and applications

Emerging evidence has demonstrated the vital role of metabolism in various diseases or disorders. Metabolomics provides a comprehensive understanding of metabolism in biological systems. With advanced analytical techniques, metabolomics exhibits unprecedented significant value in basic drug research, including understanding disease mechanisms, identifying drug targets, and elucidating the mode of action of drugs. More importantly, metabolomics greatly accelerates the drug development process by predicting pharmacokinetics, pharmacodynamics, and drug response. In addition, metabolomics facilitates the exploration of drug repurposing and drug-drug interactions, as well as the development of personalized treatment strategies. Here, we briefly review the recent advances in technologies in metabolomics and update our knowledge of the applications of metabolomics in drug research and development.

Metabolic reprogramming of clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is a malignancy that exhibits metabolic reprogramming as a result of genetic mutations. This reprogramming accommodates the energy and anabolic needs of the cancer cells, leading to changes in glucose, lipid, and bio-oxidative metabolism, and in some cases, the amino acid metabolism. Recent evidence suggests that ccRCC may be classified as a metabolic disease. The metabolic alterations provide potential targets for novel therapeutic interventions or biomarkers for monitoring tumor growth and prognosis. This literature review summarized recent discoveries of metabolic alterations in ccRCC, including changes in glucose, lipid, and amino acid metabolism. The development of metabolic drugs targeting these metabolic pathways was also discussed, such as HIF-2α inhibitors, fatty acid synthase (FAS) inhibitors, glutaminase (GLS) inhibitors, indoleamine 2,3-dioxygenase (IDO) inhibitors, and arginine depletion. Future trends in drug development are proposed, including the use of combination therapies and personalized medicine approaches. In conclusion, this review provides a comprehensive overview of the metabolic alterations in ccRCC and highlights the potential for developing new treatments for this disease.

Phosphoenolpyruvate carboxykinases as emerging targets in cancer therapy

Metabolic reprogramming is commonly accompanied by alterations in the expression of metabolic enzymes. These metabolic enzymes not only catalyze the intracellular metabolic reaction, but also participate in a series of molecular events to regulate tumor initiation and development. Thus, these enzymes may act as promising therapeutic targets for tumor management. Phosphoenolpyruvate carboxykinases (PCKs) are the key enzymes involved in gluconeogenesis, which mediates the conversion of oxaloacetate into phosphoenolpyruvate. Two isoforms of PCK, namely cytosolic PCK1 and mitochondrial PCK2, has been found. PCK not only participates in the metabolic adaptation, but also regulates immune response and signaling pathways for tumor progression. In this review, we discussed the regulatory mechanisms of PCKs expression including transcription and post-translational modification. We also summarized the function of PCKs in tumor progression in different cellular contexts and explores its role in developing promising therapeutic opportunities.

Physiological and pathological roles of lipogenesis

Lipids are essential metabolites, which function as energy sources, structural components and signalling mediators. Most cells are able to convert carbohydrates into fatty acids, which are often converted into neutral lipids for storage in the form of lipid droplets. Accumulating evidence suggests that lipogenesis plays a crucial role not only in metabolic tissues for systemic energy homoeostasis but also in immune and nervous systems for their proliferation, differentiation and even pathophysiological roles. Thus, excessive or insufficient lipogenesis is closely associated with aberrations in lipid homoeostasis, potentially leading to pathological consequences, such as dyslipidaemia, diabetes, fatty liver, autoimmune diseases, neurodegenerative diseases and cancers. For systemic energy homoeostasis, multiple enzymes involved in lipogenesis are tightly controlled by transcriptional and post-translational modifications. In this Review, we discuss recent findings regarding the regulatory mechanisms, physiological roles and pathological importance of lipogenesis in multiple tissues such as adipose tissue and the liver, as well as the immune and nervous systems. Furthermore, we briefly introduce the therapeutic implications of lipogenesis modulation.

Performance comparison of narrow-bore and capillary liquid-chromatography for non-targeted metabolomics profiling by HILIC-QTOF-MS

Clinical metabolomics studies often have to cope with limited sample amounts, thus miniaturized liquid chromatography (LC) systems are a promising alternative. Their applicability has already been demonstrated in various fields, including a few metabolomics studies that mainly used reversed-phase chromatography. However, hydrophilic interaction chromatography (HILIC), which is widely used in metabolomics due to its particular suitability for the analysis of polar molecules, has rarely been tested for miniaturized LC-MS analysis of small molecules. In the present work, the suitability of a capillary HILIC (CapHILIC)-QTOF-MS system for non-targeted metabolomics was evaluated based on extracts of porcine formalin-fixed, paraffin-embedded (FFPE) tissue samples. The performance was assessed with respect to the number and retention time span of metabolic features as well as the analytical repeatability, the signal-to-noise ratio and the signal intensity of 16 annotated metabolites from different compound classes. The results were compared with a well established narrow-bore HILIC-QTOF-MS system. Both platforms have detected a similar number of features and performed excellent with respect to retention time stability (median RT span <0.05 min) and analytical repeatability (>75% of features with CV < 20%). The signal areas of all metabolites assessed were increased up to 18-fold by the use of CapHILIC, although the signal-to-noise ratio was only improved for 50% of the metabolites. An even better reproducibility (median CV = 5.2%) and up to 80-fold increase in signal intensity were observed after optimization of CapHILIC conditions for analysis of bile acid standard solutions. Even though the observed improvement for specific bile acids (e.g. taurocholic acid) in biological matrix needs to be evaluated, the platform comparison indicates, that the tested CapHILIC system is particularly suitable for analyses of a less broad metabolite spectrum, and specifically optimized chromatography.

Feature-Based Molecular Networking Facilitates the Comprehensive Identification of Differential Metabolites in Diabetic Cognitive Dysfunction Rats

Cognitive dysfunction is a frequent complication of type 2 diabetes mellitus (T2DM), usually accompanied by metabolic disorders. However, the metabolic changes in diabetic cognitive dysfunction (DCD) patients, especially compared to T2DM groups, are not fully understood. Due to the subtle differences in metabolic alterations between DCD groups and T2DM groups, the comprehensive detection of the untargeted metabolic profiles of hippocampus and urine samples of rats was conducted by LC-MS, considering the different ionization modes and polarities of the examined compounds, and feature-based molecular networking (FBMN) was performed to help identify differential metabolites from a comprehensive perspective in this study. In addition, an association analysis of the differential metabolites in hippocampus and urine was conducted by the O2PLS model. Finally, a total of 71 hippocampal tissue differential metabolites and 179 urine differential metabolites were identified. The pathway enrichment results showed that glutamine and glutamate metabolism, alanine, aspartate, and glutamate metabolism, glycerol phospholipid metabolism, TCA cycle, and arginine biosynthesis in the hippocampus of DCD animals were changed. Seven metabolites (AUC > 0.9) in urine appeared as key differential metabolites that might reflect metabolic changes in the target tissue of DCD rats. This study showed that FBMN facilitated the comprehensive identification of differential metabolites in DCD rats. The differential metabolites may suggest an underlying DCD and be considered as potential biomarkers for DCD. Large samples and clinical experiments are needed for the subsequent elucidation of the possible mechanisms leading to these alterations and the verification of potential biomarkers.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

Non-Invasive Disease Specific Biomarker Detection Using Infrared Spectroscopy: A Review

Many life-threatening diseases remain obscure in their early disease stages. Symptoms appear only at the advanced stage when the survival rate is poor. A non-invasive diagnostic tool may be able to identify disease even at the asymptotic stage and save lives. Volatile metabolites-based diagnostics hold a lot of promise to fulfil this demand. Many experimental techniques are being developed to establish a reliable non-invasive diagnostic tool; however, none of them are yet able to fulfil clinicians' demands. Infrared spectroscopy-based gaseous biofluid analysis demonstrated promising results to fulfil clinicians' expectations. The recent development of the standard operating procedure (SOP), sample measurement, and data analysis techniques for infrared spectroscopy are summarized in this review article. It has also outlined the applicability of infrared spectroscopy to identify the specific biomarkers for diseases such as diabetes, acute gastritis caused by bacterial infection, cerebral palsy, and prostate cancer.

An omics approach to delineating the molecular mechanisms that underlie the biological effects of physical plasma

The use of physical plasma to treat cancer is an emerging field, and interest in its applications in oncology is increasing rapidly. Physical plasma can be used directly by aiming the plasma jet onto cells or tissue, or indirectly, where a plasma-treated solution is applied. A key scientific question is the mechanism by which physical plasma achieves selective killing of cancer over normal cells. Many studies have focused on specific pathways and mechanisms, such as apoptosis and oxidative stress, and the role of redox biology. However, over the past two decades, there has been a rise in omics, the systematic analysis of entire collections of molecules in a biological entity, enabling the discovery of the so-called "unknown unknowns." For example, transcriptomics, epigenomics, proteomics, and metabolomics have helped to uncover molecular mechanisms behind the action of physical plasma, revealing critical pathways beyond those traditionally associated with cancer treatments. This review showcases a selection of omics and then summarizes the insights gained from these studies toward understanding the biological pathways and molecular mechanisms implicated in physical plasma treatment. Omics studies have revealed how reactive species generated by plasma treatment preferentially affect several critical cellular pathways in cancer cells, resulting in epigenetic, transcriptional, and post-translational changes that promote cell death. Finally, this review considers the outlook for omics in uncovering both synergies and antagonisms with other common cancer therapies, as well as in overcoming challenges in the clinical translation of physical plasma.

Global Trends of Lipid Metabolism Research in Epigenetics Field: A Bibliometric Analysis from 2012-2021

Most common diseases are characterized by metabolic changes, among which lipid metabolism is a hotspot. Numerous studies have demonstrated a strong correlation between epigenetics and lipid metabolism. This study of publications on the epigenetics of lipid metabolism searched in the Web of Science Core Collection from 2012 to 2022, and a total of 3685 publications were retrieved. Much of our work focused on collecting the data of annual outputs, high-yielding countries and authors, vital journals, keywords and citations for qualitative and quantitative analysis. In the past decade, the overall number of publications has shown an upward trend. China (1382, 26.69%), the United States (1049, 20.26%) and Italy (206, 3.98%) were the main contributors of outputs. The Chinese Academy of Sciences and Yale University were significant potential cooperation institutions. Articles were mainly published in the "International Journal of Molecular Sciences". In addition to typical liver-related diseases, "ferroptosis", "diabetes" and "atherosclerosis" were identified as potential research topics. "NF-κB" and "oxidative stress" were referred to frequently in publications. METTL3 and ALKBH5 were the most discussed m⁶A-related enzymes in 2022. Our study revealed research hotspots and new trends in the epigenetics of lipid metabolism, hoping to provide significant information and inspiration for researchers to further explore new directions.

Metabolic signatures in pericardial fluid and serum are associated with new-onset atrial fibrillation after isolated coronary artery bypass grafting

Postoperative atrial fibrillation (POAF) is a common complication of coronary artery bypass grafting (CABG) procedures. However, the molecular mechanism of POAF remains poorly understood, hence the absence of effective prevention strategies. Here we used targeted metabolomics on pericardial fluid and serum samples from CABG patients to investigate POAF-associated metabolic alterations and related risk prediction of new-onset AF. Nine differential metabolites in various metabolic pathways were found in both pericardial fluid and serum samples from patients with POAF and without POAF. By using machine learning algorithms and regression models, a 4-metabolite (aceglutamide, ornithine, methionine, and arginine) risk prediction model was constructed and showed accurate performance in predicting POAF in both discovery and validation sets. This work extends the metabolic insights of the cardiac microenvironment and blood in patients with POAF and paves the way for the use of targeted metabolomics for predicting POAF in patients with CABG surgery.

The sirtuin family in health and disease

Sirtuins (SIRTs) are nicotine adenine dinucleotide(+)-dependent histone deacetylases regulating critical signaling pathways in prokaryotes and eukaryotes, and are involved in numerous biological processes. Currently, seven mammalian homologs of yeast Sir2 named SIRT1 to SIRT7 have been identified. Increasing evidence has suggested the vital roles of seven members of the SIRT family in health and disease conditions. Notably, this protein family plays a variety of important roles in cellular biology such as inflammation, metabolism, oxidative stress, and apoptosis, etc., thus, it is considered a potential therapeutic target for different kinds of pathologies including cancer, cardiovascular disease, respiratory disease, and other conditions. Moreover, identification of SIRT modulators and exploring the functions of these different modulators have prompted increased efforts to discover new small molecules, which can modify SIRT activity. Furthermore, several randomized controlled trials have indicated that different interventions might affect the expression of SIRT protein in human samples, and supplementation of SIRT modulators might have diverse impact on physiological function in different participants. In this review, we introduce the history and structure of the SIRT protein family, discuss the molecular mechanisms and biological functions of seven members of the SIRT protein family, elaborate on the regulatory roles of SIRTs in human disease, summarize SIRT inhibitors and activators, and review related clinical studies.

Micro-Slab Coil Design for Hyperpolarized Metabolic Flux Analysis in Multiple Samples

Abnormal metabolism is a hallmark of cancer cells. Accumulating evidence suggests that metabolic changes are likely to occur before other cellular responses in cancer cells upon drug treatment. Therefore, the metabolic activity or flux in cancer cells could be a potent biomarker for cancer detection and treatment monitoring. Magnetic resonance (MR)-based sensing technologies have been developed with hyperpolarized molecules for real-time flux analysis, but they still suffer from low sensitivity and throughput. To address this limitation, we have developed an innovative miniaturized MR coil, termed micro-slab MR coil, for simultaneous analysis of metabolic flux in multiple samples. Combining this approach with hyperpolarized probes, we were able to quantify the pyruvate-to-lactate flux in two different leukemic cell lines in a non-destructive manner, simultaneously. Further, we were able to rapidly assess flux changes with drug treatment in a single hyperpolarization experiment. This new multi-sample system has the potential to transform our ability to assess metabolic dynamics at scale.

A metabolomic-based biomarker discovery study for predicting phototherapy duration for neonatal hyperbilirubinemia

BACKGROUND

Phototherapy is a recommended method for the treatment of neonatal hyperbilirubinemia. However, biomarkers for predicting the more effective duration of phototherapy prior to treatment are lacking. Therefore, we aimed to determine novel predictors for the timing of phototherapy from the perspective of metabolomics.

METHODS

A total of 12 newborns with neonatal hyperbilirubinemia were recruited on the day of admission. The infants were divided into a short-duration (<30 hours) phototherapy group and a long-duration (≥30 hours) phototherapy group based on the length of phototherapy treatment. Metabolites in serum samples were then explored using an untargeted metabolomics strategy.

RESULTS

In total, 59 of 1,073 significantly different metabolites were identified between the short-duration and long-duration phototherapy groups, including 18 upregulated and 41 downregulated metabolites. The results of metabolomic analysis showed that the differentially expressed metabolites were enriched in glycerophospholipid metabolism, which is closely associated with the excretion of bilirubin. Moreover, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that the metabolites were also enriched in alpha-Linolenic acid metabolism and fatty acid elongation. Spearman correlation hierarchical clustering analysis demonstrated that 9 metabolites were negatively correlated with the duration of phototherapy. Metabolites, especially phosphatidylethanolamine (PE) (22:1(13Z)/15:0), phosphatidylcholine (PC) (18:1(9Z)/18:1(9Z)), phosphatidylserine (PS) (22:0/15:0), 5,6-dihydrouridine, and PE (MonoMe(11,3)/MonoMe(13,5)), had better predictability for the duration of phototherapy [area under curve (AUC): 1; 95% confidence interval (CI): 1-1] than total serum total bilirubin and direct bilirubin (AUC: 0.806; 95% CI: 0.55-1), as revealed by receiver operating characteristic analysis.

CONCLUSIONS

Our research found that the differential metabolites were associated with the duration of neonatal jaundice and that glycerophospholipid metabolism might have played a role in this biological process. Moreover, metabolites such as PE (22:1(13Z)/15:0), PC (18:1(9Z)/18:1(9Z)), PS (22:0/15:0), 5,6-dihydrouridine, and PE (MonoMe(11,3)/MonoMe(13,5)) could be used as predictors for phototherapy duration in neonatal hyperbilirubinemia and assist with decision-making.

Schisantherin A inhibits cell proliferation by regulating glucose metabolism pathway in hepatocellular carcinoma

Schisantherin A (STA) is a traditional Chinese medicine extracted from the plant Schisandra chinensis, which has a wide range of anti-inflammatory, antioxidant, and other pharmacological effects. This study investigates the anti-hepatocellular carcinoma effects of STA and the underlying mechanisms. STA significantly inhibits the proliferation and migration of Hep3B and HCCLM3 cells in vitro in a concentration-dependent manner. RNA-sequencing showed that 77 genes are upregulated and 136 genes are downregulated in STA-treated cells compared with untreated cells. KEGG pathway analysis showed significant enrichment in galactose metabolism as well as in fructose and mannose metabolism. Further gas chromatography-mass spectrometric analysis (GC-MS) confirmed this, indicating that STA significantly inhibits the glucose metabolism pathway of Hep3B cells. Tumor xenograft in nude mice showed that STA has a significant inhibitory effect on tumor growth in vivo. In conclusion, our results indicate that STA can inhibit cell proliferation by regulating glucose metabolism, with subsequent anti-tumor effects, and has the potential to be a candidate drug for the treatment of liver cancer.