Metabolomics toward personalized medicine

Understanding the side effects of chronic silodosin administration via untargeted metabolomics approach

BACKGROUND

Precision medicine, which looks for high efficacy and low toxicity in therapies, has increased in popularity with omics technology. This work aims to discover novel and low-toxicity therapy options by examining the complex relationship between silodosin-induced side effects and the metabolomic profiles associated with its administration.

MATERIALS AND METHODS

The plasma samples of the control group and silodosin-treated rats were analyzed by LC-Q-TOF-MS/MS. Employing XCMS and MetaboAnalyst software, MS/MS data processed to detect compounds and investigate metabolic pathways. MATLAB 2019b was used for data categorization and multivariate analysis. A thorough comparison of METLIN and HMDB databases revealed 41m/z values with significant differences between the drug-treated and control groups (p <0.01 and fold analysis≥1.5).

RESULTS

According to multivariate data analysis, 17-β-estradiol, taurocholic acid, L-kynurenine, N-formylkynurenine, D-glutamine, L-arginine, prostaglandin H2, prostaglandine G2, 15-keto-prostaglandin E2, calcidiol, thromboxane A2, 5'-methylthioadenosine, L-methionine and S-adenosylmethionine levels changed significantly compared to the control group. Differences in the metabolisms of glycerophospholipid, tyrosine, phenylalanine, arachidonic acid, cysteine and methionine, and biosynthesis of phenylalanine, tyrosine, and tryptophan, and aminoacyl-tRNA have been successfully demonstrated by metabolic pathway analysis. According to this study, vitamin D, D-glutamine, and L-arginine supplements can be recommended to prevent side effects such as fatigue, intraoperative floppy iris syndrome, blurred vision, and dizziness in the treatment of silodosin. Silodosin treatment negatively affected the immune system by affecting the kynurenine and tryptophan metabolism pathways.

CONCLUSIONS

The study is a guide for silodosin treatments that offer low side effects and high therapeutic effect within the scope of precision medicine.

Candidate Molecular Biomarkers of Traumatic Brain Injury: A Systematic Review

Traumatic brain injury (TBI) is one of the leading causes of mortality and disability among young and middle-aged individuals. Adequate and timely diagnosis of primary brain injuries, as well as the prompt prevention and treatment of secondary injury mechanisms, significantly determine the potential for reducing mortality and severe disabling consequences. Therefore, it is crucial to have objective markers that indicate the severity of the injury. A number of molecular factors-proteins and metabolites-detected in the blood immediately after trauma and associated with the development and severity of TBI can serve in this role. TBI is a heterogeneous condition with respect to its etiology, clinical form, and genesis, being accompanied by brain cell damage and disruption of blood-brain barrier permeability. Two oppositely directed flows of substances and signals are observed: one is the flow of metabolites, proteins, and nucleic acids from damaged brain cells into the bloodstream through the damaged blood-brain barrier; the other is the infiltration of immune cells (neutrophils and macrophages) and serological proteins. Both flows aggravate brain tissue damage after TBI. Therefore, it is extremely important to study the key signaling events that regulate these flows and repair the damaged tissues, as well as to enhance the effectiveness of treatments for patients after TBI.

Optimization of metabolomics pretreatment method of cholangiocarcinoma cells based on ultrahigh performance liquid chromatography coupled with mass spectrometry

Metabolomics intends to maximize the quantity of available metabolites for the global metabolome, which largely depends on sample pretreatment protocols. However, there are few studies that comprehensively examined the effects of extraction and reconstitution solvents on metabolome coverage of adherent mammalian cells. In this study, the human cholangiocarcinoma TFK-1 cells were chosen as a cell model, and eight extraction solvents and five reconstitution solvents were used for the pretreatment based on ultrahigh performance liquid chromatography coupled with mass spectrometry (UPLC/MS). The coverage, reproducibility, and stability of the data were norms to evaluate the effectiveness of different extraction solvents and reconstitution solvents. Based on the number of metabolites, the mean Euclidean distance (EDMEAN) in the principal component analysis (PCA) 3D score plots and the relative standard deviation (RSD) distribution of metabolites, it was demonstrated that MeOH-CHCl3-H2O (8:1:1, v/v/v) was the optimal extraction solvent and MeOH-H2O (1:1, v/v) or H2O was superior to other reconstitution solvents for RP column analysis, and the extraction solvent MeOH-ACN-H2O (2:2:1, v/v/v) and the reconstitution solvents ACN-H2O (4:1, v/v) or MeOH-H2O (1:1, v/v) provide the best performance for HILIC column analysis. The optimized pretreatment methods explored in this study expand the coverage of polar and non-polar metabolites and improve the reproducibility and stability of the metabolic data, which can be applied to UPLC/MS-based global metabolomics study on cholangiocarcinoma cells, potentially providing better extraction solvents and reconstitution solvents for other adherent mammalian cells with similar chemical and physical properties.

Global research trends and hotspots in metabolomics of osteosarcoma: a decade-spanning bibliometric and visualized analysis

Object

Osteosarcoma is a malignant tumor originating from the bones, commonly found in children and adolescents, especially in rapidly growing bone areas such as the knees and upper arms. In this study, we aim to delineate the evolution and convergence of research themes in osteosarcoma metabolomics over the past decade, identify major contributors, and forecast emerging trends that could direct future research efforts.

Method

The bibliometric method has been applied to systematically analyze the literature in the field of osteosarcoma metabolomics. The relevant literatures were collected from the Web of Science Core Collection, spanning from January 1, 2014, to December 31, 2023. Tools such as CiteSpace, Bibliometrix, and VOSviewer were used for the visual analysis of the collected literatures. The focused information includes institutions, journals, countries, authors, keywords, and citations.

Result

Various aspects in the field of osteosarcoma metabolism were analyzed. Shanghai Jiao Tong University has published the most papers in the past ten years, followed by Central South University and Zhejiang University. Among the sources, the international journal of molecular sciences publishes the most articles, and oncotarget is the journal with the highest H index. According to Bradford's law, there are 34 core journals identified. A total of 5501 authors participated in the creation of papers in this field. The distribution of authors follows Lotka`s Law, and 85.3% of authors have only one article. 46% of the corresponding authors are from China, but most of these corresponding authors are not good at international cooperation. China also has the largest number of publications, followed by the United States. It can be confirmed that China dominates this field. Among the keywords, "expression" is the keyword that has received the most attention in the past ten years. All keywords can be divided into 9 clusters. Based on the explosive words and hot topics each year, we speculate that future research will focus on the tumor microenvironment, molecular mechanisms and autophagy, targeted therapies and inhibitors.

Conclusion

In summary, this study comprehensively analyzed the current state of research in the field of osteosarcoma metabolism through bibliometric methods. The findings revealed the development trends and research hotspots in this field, which may provide valuable references for future research directions.

Saliva metabolomics: concepts and applications in oral disorders

OBJECTIVES

The purpose of this review was to present the basic concepts of metabolomics methodology and the use of saliva for diagnostic, prognostic, and predictive strategies.

MATERIAL AND METHODS

This review followed the focus in: "saliva metabolomics" and "oral diseases". The authors searched studies on PubMed database. The inclusion criteria were original studies and reviews that assessed metabolomics techniques. A descriptive analysis was performed considering the study design, approach system, clinical steps, and tools for the determination of profile or biomarkers metabolites, and the advantages and disadvantages.

RESULTS

Metabolomic analyses use a combination of analytical instrumentation and informatic tools to provide information on metabolite characteristics. In this review we described different technologies applied and the advantages and limitations of each technique. Furthermore, in the literature search, we retrieved 25 studies that investigated saliva metabolites in oral diseases: 8 studies used targeted analysis and 17 untargeted metabolomics approaches. Most studies included patients with periodontal diseases, oral squamous cell carcinoma, and Sjögren Syndrome. The most frequently reported metabolites were glycine, leucine, phenylalanine, dipeptides, linoleic acid, arachidonic acid, tyrosine, choline, taurine, lactate, valine, and proline.

CONCLUSIONS

Metabolomics analysis has emerged as a powerful tool for tumor diagnosis and to enhance tumor classification, including salivary gland tumors (SGTs). It also holds promise for developing personalized treatment plans and defining more precise prognostic categories.

CLINICAL RELEVANCE

Metabolomics is the most functional and comprehensive technique for monitoring and understanding gene functions and identifying the biochemical state of an organism in response to genetic and environmental changes.

Metabolomics in systemic sclerosis

Systemic sclerosis is a rare autoimmune condition leading to incurable complications. Therefore fast and precise diagnosis is crucial to prevent patient death and to maintain quality of life. Unfortunately, currently known biomarkers do not meet this need. To address this problem researchers use diverse approaches to elucidate the underlying aberrations. One of the methods applied is metabolomics. This modern technique enables a comprehensive assessment of multiple compound concentrations simultaneously. As it has been gaining popularity, we found it necessary to summarize metabolomic studies presented so far in a narrative review. We found 11 appropriate articles. All of the researchers found significant differences between patients and control groups, whereas the reported findings were highly inconsistent. Additionally, we have found the investigated groups in most studies were scarcely described, and the inclusion/exclusion approach was diverse. Therefore, further study with meticulous patient assessment is necessary.

Identification of serum metabolites associated with aristolochic acid nephropathy severity and insights into the underlying mechanism

Aristolochic acid nephropathy (AAN) is a rapidly progressive kidney disease caused by medical or environmental exposure to aristolochic acids (AAs). This study aimed to identify serum metabolites associated with the severity of acute AAN and investigate the underlying mechanisms. Male C57BL/6 mice were treated with vehicle and 3 doses of aristolochic acid I (AAI) (1.25, 2.5, and 5 mg/kg/d) for 5 days by intraperitoneal injection. The results showed that AAI dose-dependently increased blood urea nitrogen (BUN) and serum creatinine (Scr) levels and renal pathological damage. Non-targeted metabolomics revealed that differences in serum metabolite profiles from controls increased with increasing AAI doses. Compared with the control group, 56 differentially expressed metabolites (DEMs) that could be affected by all 3 doses of AAI were obtained. We further identified 13 DEMs whose abundance significantly correlated with Scr and BUN levels and had good predictive values for diagnosing AAI exposure. Among the 13 DEMs, lipids and lipid-like molecules constituted the majority. Western blotting found that AAI suppressed renal fatty acid oxidation (FAO)-related enzymes expression. In conclusion, these findings provided evidence for developing biomarkers for monitoring AAs exposure and AAN diagnosis and indicated activation of FAO as a potential direction for the treatment of AAN.

Plasma metabolomic signature of healthy lifestyle, structural brain reserve and risk of dementia

Although the association between healthy lifestyle and dementia risk has been documented, the relationship between a metabolic signature indicative of healthy lifestyle and dementia risk and the mediating role of structural brain impairment remain unknown. We retrieved 136 628 dementia-free participants from UK Biobank. Elastic net regression was used to obtain a metabolic signature that represented lifestyle behaviours. Cox proportional hazard models were fitted to explore the associations of lifestyle-associated metabolic signature with incident dementia. Causal associations between identified metabolites and dementia were investigated using Mendelian randomization. Mediation analysis was also conducted to uncover the potential mechanisms involving 19 imaging-derived phenotypes (brain volume, grey matter volume, white matter volume and regional grey matter volumes). During a follow-up of 12.55 years, 1783 incident cases of all-cause dementia were identified, including 725 cases of Alzheimer's dementia and 418 cases of vascular dementia. We identified 83 metabolites that could represent healthy lifestyle behaviours using elastic net regression. The metabolic signature was associated with a lower dementia risk, and for each standard deviation increment in metabolic signature, the hazard ratio was 0.89 [95% confidence interval (CI): 0.85, 0.93] for all-cause dementia, 0.95 (95% CI: 0.88, 1.03) for Alzheimer's dementia and 0.84 (95% CI: 0.77, 0.91) for vascular dementia. Mendelian randomization revealed potential causal associations between the identified metabolites and risk of dementia. In addition, the specific structural brain reserve, including the hippocampus, grey matter in the hippocampus, parahippocampal gyrus and middle temporal gyrus, were detected to mediate the effects of metabolic signature on dementia risk (mediated proportion ranging from 6.21% to 11.98%). The metabolic signature associated with a healthy lifestyle is inversely associated with dementia risk, and greater structural brain reserve plays an important role in mediating this relationship. These findings have significant implications for understanding the intricate connections between lifestyle, metabolism and brain health.

Metabolomics for hematologic malignancies: Advances and perspective

With the use of advanced technology, metabolomics allows for a thorough examination of metabolites and other small molecules found in biological specimens, blood, and tissues. In recent years, metabolomics has been recognized that is closely related to the development of malignancies in the hematological system. Alterations in metabolomic pathways and networks are important in the pathogenesis of hematologic malignancies and can also provide a theoretical basis for early diagnosis, efficacy evaluation, accurate staging, and individualized targeted therapy. In this review, we summarize the progress of metabolomics, including glucose metabolism, amino acid metabolism, and lipid metabolism in lymphoma, myeloma, and leukemia through specific mechanisms and pathways. The research of metabolomics gives a new insight and provides therapeutic targets for the treatment of patients with hematologic malignancies.

Profiling of metabolic dysregulation in ovarian cancer tissues and biofluids

Ovarian cancer (OC) is the most lethal gynecologic cancer, mainly due to late diagnosis with widespread peritoneal spread at first presentation. We performed metabolomic analyses of ovarian and paired control tissues using capillary electrophoresis-mass spectrometry and liquid chromatography-mass spectrometry to understand its metabolomic dysregulation. Of the 130 quantified metabolites, 96 metabolites of glycometabolism, including glycolysis, tricarboxylic acid cycles, urea cycles, and one-carbon metabolites, showed significant differences between the samples. To evaluate the local and systemic metabolomic differences in OC, we also analyzed low or non-invasively available biofluids, including plasma, urine, and saliva collected from patients with OC and benign gynecological diseases. All biofluids and tissue samples showed consistently elevated concentrations of N1,N12-diacetylspermine compared to controls. Four metabolites, polyamines, and betaine, were significantly and consistently elevated in both plasma and tissue samples. These data indicate that plasma metabolic dysregulation, which the most reflected by those of OC tissues. Our metabolomic profiles contribute to our understanding of metabolomic abnormalities in OC and their effects on biofluids.

Association of leucine and other branched chain amino acids with clinical outcomes in malnourished inpatients: a secondary analysis of the randomized clinical trial EFFORT

BACKGROUND

The essential branched-chain amino acids leucine, isoleucine and valine are considered anabolic and stimulate protein synthesis in the muscles as well in the liver. They also promote muscle recovery and contribute to glucose homeostasis. Recent studies in critically ill patients have demonstrated that depletion of plasma leucine is associated with increased mortality, but data in the non-critical care setting is lacking.

METHODS

This secondary analysis of the randomized controlled Effect of early nutritional support on Frailty, Functional Outcomes, and Recovery of malnourished medical inpatients Trial (EFFORT), investigated the impact of leucine, isoleucine, and valine metabolism on clinical outcomes. The primary endpoint was 180-day all-cause mortality.

RESULTS

Among 238 polymorbid patients with available metabolite measurements, low serum leucin levels were associated with a doubled risk of 180-day all-cause mortality in a fully adjusted regression model (adjusted HR 2.20 [95% CI 1.46-3.30], p < 0.001). There was also an association with mortality for isoleucine (1.56 [95% CI 1.03-2.35], p = 0.035) and valine (1.69 [95% CI 1.13-2.53], p = 0.011). When comparing effects of nutritional support on mortality in patients with high and low levels of leucine, there was no evidence of significant differences in effectiveness of the intervention. The same was true for isoleucine and valine.

CONCLUSION

Our data suggest that depletion of leucine, isoleucine, and valine among malnourished polymorbid patients is associated with increases in long-term mortality. However, patients with low metabolite levels did not show a pronounced benefit from nutritional support. Further research should focus on the clinical effects of nutritional support in patients with depleted stores of essential branched-chain amino acids.

CLINICAL TRIAL REGISTRATION

clinicaltrials.gov as NCT02517476 (registered 7 August 2015).

Metabolomic Applications in Gut Microbiota-Host Interactions in Human Diseases

The human gut microbiota, consisting of trillions of microorganisms, encodes diverse metabolic pathways that impact numerous aspects of host physiology. One key way in which gut bacteria interact with the host is through the production of small metabolites. Several of these microbiota-dependent metabolites, such as short-chain fatty acids, have been shown to modulate host diseases. In this review, we examine how disease-associated metabolic signatures are identified using metabolomic platforms, and where metabolomics is applied in gut microbiota-disease interactions. We further explore how integration of metagenomic and metabolomic data in human studies can facilitate biomarkers discoveries in precision medicine.

The role of bile acid metabolism in bone and muscle: from analytics to mechanisms

Osteoporosis and sarcopenia are both common age-related disorders that are associated with increased morbidity and mortality. Bone and muscle are metabolically very active tissues that require large amounts of energy. Bile acids (BAs), a group of liver-derived steroid compounds, are primarily known as emulsifiers that facilitate the resorption of dietary fat and lipids. In addition, they have pleiotropic metabolic functions in lipoprotein and glucose metabolism, inflammation, and intestinal bacterial growth. Through these effects, they are related to metabolic diseases, such as diabetes, hypertriglyceridemia, atherosclerosis, and nonalcoholic steatohepatitis. BAs mediate their metabolic effects through receptor dependent and receptor-independent mechanisms. Emerging evidence suggests that BAs are also involved in bone and muscle metabolism. Under normal circumstances, BAs support bone health by shifting the delicate equilibrium of bone turnover toward bone formation. In contrast, low or excessive amounts of BAs promote bone resorption. In cholestatic liver disease, BAs accumulate in the liver, reach toxic concentrations in the circulation, and thus may contribute to bone loss and muscle wasting. In addition, the measurement of BAs is in rapid evolution with modern mass spectrometry techniques that allow for the detection of a continuously growing number of BAs. This review provides a comprehensive overview of the biochemistry, physiology and measurement of bile acids. Furthermore, it summarizes the existing literature regarding their role in bone and muscle.

Impact of the redox-active MnTnHex-2-PyP5+ and cisplatin on the metabolome of non-small cell lung cancer cells

Redox-based cancer therapeutic strategies aim to raise reactive oxygen species (ROS) levels in cancer cells, thus modifying their redox status, and eventually inducing cell death. Promising compounds, known as superoxide dismutase mimics (SODm), e.g. MnTnHex-2-Py5+ (MnTnHex), could increase intracellular H2O2 in cancer cells with deficient ROS removal systems and therefore enhance radio- and chemotherapy efficacy. We have previously shown that MnTnHex was cytotoxic either alone or combined with cisplatin to non-small cell lung cancer (NSCLC) cells. To gain a deeper understanding of the effects and safety of this compound, it is crucial to analyze the metabolic alterations that take place within the cell. Our goal was thus to study the intracellular metabolome (intracellular metabolites) of NSCLC cells (A549 and H1975) using nuclear magnetic resonance (NMR) spectroscopy-based metabolomics to evaluate the changes in cellular metabolism upon exposure to MnTnHex per se or in combination with cisplatin. 1H NMR metabolomics revealed a higher number of significantly altered metabolites in A549 cells exposed to MnTnHex alone or combined with cisplatin in comparison with non-treated cells (nine dysregulated metabolites), suggesting an impact on aminoacyl-tRNA biosynthesis, glycolysis/gluconeogenesis, taurine, hypotaurine, glycerophospholipid, pyruvate, arginine and proline metabolisms. Regarding H1975 cells, significant alterations in the levels of six metabolites were observed upon co-treatment with MnTnHex and cisplatin, suggesting dysregulations in aminoacyl-tRNA biosynthesis, arginine and proline metabolism, pyruvate metabolism, and glycolysis/gluconeogenesis. These findings help us to understand the impact of MnTnHex on NSCLC cells. Importantly, specific altered metabolites, such as taurine, may contribute to the chemosensitizing effects of MnTnHex.

Targeted metabolomic analysis of serum free fatty acids: Lipidomics disturbance in patients with superior limbic keratoconjunctivitis

This study aimed to identify and quantify free fatty acids (FFAs), secretory phospholipase A2 group IIa (sPLA2-IIa) and cytosolic phospholipase A2 (cPLA2) in serum of superior limbic keratoconjunctivitis (SLK) patients and explored the association between FFAs, sPLA2-IIa and cPLA2 variations and SLK. Targeted metabolomic analysis of FFAs in serum was performed by gas chromatography tandem mass spectrometry (GC-MS/MS) analysis on 16 SLK patients (43.88 ± 7.88 years; female: 62.50%) and 25 healthy controls (43.12 ± 7.88 years; female: 64.00%). Qualitative and absolute quantitative results of FFAs were obtained and classified according to gender and thyroid tests. Differential lipid metabolites, metabolomic pathways and biomarkers were further evaluated. The serum sPLA2-IIa and cPLA2 were determined by enzyme linked immunosorbent assay (ELISA). Among 40 FFAs identified, 6 FFAs showed significant changes (P < 0.05) in SLK patients, including 4 decreased and 2 increased. They were mainly related to unsaturated fatty acid biosynthesis, α-linolenic acid and linoleic acid metabolism, and fatty acid biosynthesis. When dividing the data by gender or abnormal thyroid tests, some comparable FFAs alterations displayed in SLK patients. The ROC analysis revealed that the AUC values of linoleic acid, γ-linolenic acid, cis-8,11,14-eicosatrienoic acid, stearic acid, and palmitic acid, were all greater than 0.8. The serum concentrations of sPLA2-IIa and cPLA2 in patients with SLK were significantly higher than that in healthy controls. Lipidomics disturbance might be the potential mechanism of SLK. Serum FFA biomarkers associated with SLK have potential for the diagnosis and treatment of the disease.

The MACROD2 rs6110695 A>G Polymorphism and the Metabolites Indoleacrylic Acid and Butyrylcarnitine Potentially Have Clinical Relevance to WBC Count Prediction

Our previous study suggested that the Mono-ADP ribosylhydrolase 2 (MACROD2) rs6110695 A>G polymorphism is significantly associated with white blood cell (WBC) count in the Korean population. The present study aimed to evaluate the clinical relevance of the MACROD2 rs6110695 A>G polymorphism for predicting WBC count by utilizing plasma metabolites and a single-nucleotide polymorphism (SNP). Two groups were characterized by MACROD2 rs6110695 A>G SNP genotypes among 139 healthy subjects based on the genetic information provided in our previous work: rs6110695 AA genotype group (n = 129) and rs6110695 AG genotype group (n = 10). Plasma global metabolic profiling was performed using ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). To estimate the predictive abilities of WBC count models using the rs6110695 genotype and/or significant differential metabolites, multiple linear regression analysis and receiver operating characteristic (ROC) curve analysis were conducted. The AG genotype had greater WBC-to-apolipoprotein (apo) A-I ratios; counts of WBCs, lymphocytes, monocytes, and granulocytes; monocyte-to-lymphocyte ratio (MLR); and monocyte-to-platelet ratio (MPR) than the AA genotype. In terms of metabolic profile, indoleacetic acid, and butyrylcarnitine levels were considerably distinct between the two groups, and these metabolites were considered to be meaningful prognostic variables for the rs6110695 genotype. Finally, ROC curve analysis demonstrated that the model containing the rs6110695 genotype and the two main metabolites was reliable. The present study revealed that individuals carrying the rs6110695 AG genotype with high plasma indoleacrylic acid and butyrylcarnitine levels might have elevated WBC counts. The rs6110695 genotype and the concentrations of indoleacrylic acid and butyrylcarnitine could contribute to reducing the risk of chronic diseases in the future.

The Association of the Essential Amino Acids Lysine, Methionine, and Threonine with Clinical Outcomes in Patients at Nutritional Risk: Secondary Analysis of a Randomized Clinical Trial

Lysine, methionine, and threonine are essential amino acids with vital functions for muscle and connective tissue health, metabolic balance, and the immune system. During illness, the demand for these amino acids typically increases, which puts patients at risk for deficiencies with harmful clinical consequences. In a secondary analysis of the Effect of Early Nutritional Support on Frailty, Functional Outcomes, and Recovery of Malnourished Medical Inpatients Trial (EFFORT), which compared individualized nutritional support to usual care nutrition in patients at nutritional risk, we investigated the prognostic impact of the lysine, methionine, and threonine metabolism. We had complete clinical and amino acid data in 237 patients, 58 of whom reached the primary endpoint of death at 30 days. In a model adjusted for comorbidities, sex, nutritional risk, and trial intervention, low plasma methionine levels were associated with 30-day mortality (adjusted HR 1.98 [95% CI 1.16 to 3.36], p = 0.01) and with a decline in functional status (adjusted OR 2.06 [95% CI 1.06 to 4.01], p = 0.03). The results for lysine and threonine did not show statistically significant differences regarding clinical outcomes. These findings suggest that low levels of methionine may be critical during hospitalization among patients at nutritional risk. Further studies should investigate the effect of supplementation of methionine in this patient group to improve outcomes.

An Ensemble Spectral Prediction (ESP) model for metabolite annotation

MOTIVATION

A key challenge in metabolomics is annotating measured spectra from a biological sample with chemical identities. Currently, only a small fraction of measurements can be assigned identities. Two complementary computational approaches have emerged to address the annotation problem: mapping candidate molecules to spectra, and mapping query spectra to molecular candidates. In essence, the candidate molecule with the spectrum that best explains the query spectrum is recommended as the target molecule. Despite candidate ranking being fundamental in both approaches, limited prior works incorporated rank learning tasks in determining the target molecule.

RESULTS

We propose a novel machine learning model, Ensemble Spectral Prediction (ESP), for metabolite annotation. ESP takes advantage of prior neural network-based annotation models that utilize multilayer perceptron (MLP) networks and Graph Neural Networks (GNNs). Based on the ranking results of the MLP- and GNN-based models, ESP learns a weighting for the outputs of MLP and GNN spectral predictors to generate a spectral prediction for a query molecule. Importantly, training data is stratified by molecular formula to provide candidate sets during model training. Further, baseline MLP and GNN models are enhanced by considering peak dependencies through label mixing and multi-tasking on spectral topic distributions. When trained on the NIST 2020 dataset and evaluated on the relevant candidate sets from PubChem, ESP improves average rank by 23.7% and 37.2% over the MLP and GNN baselines, respectively, demonstrating performance gain over state-of-the-art neural network approaches. However, MLP approaches remain strong contenders when considering top five ranks. Importantly, we show that annotation performance is dependent on the training dataset, the number of molecules in the candidate set and candidate similarity to the target molecule.

AVAILABILITY AND IMPLEMENTATION

The ESP code, a trained model, and a Jupyter notebook that guide users on using the ESP tool is available at https://github.com/HassounLab/ESP.

Comparative Targeted Metabolomics of Ischemic Stroke: Thrombi and Serum Profiling for the Identification of Stroke-Related Metabolites

Ischemic stroke is one of the leading causes of death and permanent disability in the world. Rapid diagnosis and intervention are crucial for reducing its consequences on individuals and societies. Therefore, identifying reliable biomarkers for early detection, prognostics, and therapy can facilitate the early prediction and prevention of stroke. Metabolomics has been shown as a promising tool for biomarker discovery since many post-ischemic metabolites can be found in the plasma or serum of the patient. In this research, we performed a comparative targeted metabolomic analysis of stroke thrombi, stroke patient serums, and healthy control serums in order to determine the alteration in the patients' metabolomes, which might serve as biomarkers for early prediction or stroke prevention. The most statistically altered metabolites characterized in the patient serums compared with the control serums were glutamate and serotonin, followed by phospholipids and triacylglycerols. In stroke thrombi compared with the patients' serums, the most significantly altered metabolites were classified as lipids, with choline-containing phospholipids and sphingomyelins having the highest discriminatory score. The results of this preliminary study could help in understanding the roles of different metabolic changes that occur during thrombosis and cerebral ischemia and possibly suggest new metabolic biomarkers for ischemic stroke.

Metabolomics in sturgeon research: a mini-review

Sturgeons are ancient fish, with 27 species distributed in the Northern Hemisphere. This review first touches upon the significance of sturgeons in the context of their biological, ecological, and economic importance, highlighting their status as "living fossils" and the challenges they face in genomic research due to their diverse chromosome numbers. This review then discusses how omics technologies (genomics, transcriptomics, proteomics, and metabolomics) have been used in sturgeon research, which so far has only been done on Acipenser species. It focuses on metabolomics as a way to better understand how sturgeons work and how they react to their environment. Specific studies in sturgeon metabolomics are cited, showing how metabolomics has been used to investigate various aspects of sturgeon biology, such as growth, reproduction, stress responses, and nutrition. These studies demonstrate the potential of metabolomics in improving sturgeon aquaculture practices and conservation efforts. Overall, the review suggests that metabolomics, as a relatively new scientific tool, has the potential to enhance our understanding of sturgeon biology and aid in their conservation and sustainable aquaculture, contributing to global food security efforts.

Isoliquiritigenin in combination with visceral adipose tissue and related markers as a predictive tool for nonalcoholic fatty liver disease

Nonalcoholic fatty liver disease (NAFLD) is the most prevalent form of chronic liver disease in the world. New non-invasive diagnostic tools are needed to promptly treat this disease and avoid its complications. This study aimed to find key metabolites and related variables that could be used to predict and diagnose NAFLD. Ninety-eight subjects with NAFLD and 45 controls from the Fatty Liver in Obesity (FLiO) Study (NCT03183193) were analyzed. NAFLD was diagnosed and graded by ultrasound and classified into two groups: 0 (controls) and ≥ 1 (NAFLD). Hepatic status was additionally assessed through magnetic resonance imaging (MRI), elastography, and determination of transaminases. Anthropometry, body composition (DXA), biochemical parameters, and lifestyle factors were evaluated as well. Non-targeted metabolomics of serum was performed with high-performance liquid chromatography coupled to time-of-flight mass spectrometry (HPLC-TOF-MS). Isoliquiritigenin (ISO) had the strongest association with NAFLD out of the determinant metabolites. Individuals with higher concentrations of ISO had healthier metabolic and hepatic status and were less likely to have NAFLD (OR 0.13). Receiver operating characteristic (ROC) curves demonstrated the predictive power of ISO in panel combination with other NAFLD and IR-related variables, such as visceral adipose tissue (VAT) (AUROC 0.972), adiponectin (AUROC 0.917), plasmatic glucose (AUROC 0.817), and CK18-M30 (AUROC 0.810). Individuals with lower levels of ISO have from 71 to 82% more risk of presenting NAFLD compared to individuals with higher levels. Metabolites such as ISO, in combination with visceral adipose tissue, IR, and related markers, constitute a potential non-invasive tool to predict and diagnose NAFLD.

Advances in omics-based biomarker discovery for biliary tract malignancy Diagnosis:A narrative review

Biliary tract neoplasms, which originate from the intrahepatic or extrahepatic biliary epithelium, are relatively rare but diagnostically challenging types of tumours, and their morbidity and mortality have increased in recent years. Due to ineffective early diagnostic methods, once detected, patients are in an advanced stage with a poor prognosis and few treatment options. With the development of omics technologies, the associations between microorganisms, bile acid and salts, noncoding RNAs and biliary tract malignancies have been gradually revealed, providing new methods for the discovery of diagnostic biomarkers. Here, we review the research advances in microbiomics, transcriptomics, metabolomics, and proteomics in the discovery of diagnostic biomarkers for biliary tract malignancies.

Metabolomic Profiles in Jamaican Children With and Without Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with a wide range of behavioral and cognitive impairments. While genetic and environmental factors are known to contribute to its etiology, metabolic perturbations associated with ASD, which can potentially connect genetic and environmental factors, remain poorly understood. Therefore, we conducted a metabolomic case-control study and performed a comprehensive analysis to identify significant alterations in metabolite profiles between children with ASD and typically developing (TD) controls in order to identify specific metabolites that may serve as biomarkers for the disorder. We conducted metabolomic profiling on plasma samples from participants in the second phase of Epidemiological Research on Autism in Jamaica, an age and sex-matched cohort of 200 children with ASD and 200 TD controls (2-8 years old). Using high-throughput liquid chromatography-mass spectrometry techniques, we performed a targeted metabolite analysis, encompassing amino acids, lipids, carbohydrates, and other key metabolic compounds. After quality control and missing data imputation, we performed univariable and multivariable analysis using normalized metabolites while adjusting for covariates, age, sex, socioeconomic status, and child's parish of birth. Our findings revealed unique metabolic patterns in children with ASD for four metabolites compared to TD controls. Notably, three metabolites were fatty acids, including myristoleic acid, eicosatetraenoic acid, and octadecenoic acid. The amino acid sarcosine exhibited a significant association with ASD. These findings highlight the role of metabolites in the etiology of ASD and suggest opportunities for the development of targeted interventions.

Stereoselective Amine-omics Using Heavy Atom Isotope Labeled l- and d-Marfey's Reagents

Biological amines and amino acids play essential roles in many biochemical processes. The chemical complexity of biological samples is challenging, and the selective identification and quantification of amines and amino acid stereoisomers would be very useful for amine-focused "amino-omics" studies. Many amines and amino acids are chiral, and their stereoisomers cannot be resolved on achiral media without chiral derivatization. In prior studies, we demonstrated the use of Marfey's reagent─a chiral derivatization reagent for amines and phenolic OH groups─for the LC-MS/MS resolution and quantification of amines and amino acid stereoisomers. In this study, a heavy atom isotope labeled Marfey's reagent approach for the stereoselective detection and quantification of amines and amino acids was developed. Heavy (13C2) l-Marfey's (Hl-Mar) and heavy (2H3) d-Marfey's (Hd-Mar) were synthesized from 13C2-l-Ala and 2H3-d-Ala, respectively. Both light and heavy Marfey's reagents were used to derivatize standard amine mixtures, which were analyzed by LC-QToF-HRMS. Aligned peak lists were comparatively analyzed by light vs heavy Mar mass differences to identify mono-, di-, and tri-Marfey's adducts and then by the retention time difference between l- and d-Mar derivatives to identify stereoisomers. This approach was then applied to identify achiral and chiral amine and amino acid components in a methicillin-resistant Staphylococcus aureus (MRSA) extract. This approach shows high analytical selectivity and reproducibility.

Exploring machine learning for untargeted metabolomics using molecular fingerprints

BACKGROUND

Metabolomics, the study of substrates and products of cellular metabolism, offers valuable insights into an organism's state under specific conditions and has the potential to revolutionise preventive healthcare and pharmaceutical research. However, analysing large metabolomics datasets remains challenging, with available methods relying on limited and incompletely annotated metabolic pathways.

METHODS

This study, inspired by well-established methods in drug discovery, employs machine learning on metabolite fingerprints to explore the relationship of their structure with responses in experimental conditions beyond known pathways, shedding light on metabolic processes. It evaluates fingerprinting effectiveness in representing metabolites, addressing challenges like class imbalance, data sparsity, high dimensionality, duplicate structural encoding, and interpretable features. Feature importance analysis is then applied to reveal key chemical configurations affecting classification, identifying related metabolite groups.

RESULTS

The approach is tested on two datasets: one on Ataxia Telangiectasia and another on endothelial cells under low oxygen. Machine learning on molecular fingerprints predicts metabolite responses effectively, and feature importance analysis aligns with known metabolic pathways, unveiling new affected metabolite groups for further study.

CONCLUSION

In conclusion, the presented approach leverages the strengths of drug discovery to address critical issues in metabolomics research and aims to bridge the gap between these two disciplines. This work lays the foundation for future research in this direction, possibly exploring alternative structural encodings and machine learning models.

Heavy metal exposure and metabolomics analysis: an emerging frontier in environmental health

Exposure to heavy metals in various populations can lead to extensive damage to different organs, as these metals infiltrate and bioaccumulate in the human body, causing metabolic disruptions in various organs. To comprehensively understand the metal homeostasis, inter-organ "traffic," and extensive metabolic alterations resulting from heavy metal exposure, employing complementary analytical methods is crucial. Metabolomics is pivotal in unraveling the intricacies of disease vulnerability by furnishing thorough understandings of metabolic changes linked to different metabolic diseases. This field offers exciting prospects for enhancing the disease prevention, early detection, and tailoring treatment approaches to individual needs. This article consolidates the existing knowledge on disease-linked metabolic pathways affected by the exposure of diverse heavy metals providing concise overview of the underlying impact mechanisms. The main aim is to investigate the connection between the altered metabolic pathways and long-term complex health conditions induced by heavy metals such as diabetes mellitus, cardiovascular diseases, renal disorders, inflammation, neurodegenerative diseases, reproductive risks, and organ damage. Further exploration of common pathways may unveil the shared targets for treating associated pathological conditions. In this article, the role of metabolomics in disease susceptibility is emphasized that metabolomics is expected to be routinely utilized for the diagnosis and monitoring of diseases and practical value of biomarkers derived from metabolomics, as well as determining their appropriate integration into extensive clinical settings.

Metabolic characterization of neurogenetic disorders involving glutamatergic neurotransmission

The study of inborn errors of neurotransmission has been mostly focused on monoamine disorders, GABAergic and glycinergic defects. The study of the glutamatergic synapse using the same approach than classic neurotransmitter disorders is challenging due to the lack of biomarkers in the CSF. A metabolomic approach can provide both insight into their molecular basis and outline novel therapeutic alternatives. We have performed a semi-targeted metabolomic analysis on CSF samples from 25 patients with neurogenetic disorders with an important expression in the glutamatergic synapse and 5 controls. Samples from patients diagnosed with MCP2, CDKL5-, GRINpathies and STXBP1-related encephalopathies were included. We have performed univariate (UVA) and multivariate statistical analysis (MVA), using Wilcoxon rank-sum test, principal component analysis (PCA), and OPLS-DA. By using the results of both analyses, we have identified the metabolites that were significantly altered and that were important in clustering the respective groups. On these, we performed pathway- and network-based analyses to define which metabolic pathways were possibly altered in each pathology. We have observed alterations in the tryptophan and branched-chain amino acid metabolism pathways, which interestingly converge on LAT1 transporter-dependency to cross the blood-brain barrier (BBB). Analysis of the expression of LAT1 transporter in brain samples from a mouse model of Rett syndrome (MECP2) revealed a decrease in the transporter expression, that was already noticeable at pre-symptomatic stages. The study of the glutamatergic synapse from this perspective advances the understanding of their pathophysiology, shining light on an understudied feature as is their metabolic signature.

A multi-omics study to monitor senescence-associated secretory phenotypes of Alzheimer's disease

OBJECTIVE

Alzheimer's disease (AD) is characterized by the progressive degeneration and damage of neurons in the brain. However, developing an accurate diagnostic assay using blood samples remains a challenge in clinic practice. The aim of this study was to explore senescence-associated secretory phenotypes (SASPs) in peripheral blood using mass spectrometry based multi-omics approach and to establish diagnostic assays for AD.

METHODS

This retrospective study included 88 participants, consisting of 29 AD patients and 59 cognitively normal (CN) individuals. Plasma and serum samples were examined using high-resolution mass spectrometry to identify proteomic and metabolomic profiles. Receiver operating characteristic (ROC) analysis was employed to screen biomarkers with diagnostic potential. K-nearest neighbors (KNN) algorithm was utilized to construct a multi-dimensional model for distinguishing AD from CN.

RESULTS

Proteomics analysis revealed upregulation of five plasma proteins in AD, including RNA helicase aquarius (AQR), zinc finger protein 587B (ZNF587B), C-reactive protein (CRP), fibronectin (FN1), and serum amyloid A-1 protein (SAA1), indicating their potential for AD classification. Interestingly, KNN-based three-dimensional model, comprising AQR, ZNF587B, and CRP, demonstrated its high accuracy in AD recognition, with evaluation possibilities of 0.941, 1.000, and 1.000 for the training, testing, and validation datasets, respectively. Besides, metabolomics analysis suggested elevated levels of serum phenylacetylglutamine (PAGIn) in AD.

INTERPRETATION

The multi-omics outcomes highlighted the significance of the SASPs, specifically AQR, ZNF587B, CRP, and PAGIn, in terms of their potential for diagnosing AD and suggested neuronal aging-associated pathophysiology.

Metabolomics Signature in Prediabetes and Diabetes: Insights From Tandem Mass Spectrometry Analysis

OBJECTIVE

This study investigates the metabolic differences between normal, prediabetic and diabetic patients with good and poor glycaemic control (GGC and PGC).

DESIGN

In this study, 1102 individuals were included, and 50 metabolites were analysed using tandem mass spectrometry. The diabetes diagnosis and treatment standards of the American Diabetes Association (ADA) were used to classify patients.

METHODS

The nearest neighbour method was used to match controls and cases in each group on the basis of age, sex and BMI. Factor analysis was used to reduce the number of variables and find influential underlying factors. Finally, Pearson's correlation coefficient was used to check the correlation between both glucose and HbAc1 as independent factors with binary classes.

RESULTS

Amino acids such as glycine, serine and proline, and acylcarnitines (AcylCs) such as C16 and C18 showed significant differences between the prediabetes and normal groups. Additionally, several metabolites, including C0, C5, C8 and C16, showed significant differences between the diabetes and normal groups. Moreover, the study found that several metabolites significantly differed between the GGC and PGC diabetes groups, such as C2, C6, C10, C16 and C18. The correlation analysis revealed that glucose and HbA1c levels significantly correlated with several metabolites, including glycine, serine and C16, in both the prediabetes and diabetes groups. Additionally, the correlation analysis showed that HbA1c significantly correlated with several metabolites, such as C2, C5 and C18, in the controlled and uncontrolled diabetes groups.

CONCLUSIONS

These findings could help identify new biomarkers or underlying markers for the early detection and management of diabetes.

Metabolomics facilitates differential diagnosis in common inherited retinal degenerations by exploring their profiles of serum metabolites

The diagnosis of inherited retinal degeneration (IRD) is challenging owing to its phenotypic and genotypic complexity. Clinical information is important before a genetic diagnosis is made. Metabolomics studies the entire picture of bioproducts, which are determined using genetic codes and biological reactions. We demonstrated that the common diagnoses of IRD, including retinitis pigmentosa (RP), cone-rod dystrophy (CRD), Stargardt disease (STGD), and Bietti's crystalline dystrophy (BCD), could be differentiated based on their metabolite heatmaps. Hundreds of metabolites were identified in the volcano plot compared with that of the control group in every IRD except BCD, considered as potential diagnosing markers. The phenotypes of CRD and STGD overlapped but could be differentiated by their metabolomic features with the assistance of a machine learning model with 100% accuracy. Moreover, EYS-, USH2A-associated, and other RP, sharing considerable similar characteristics in clinical findings, could also be diagnosed using the machine learning model with 85.7% accuracy. Further study would be needed to validate the results in an external dataset. By incorporating mass spectrometry and machine learning, a metabolomics-based diagnostic workflow for the clinical and molecular diagnoses of IRD was proposed in our study.

Cardiometabolic Differences in People Living with HIV Receiving Integrase Strand Transfer Inhibitors Compared to Non-nucleoside Reverse Transcriptase Inhibitors: Implications for Current ART Strategies

In people living with HIV (PLHIV), integrase strand transfer inhibitors (INSTIs) are part of the first-line combination antiretroviral therapy (cART), while non-nucleoside reverse transcriptase inhibitor (NNRTI)-based regimens are alternatives. Distinct cART regimens may variably influence the risk for non-AIDS comorbidities. We aimed to compare the metabolome and lipidome of INSTI and NNRTI-based regimens. The 2000HIV study includes asymptomatic PLHIV (n = 1646) on long-term cART, separated into a discovery cohort with 730 INSTI and 617 NNRTI users, and a validation cohort encompassing 209 INSTI and 90 NNRTI users. Baseline plasma samples from INSTI and NNRTI users were compared using mass spectrometry-based untargeted metabolomic (n = 500) analysis. Perturbed metabolic pathways were identified using MetaboAnalyst software. Subsequently, nuclear magnetic resonance spectroscopy was used for targeted lipoprotein and lipid (n = 141) analysis. Metabolome homogeneity was observed between the different types of INSTI and NNRTI. In contrast, higher and lower levels of 59 and 45 metabolites, respectively, were found in the INSTI group compared to NNRTI users, of which 77.9% (81/104) had consistent directionality in the validation cohort. Annotated metabolites belonged mainly to 'lipid and lipid-like molecules', 'organic acids and derivatives' and 'organoheterocyclic compounds'. In pathway analysis, perturbed 'vitamin B1 (thiamin) metabolism', 'de novo fatty acid biosynthesis', 'bile acid biosynthesis' and 'pentose phosphate pathway' were detected, among others. Lipoprotein and lipid levels in NNRTIs were heterogeneous and could not be compared as a group. INSTIs compared to individual NNRTI types showed that HDL cholesterol was lower in INSTIs compared to nevirapine but higher in INSTIs compared to doravirine. In addition, LDL size was lower in INSTIs and nevirapine compared to doravirine. NNRTIs show more heterogeneous cardiometabolic effects than INSTIs, which hampers the comparison between these two classes of drugs. Targeted lipoproteomic and lipid NMR spectroscopy showed that INSTI use was associated with a more unfavorable lipid profile compared to nevirapine, which was shifted to a more favorable profile for INSTI when substituting nevirapine for doravirine, with evidently higher fold changes. The cardiovascular disease risk profile seems more favorable in INSTIs compared to NNRTIs in untargeted metabolomic analysis using mass-spectrometry.

Potential of Targeting TDO2 as the Lung Adenocarcinoma Treatment

Tryptophan catabolism plays an important role in the metabolic reconnection in cancer cells to support special demands of tumor initiation and progression. The catabolic product of the tryptophan pathway, kynurenine, has the capability of suppressing the immune reactions of tumor cells. In this study, we conducted internal and external cohort studies to reveal the importance of tryptophan 2,3-dioxygenase (TDO) for lung adenocarcinoma (LUAD). Our study further demonstrated that the TDO2 expression was associated with the proliferation, survival, and invasion of LUAD cells, and targeting TDO2 for LUAD tumors could be a potential therapeutic strategy.

Atopic dermatitis: Pathophysiology, microbiota, and metabolome - A comprehensive review

Atopic dermatitis (AD) is a prevalent inflammatory skin condition that commonly occurs in children. Genetics, environment, and defects in the skin barrier are only a few of the factors that influence how the disease develops. As human microbiota research has advanced, more scientific evidence has shown the critical involvement of the gut and skin bacteria in the pathogenesis of atopic dermatitis. Microbiome dysbiosis, defined by changed diversity and composition, as well as the development of pathobionts, has been identified as a potential cause for recurring episodes of atopic dermatitis. Gut dysbiosis causes "leaky gut syndrome" by disrupting the epithelial lining of the gut, which allows bacteria and other endotoxins to enter the bloodstream and cause inflammation. The same is true for the disruption of cutaneous homeostasis caused by skin dysbiosis, which enables bacteria and other pathogens to reach deeper skin layers or even systemic circulation, resulting in inflammation. Furthermore, it is now recognized that the gut and skin microbiota releases both beneficial and toxic metabolites. Here, this review covers a range of topics related to AD, including its pathophysiology, the microbiota-AD connection, commonly used treatments, and the significance of metabolomics in AD prevention, treatment, and management, recognizing its potential in providing valuable insights into the disease.

Metabolic biomarkers using nuclear magnetic resonance metabolomics assay for the prediction of aging-related disease risk and mortality: a prospective, longitudinal, observational, cohort study based on the UK Biobank

The identification of metabolic biomarkers for aging-related diseases and mortality is of significant interest in the field of longevity. In this study, we investigated the associations between nuclear magnetic resonance (NMR) metabolomics biomarkers and aging-related diseases as well as mortality using the UK Biobank dataset. We analyzed NMR samples from approximately 110,000 participants and used multi-head machine learning classification models to predict the incidence of aging-related diseases. Cox regression models were then applied to assess the relevance of NMR biomarkers to the risk of death due to aging-related diseases. Additionally, we conducted survival analyses to evaluate the potential improvements of NMR in predicting survival and identify the biomarkers most strongly associated with negative health outcomes by dividing participants into health, disease, and death groups for all age groups. Our analysis revealed specific metabolomics profiles that were associated with the incidence of age-related diseases, and the most significant biomarker was intermediate density lipoprotein cholesteryl (IDL-CE). In addition, NMR biomarkers could provide additional contributions to relevant mortality risk prediction when combined with conventional risk factors, by improving the C-index from 0.813 to 0.833, with 17 NMR biomarkers significantly contributing to disease-related death, such as monounsaturated fatty acids (MUFA), linoleic acid (LA), glycoprotein acetyls (GlycA), and omega-3. Moreover, the value of free cholesterol in very large HDL particles (XL-HDL-FC) in the healthy control group demonstrated significantly higher values than the disease and death group across all age groups. This study highlights the potential of NMR metabolomics profiling as a valuable tool for identifying metabolic biomarkers associated with aging-related diseases and mortality risk, which could have practical implications for aging-related disease risk and mortality prediction.

Bayesian functional analysis for untargeted metabolomics data with matching uncertainty and small sample sizes

Untargeted metabolomics based on liquid chromatography-mass spectrometry technology is quickly gaining widespread application, given its ability to depict the global metabolic pattern in biological samples. However, the data are noisy and plagued by the lack of clear identity of data features measured from samples. Multiple potential matchings exist between data features and known metabolites, while the truth can only be one-to-one matches. Some existing methods attempt to reduce the matching uncertainty, but are far from being able to remove the uncertainty for most features. The existence of the uncertainty causes major difficulty in downstream functional analysis. To address these issues, we develop a novel approach for Bayesian Analysis of Untargeted Metabolomics data (BAUM) to integrate previously separate tasks into a single framework, including matching uncertainty inference, metabolite selection and functional analysis. By incorporating the knowledge graph between variables and using relatively simple assumptions, BAUM can analyze datasets with small sample sizes. By allowing different confidence levels of feature-metabolite matching, the method is applicable to datasets in which feature identities are partially known. Simulation studies demonstrate that, compared with other existing methods, BAUM achieves better accuracy in selecting important metabolites that tend to be functionally consistent and assigning confidence scores to feature-metabolite matches. We analyze a COVID-19 metabolomics dataset and a mouse brain metabolomics dataset using BAUM. Even with a very small sample size of 16 mice per group, BAUM is robust and stable. It finds pathways that conform to existing knowledge, as well as novel pathways that are biologically plausible.

Metabolomics of male infertility

This review explores the use of metabolomics in male infertility. Metabolomics, an evolving omics technology that targets the products of cellular metabolism, is valuable for elucidating underlying pathophysiology of many disorders including male infertility. The identification of reliable biomarkers is essential for accurate diagnosis and for developing precision therapeutics for those afflicted by reproductive dysfunction. Unfortunately, despite significant progress to date, the intricate relationships between these metabolic pathways and male infertility remain elusive. It is clear, however, that additional research is required to more fully characterize the role of metabolomics in this disorder and in the potential development of targeted therapies for precision medicine.

Untargeted NMR-based metabolomics analysis of kidney allograft perfusates identifies a signature of delayed graft function

INTRODUCTION

Kidney transplantation (KTx) necessarily conveys an ischemia/reperfusion (I/R) process, which impacts on allograft outcomes. Delayed graft function (DGF) is defined as a non-decrease of serum creatinine by at least 10% daily on 3 consecutive days during the first 7 days post-KTx. DGF significantly conditions both short- and long-term graft outcomes. Still there is a lack of DGF predictive biomarkers.

OBJECTIVES

This study aimed to explore the potential of kidney graft perfusate metabolomics to predict DGF occurrence.

METHODS

49 human perfusates from grafts categorized upon donor type [donation after brain death (DBD)/donation after circulatory death (DCD)] and DGF occurrence and 19 perfusates from a murine model classified upon death type (DBD/DCD) were collected and analyzed by NMR-based metabolomics.

RESULTS

The multivariate analysis of the murine data highlighted significant differences between perfusate metabolomes of DBD versus DCD. These differences were similarly observed in the human perfusates. After correcting for the type of donor, multivariate analysis of human data demonstrated a metabolomics signature that could be correlated with DGF occurrence.

CONCLUSIONS

The metabolome of kidney grafts is influenced by the donor's type in both human and pre-clinical studies and could be correlated with DGF in the human DBD cohort. Thus, metabolomic analysis of perfusate applied prior to KTx may represent a new predictive tool for clinicians in a more personalized management of DGF. Moreover, our data paves the way to better understand the impact of donor's types on the biochemical events occurring between death and the hypothermic storage.

Prognostic implications of the arginine metabolism in patients at nutritional risk: A secondary analysis of the randomized EFFORT trial

BACKGROUND

Arginine, a conditionally essential amino acid, is key component in metabolic pathways including immune regulation and protein synthesis. Depletion of arginine contributes to worse outcomes in severely ill and surgical patient populations. We assessed prognostic implications of arginine levels and its metabolites and ratios in polymorbid medical inpatients at nutritional risk regarding clinical outcomes and treatment response.

METHODS

Within this secondary analysis of the randomized controlled Effect of early nutritional support on Frailty, Functional Outcomes, and Recovery of malnourished medical inpatients Trial (EFFORT), we investigated the association of arginine, its metabolites and ratios (i.e., ADMA and SDMA, ratios of arginine/ADMA, arginine/ornithine, and global arginine bioavailability ratio) measured on hospital admission with short-term and long-term mortality by means of regression analysis.

RESULTS

Among the 231 patients with available measurements, low arginine levels ≤90.05 μmol/l (n = 86; 37 %) were associated with higher all-cause mortality at 30 days (primary endpoint, adjusted HR 3.27, 95 % CI 1.86 to 5.75, p < 0.001) and at 5 years (adjusted HR 1.50, 95 % CI 1.07 to 2.12, p = 0.020). Arginine metabolites and ratios were also associated with adverse outcome, but had lower prognostic value. There was, however, no evidence that treatment response was influenced by admission arginine levels.

CONCLUSION

This secondary analysis focusing on medical inpatients at nutritional risk confirms a strong association of low plasma arginine levels and worse clinical courses. The potential effects of arginine-enriched nutritional supplements should be investigated in this population of patients.

CLINICAL TRIAL REGISTRATION

clinicaltrials.gov as NCT02517476 (registered 7 August 2015).

Diagnostic and Prognostic Performance of Metabolic Signatures in Pancreatic Ductal Adenocarcinoma: The Clinical Application of Quantitative NextGen Mass Spectrometry

With 64,050 new diagnoses and 50,550 deaths in the US in 2023, pancreatic ductal adenocarcinoma (PDAC) is among the most lethal of all human malignancies. Early detection and improved prognostication remain critical unmet needs. We applied next-generation metabolomics, using quantitative tandem mass spectrometry on plasma, to develop biochemical signatures that identify PDAC. We first compared plasma from 10 PDAC patients to 169 samples from healthy controls. Using metabolomic algorithms and machine learning, we identified ratios that incorporate amino acids, biogenic amines, lysophosphatidylcholines, phosphatidylcholines and acylcarnitines that distinguished PDAC from normal controls. A confirmatory analysis then applied the algorithms to 30 PDACs compared with 60 age- and sex-matched controls. Metabolic signatures were then analyzed to compare survival, measured in months, from date of diagnosis to date of death that identified metabolite ratios that stratified PDACs into distinct survival groups. The results suggest that metabolic signatures could provide PDAC diagnoses earlier than tumor markers or radiographic measures and offer insights into disease severity that could allow more judicious use of therapy by stratifying patients into metabolic-risk subgroups.

Modeling blood metabolite homeostatic levels reduces sample heterogeneity across cohorts

Blood metabolite levels are affected by numerous factors, including preanalytical factors such as collection methods and geographical sites. These perturbations have caused deleterious consequences for many metabolomics studies and represent a major challenge in the metabolomics field. It is important to understand these factors and develop models to reduce their perturbations. However, to date, the lack of suitable mathematical models for blood metabolite levels under homeostasis has hindered progress. In this study, we develop quantitative models of blood metabolite levels in healthy adults based on multisite sample cohorts that mimic the current challenge. Five cohorts of samples obtained across four geographically distinct sites were investigated, focusing on approximately 50 metabolites that were quantified using 1H NMR spectroscopy. More than one-third of the variation in these metabolite profiles is due to cross-cohort variation. A dramatic reduction in the variation of metabolite levels (90%), especially their site-to-site variation (95%), was achieved by modeling each metabolite using demographic and clinical factors and especially other metabolites, as observed in the top principal components. The results also reveal that several metabolites contribute disproportionately to such variation, which could be explained by their association with biological pathways including biosynthesis and degradation. The study demonstrates an intriguing network effect of metabolites that can be utilized to better define homeostatic metabolite levels, which may have implications for improved health monitoring. As an example of the potential utility of the approach, we show that modeling gender-related metabolic differences retains the interesting variance while reducing unwanted (site-related) variance.

Sodium fluoride preserves blood metabolite integrity for biomarker discovery in large-scale, multi-site metabolomics investigations

Background: Metabolite profiling of blood by nuclear magnetic resonance (NMR) is invaluable to clinical biomarker discovery. To ensure robustness, biomarkers require validation in large cohorts and across multiple centres. However, collection procedures are known to impact on the stability of biofluids that may, in turn, degrade biomarker signals. We trialled three blood collection tubes with the aim of solving technical challenges due to preanalytical variation in blood metabolite levels that are common in cohort studies. Methods: We first investigated global NMR-based metabolite variability between biobanks, including the large-scale UK Biobank and TwinsUK biobank of the general UK population, and more targeted biobanks derived from multicentre clinical trials relating to inflammatory bowel disease. We then compared the blood metabolome of 12 healthy adult volunteers when collected into either sodium fluoride/potassium oxalate, lithium heparin, or serum blood tubes using different pre-processing parameters. Results: Preanalytical variation in the method of blood collection strongly influences metabolite composition within and between biobanks. This variability can largely be attributed to glucose and lactate. In the healthy control cohort, the fluoride oxalate collection tube prevented fluctuation in glucose and lactate levels for 24 hours at either 4 °C or room temperature (20 °C). Conclusions: Blood collection into a fluoride oxalate collection tube appears to preserve the blood metabolome with delayed processing up to 24 hours at 4 °C. This method may be considered as an alternative when rapid processing is not feasible.

Metabolomic Profiling of Blood Plasma in Females with Hyperplasia and Endometrial Cancer

Uterine cancer is the most prevalent gynecologic malignancy in women worldwide. Endometrial cancer (EC) has an 81% five-year survival rate, depending on disease stage and time of diagnosis. While endometrial cancer is largely treatable when detected early, no established screening techniques are available in clinical practice. As a result, one of the most significant issues in the medical field is the development of novel ways for early cancer identification, which could boost treatment success rates. Liquid chromatography-high-resolution mass spectrometry (LC-HRMS)-based metabolomics was employed to explore the metabolomic markers and pathways unique to this cancer type and link them to the benign endometrial hyperplasia that may progress to cancer in 5% to 25% of patients. The study involved 59 postmenopausal participants, 20 with EC type 1, 20 with benign hyperplasia, and 19 healthy participants. Metabolite distribution changes were analyzed, and 338 of these features were dysregulated and significant. The first two main components, PC1 and PC2, were responsible for 11.5% and 12.2% of the total metabolites, respectively. Compared with the control group (CO), EC samples had 203 differentially expressed metabolites (180 upregulated and 23 downregulated); in hyperplasia (HP), 157 metabolites were dysregulated (127 upregulated and 30 downregulated) compared to the CO group while 21 metabolites exhibited differential regulation (16 upregulated and 5 downregulated) in EC plasma samples compared to the HP group. Hyperplasia samples exhibited similar metabolic changes to those reported in cancer, except for alterations in triglyceride levels, 7a,12 b-dihydroxy-5b-Cholan-24-oic acid, and Hept-2-enedioyl carnitine levels. The metabolites N-heptanoyl glycine and -(Methylthio)-2,3-isopentyl phosphate and formimino glutamic acid can be specific markers for hyperplasia conditions and dimethyl phosphatidyl ethanolamine and 8-isoprostaglandin E2 can be specific markers for EC conditions. Metabolic activities rely on mitochondrial oxidative phosphorylation for energy generation. The changes in metabolites identified in our study indicate that endometrial cancer cells adopt alternative strategies to increase energy production to meet the energy demand, thereby supporting proliferation.

Mapping the metabolic responses to oxaliplatin-based chemotherapy with in vivo spatiotemporal metabolomics

Adjuvant chemotherapy improves the survival outlook for patients undergoing operations for lung metastases caused by colorectal cancer (CRC). However, a multidisciplinary approach that evaluates several factors related to patient and tumor characteristics is necessary for managing chemotherapy treatment in metastatic CRC patients with lung disease, as such factors dictate the timing and drug regimen, which may affect treatment response and prognosis. In this study, we explore the potential of spatial metabolomics for evaluating metabolic phenotypes and therapy outcomes during the local delivery of the anticancer drug, oxaliplatin, to the lung. 12 male Yorkshire pigs underwent a 3 h left lung in vivo lung perfusion (IVLP) with various doses of oxaliplatin (7.5, 10, 20, 40, and 80 mg/L), which were administered to the perfusion circuit reservoir as a bolus. Biocompatible solid-phase microextraction (SPME) microprobes were combined with global metabolite profiling to obtain spatiotemporal information about the activity of the drug, determine toxic doses that exceed therapeutic efficacy, and conduct a mechanistic exploration of associated lung injury. Mild and subclinical lung injury was observed at 40 mg/L of oxaliplatin, and significant compromise of the hemodynamic lung function was found at 80 mg/L. This result was associated with massive alterations in metabolic patterns of lung tissue and perfusate, resulting in a total of 139 discriminant compounds. Uncontrolled inflammatory response, abnormalities in energy metabolism, and mitochondrial dysfunction next to accelerated kynurenine and aldosterone production were recognized as distinct features of dysregulated metabolipidome. Spatial pharmacometabolomics may be a promising tool for identifying pathological responses to chemotherapy.

Context-dependent role of SIRT3 in cancer

Sirtuin 3 (SIRT3), an NAD+-dependent deacetylase, plays a key role in the modulation of metabolic reprogramming and regulation of cell death, as well as in shaping tumor phenotypes. Owing to its critical role in determining tumor-type specificity or the direction of tumor evolution, the development of small-molecule modulators of SIRT3, including inhibitors and activators, is of significant interest. In this review, we discuss recent studies on the oncogenic or tumor-suppressive functions of SIRT3, evaluate advances in SIRT3-targeted drug discovery, and present potential avenues for the design of small-molecule modulators of SIRT3 for cancer therapy.

The Role of Protein Degradation in Estimation Postmortem Interval and Confirmation of Cause of Death in Forensic Pathology: A Literature Review

It is well known that proteins are important bio-macromolecules in human organisms, and numerous proteins are widely used in the clinical practice, whereas their application in forensic science is currently limited. This limitation is mainly attributed to the postmortem degradation of targeted proteins, which can significantly impact final conclusions. In the last decade, numerous methods have been established to detect the protein from a forensic perspective, and some of the postmortem proteins have been applied in forensic practice. To better understand the emerging issues and challenges in postmortem proteins, we have reviewed the current application of protein technologies at postmortem in forensic practice. Meanwhile, we discuss the application of proteins in identifying the cause of death, and postmortem interval (PMI). Finally, we highlight the interpretability and limitations of postmortem protein challenges. We believe that utilizing the multi-omics method can enhance the comprehensiveness of applying proteins in forensic practice.

Causal association between 637 human metabolites and ovarian cancer: a mendelian randomization study

BACKGROUND

Current evidence suggests a significant association between metabolites and ovarian cancer (OC); however, the causal relationship between the two remains unclear. This study employs Mendelian randomization (MR) to investigate the causal effects between different metabolites and OC.

METHODS

In this study, a total of 637 metabolites were selected as the exposure variables from the Genome-wide Association Study (GWAS) database ( http://gwas.mrcieu.ac.uk/datasets/ ). The OC related GWAS dataset (ieu-b-4963) was chosen as the outcome variable. R software and the TwoSampleMR package were utilized for the analysis in this study. MR analysis employed the inverse variance-weighted method (IVW), MR-Egger and weighted median (WM) for regression fitting, taking into consideration potential biases caused by linkage disequilibrium and weak instrument variables. Metabolites that did not pass the tests for heterogeneity and horizontal pleiotropy were considered to have no significant causal effect on the outcome. Steiger's upstream test was used to determine the causal direction between the exposure and outcome variables.

RESULTS

The results from IVW analysis revealed that a total of 31 human metabolites showed a significant causal effect on OC (P < 0.05). Among them, 9 metabolites exhibited consistent and stable causal effects, which were confirmed by Steiger's upstream test (P < 0.05). Among these 9 metabolites, Androsterone sulfate, Propionylcarnitine, 5alpha-androstan-3beta,17beta-diol disulfate, Total lipids in medium VLDL and Concentration of medium VLDL particles demonstrated a significant positive causal effect on OC, indicating that these metabolites promote the occurrence of OC. On the other hand, X-12,093, Octanoylcarnitine, N2,N2-dimethylguanosine, and Cis-4-decenoyl carnitine showed a significant negative causal association with OC, suggesting that these metabolites can inhibit the occurrence of OC.

CONCLUSIONS

The study revealed the complex effect of metabolites on OC through Mendelian randomization. As promising biomarkers, these metabolites are worthy of further clinical validation.

Metabolomic Changes in Rat Serum after Chronic Exposure to Glyphosate-Based Herbicide

Glyphosate-based herbicides (GBHs) have gained extensive popularity in recent decades. For many years, glyphosate has been regarded as harmless or minimally toxic to mammals due to the absence of its primary target, the shikimic acid pathway in humans. Nonetheless, mounting evidence suggests that glyphosate may cause adverse health effects in humans via other mechanisms. In this study, we described the metabolomic changes in the serum of experimental rats exposed to chronic GBH using the highly sensitive LC-MS/MS technique. We investigated the possible relationship between chronic exposure to GBH and neurological disorders. Our findings suggest that chronic exposure to GBH can alter spatial learning memory and the expression of some important metabolites that are linked to neurophysiological disorders in young rats, with the female rats showing higher susceptibility compared to the males. This indicates that female rats are more likely to show early symptoms of the disorder on exposure to chronic GBH compared to male rats. We observed that four important metabolites (paraxanthine, epinephrine, L-(+)-arginine, and D-arginine) showed significant changes and involvement in neurological changes as suggested by ingenuity pathway analysis. In conclusion, our results indicate that chronic exposure to GBH can increase the risk of developing neurological disorders.

Association of Glutamine and Glutamate Metabolism with Mortality among Patients at Nutritional Risk-A Secondary Analysis of the Randomized Clinical Trial EFFORT

Glutamine and its metabolite glutamate serve as the main energy substrates for immune cells, and their plasma levels drop during severe illness. Therefore, glutamine supplementation in the critical care setting has been advocated. However, little is known about glutamine metabolism in severely but not critically ill medical patients. We investigated the prognostic impact of glutamine metabolism in a secondary analysis of the Effect of Early Nutritional Support on Frailty, Functional Outcomes, and Recovery of Malnourished Medical Inpatients Trial (EFFORT), a randomized controlled trial comparing individualized nutritional support to usual care in patients at nutritional risk. Among 234 patients with available measurements, low plasma levels of glutamate were independently associated with 30-day mortality (adjusted HR 2.35 [95% CI 1.18-4.67, p = 0.015]). The impact on mortality remained consistent long-term for up to 5 years. No significant association was found for circulating glutamine levels and short- or long-term mortality. There was no association of glutamate nor glutamine with malnutrition parameters or with the effectiveness of nutritional support. This secondary analysis found glutamate to be independently prognostic among medical inpatients at nutritional risk but poorly associated with the effectiveness of nutritional support. In contrast to ICU studies, we found no association between glutamine and clinical outcome.

Precision Medicine in Depression: The Role of Proteomics and Metabolomics in Personalized Treatment Approaches

Depression, or major depressive disorder (MDD), is a widespread mental health condition marked by enduring feelings of sorrow and loss of interest. Treatment of depression frequently combines psychotherapy, medication, and lifestyle modifications. However, the occurrence of treatment resistance in certain individuals makes it difficult for physicians to effectively manage this disorder, calling for the implementation of alternative therapeutic strategies. Recently, precision medicine has gained increased attention in the field of mental health, paving the way for more personalized and effective therapeutic interventions in depression. Also known as personalized medicine, this approach relies on genetic composition, molecular profiles, and environmental variables to customize therapies to individual patients. In particular, precision medicine has offered novel viewpoints on depression through two specific domains: proteomics and metabolomics. On one hand, proteomics is the thorough study of proteins in a biological system, while metabolomics focuses on analyzing the complete set of metabolites in a living being. In the past few years, progress in research has led to the identification of numerous depression-related biomarkers using proteomics and metabolomics techniques, allowing for early identification, precise diagnosis, and improved clinical outcome. However, despite significant progress in these techniques, further efforts are required for advancing precision medicine in the diagnosis and treatment of depression. The overarching goal of this chapter is to provide the current state of knowledge regarding the use of proteomics and metabolomics in identifying biomarkers related to depression. It also highlights the potential of proteomics and metabolomics in elucidating the intricate processes underlying depression, opening the door for tailored therapies that could eventually enhance clinical outcome in depressed patients. This chapter finally discusses the main challenges in the use of proteomics and metabolomics and discusses potential future research directions.

Designed Nanomaterials-Assisted Proteomics and Metabolomics Analysis for In Vitro Diagnosis

In vitro diagnosis (IVD) is pivotal in modern medicine, enabling early disease detection and treatment optimization. Omics technologies, particularly proteomics and metabolomics, offer profound insights into IVD. Despite its significance, omics analyses for IVD face challenges, including low analyte concentrations and the complexity of biological environments. In addition, the direct omics analysis by mass spectrometry (MS) is often hampered by issues like large sample volume requirements and poor ionization efficiency. Through manipulating their size, surface charge, and functionalization, as well as the nanoparticle-fluid incubation conditions, nanomaterials have emerged as a promising solution to extract biomolecules and enhance the desorption/ionization efficiency in MS detection. This review delves into the last five years of nanomaterial applications in omics, focusing on their role in the enrichment, separation, and ionization analysis of proteins and metabolites for IVD. It aims to provide a comprehensive update on nanomaterial design and application in omics, highlighting their potential to revolutionize IVD.